lda-wf/docs/thesis/system-design-implementation.md

---
title: "Design and Implementation of lda.chat: An AI Agent for Automating and Creating Workspace Workflows"
subtitle: ""
date: "July 1, 2026"
lang: "en-US"
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keywords:
  - workflow
  - agents
  - source providers
  - JSON-RPC
  - MCP
  - Python sources
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# List of Abbreviations {.unnumbered}

<!-- The heading identifies this captionless table; it has no table number. -->
| Abbreviation | Meaning |
| --- | --- |
| API | Application Programming Interface |
| AI | Artificial Intelligence |
| CLI | Command-Line Interface |
| JSON-RPC | JavaScript Object Notation Remote Procedure Call |
| LLM | Large Language Model |
| MCP | Model Context Protocol |
| RPC | Remote Procedure Call |
| USTH | University of Science and Technology of Hanoi |

# Abstract {.unnumbered}

Preparing reports, transforming documents, and collecting workspace information
often involve procedures that must be repeated with new inputs. An AI assistant
can help perform such work. Repeating it requires preserving the operations
and their data connections as an executable procedure. This thesis develops
the programmable workflow subsystem of `lda.chat` for defining, checking,
running, and inspecting reusable workspace procedures.

A workflow describes the operations to perform, the data they exchange, and
the decisions that select the next step. An **artifact** is an immutable saved
version of that workflow. A **deployment** connects a saved version to the
concrete services it will use. A **run** records one execution, including its
inputs, status, result, and trace. These distinctions separate revising a
procedure, configuring where it operates, and examining what happened.

The design addresses both authoring experience and execution behavior. Authors
need to discover operations, connect their inputs and outputs, understand
validation errors, and inspect results. The runtime needs corresponding rules
for data contracts, control flow, state updates, and interruption. The thesis
compares these concerns with other workflow systems and explains the prototype's
choice to separate a node's data output from its routing outcome.

The implementation includes a workflow runtime, Python authoring objects, a
workflow service, and adapters for trusted Python functions and external tools.
The evaluation focuses on workflow construction and execution through the
Python client. It uses a deterministic case study and identifies focused
tests of validation, execution, and persistence. Agent-driven authoring and
authoring usability remain to be evaluated as the interaction design develops.

# Introduction

Preparing a weekly project report involves collecting notes, extracting actions
and risks, checking the result, and producing a document that other people can
use. A person can do this manually, write a script, or ask an AI assistant for
help. When the procedure becomes recurring, the workspace also needs a way to
preserve it, supply different inputs, and inspect unsuccessful attempts.

`lda.chat` turns such a procedure into a reusable workflow: a saved description
of operations, their data connections, and their execution order. An author
can revise this description, connect it to available services, and run it
with new inputs. Each execution has a separate record of its progress and
results. The procedure remains available after the conversation or programming
session that created it has ended.

For the report task, the initial procedure is short:

Read the notes, extract a structured report, and render it as Markdown.

The operator supplies new notes each week using the saved steps.
If the requirements change, the author revises the procedure and saves another
version. If execution fails, the operator needs to identify the affected step
and its inputs.

## From a useful procedure to a usable system

The intended beneficiary is a workspace operator who needs repeatable work.
Its author may be that person, a developer, or an external agent acting on their
behalf. Once saved, the procedure can run with or without an LLM operation,
according to the steps its author included.

A workflow system must help its author answer practical questions: which
operations are available, what information they require, and how one operation's
result becomes another's input. During operation, it must distinguish an invalid
definition, a missing service, a failed execution, and a request for more input.
The **runtime** is the part of the system that executes the workflow: it invokes
steps, maintains working data, and determines where execution continues.
These are interaction-design concerns as well as runtime concerns. An interface
that draws a branch without explaining whether one path or both will execute
leaves a consequential rule implicit.

The current authoring interface is Python-based. It presents workflow and run
objects for discovering operations, revising a graph, and inspecting execution.
An external agent can use that interface through a Python execution environment.
The interface and runtime are under development, with the implemented behavior
and its supporting evidence examined in this thesis.

## Engineering question and contribution

This thesis examines how authors can define a reusable procedure while the
system manages its individual executions. Authors specify data connections
and decisions about what happens next. The system must preserve those rules
when steps repeat, when one workflow calls another, and when execution pauses
for input.
Separating these responsibilities allows a saved procedure to be executed
without retaining the conversation or programming session that produced it.

The prototype implements three design choices as one lifecycle:

1. A graph with declared data structures represents operations, working data,
   and routing decisions so they can be validated and inspected.
2. Separate saved versions, service configuration, and execution records make
   editing, configuration, and operation distinct activities.
3. Programmable authoring, validation diagnostics, and run inspection expose
   those distinctions to human and agent clients.

The work integrates established workflow techniques into a service for reusable
procedures. Its design is examined from both sides:
what an author must understand and do, and what the runtime guarantees when
the procedure executes. The comparison with related systems examines concrete
authoring and execution mechanisms.

## Scope of the implementation

The implemented runtime supports conditional routes, iteration, calls to saved
workflows, and explicit interruption/resume. A run-wide limit bounds the number
of node executions it admits. General parallel fork/gather remains proposed.
The evaluation covers controlled execution and lifecycle tests, with usability
and production operation identified as further evaluation work. A companion
assistant backend can execute code in persistent Python shells. Its workflow
authoring integration and developing chat interface are discussed in Future
Work, outside the evaluated workflow case study.

## Report outline

Chapter 2 derives interaction and execution requirements from the workspace
task. Chapter 3 compares ways to author and operate that task in related
systems. Chapter 4 explains the prototype's concepts through an example.
Chapters 5 and 6 describe architecture and implementation; Chapter 7 presents
the reproducible case study. Chapter 8 evaluates the available evidence.
The remaining chapters discuss limitations, future work, and conclusions.

# Problem Statement And Requirements

A procedure that works once is not necessarily ready for repeated operation.
For the report example, a renamed field may invalidate extraction, a different
workspace may use another notes service, or the input may omit information
needed by the final document. The author needs feedback that distinguishes these
situations, while the runtime needs rules for handling them.

LLM tool-use approaches illustrate dynamic selection of actions
[@react-2022; @toolformer-2023]. Such approaches can incorporate schemas and
persistence. This thesis examines which responsibilities belong in the saved
procedure and which remain with its authoring environment.

The requirements are organized into two groups. R1–R5 describe the authoring
and operation experience the system should support. X1–X5 define the execution
rules needed to support that experience. Their identifiers link the design
to the evidence assessment in [@tbl:requirements-evidence].

## Authoring and operation requirements

The authoring requirements cover discovery, data movement, revision, version
selection, and execution inspection:

1. **R1 — Discover before connecting.** Show available operations and the inputs,
   outputs, and services they require. The author should not need to inspect
   server implementation code to learn how an operation can be used.
2. **R2 — Make data movement understandable.** Explain how a document path becomes
   text, how text becomes structured fields, and which fields reach the result.
   Distinguish a data connection from a decision about what executes next.
3. **R3 — Support revision and useful feedback.** An author should be able to revise
   an unfinished procedure and locate errors in the relevant step or data
   mapping. Diagnostics should locate the rejected part and explain the
   violated constraint.
4. **R4 — Separate editing from running.** Make clear which version an execution
   uses. Editing the next version should not silently change an earlier one.
5. **R5 — Explain execution state.** Distinguish completed, failed, and interrupted
   runs, expose relevant intermediate evidence, and identify the input required
   to resume an interruption.

Agent-assisted authoring places particular demands on this interaction. An
agent must discover the available operations, interpret a rejected definition,
and determine which changes are permitted without relying on implementation
files. This motivates three complementary forms of support: descriptions of
operations and their contracts, diagnostics that locate errors, and instructions
for the authoring and execution lifecycle. Structured responses, stable
identities, and inspection results with explicit size limits make that information
available to
programmatic clients. Human authors and other software clients use the same
information to construct and operate workflows.

A saved definition follows the same routing and data rules
whether it was written by a developer or assembled by an agent. The evaluation
therefore examines ease of use and compliance with execution rules as separate
questions.

## Execution requirements behind the interaction

Those interactions require corresponding runtime contracts (X1–X5):

1. **X1 — Preserve definitions and executions independently.** Save an identifiable
   procedure version and keep separate records for its invocations.
2. **X2 — Validate known constraints before work starts.** Check graph structure,
   declared data contracts, mappings, and required service connections.
3. **X3 — Specify routing and state changes.** Define what selects the successor,
   where outputs are written, and how repeated writes affect the workflow's
   working data, referred to here as workflow state.
4. **X4 — Separate environment choices from procedure logic.** Resolve logical
   service requirements to concrete configured services and report mismatches.
5. **X5 — Bound and inspect execution.** Limit admitted node executions, retain
   status and trace information, and support resume at defined interruption
   boundaries.

Together, these requirements motivate separate records for saved procedures,
environment selection, and individual executions.

## Costs and boundaries

Declaring data structures, mappings, and saved versions imposes authoring work.
A script
can be preferable for a short-lived task, especially when its author already
understands the libraries involved. The platform targets cases where reuse and
inspection justify that setup. Determining when those benefits outweigh the
setup cost requires further evaluation.

A workflow interface must also avoid promising more than its runtime supports.
Successful validation does not guarantee a remote service will succeed, and a
saved interruption does not make arbitrary external effects reversible.

# Positioning And Related Systems

The report task provides a common example for comparing authoring and execution.
The comparison asks how an author connects operations and data, how the runtime
handles decisions and repeated work, and how the author tests and inspects a
run. These questions apply to graphical editors and Python interfaces alike.

The accounts below use documented mechanisms consulted in September 2026.
They describe selected interaction and execution mechanisms. Comparative
usability would require participants performing matched tasks.

## Starting with code or direct tool calls

A developer can express the report procedure as function calls and use the
language's conditionals and loops. An agent can instead select successive tool
calls from the information available at each turn. Both approaches can be
combined with schemas, tests, logs, and persistence.

Scripts, including generated scripts, are a substantive alternative rather
than merely a preliminary form of workflow automation. Code puts the procedure
close to its implementation and makes ordinary debugging
tools available. It also leaves decisions about configuration, saved versions,
and run records to the program or its surrounding infrastructure. A workflow
platform makes some of those decisions part of its public contract, at the cost
of introducing another model for the author to learn.

The prototype still uses Python for authoring. The distinction is whether that
code performs the entire procedure directly or constructs a saved workflow for
the service to execute. Neither representation is automatically better for a
one-off task.

For this comparison, state means working data retained during execution;
a reducer defines how a new write changes a state field. The prototype's
outcome is a routing label returned separately from a step's data output.
Chapter 4 develops these concepts through the report example.

The comparison asks the same questions of each system: what a step produces,
how subsequent work is selected, how data combines, and how an author sees
those rules. Suspension is considered separately from ordinary branching.

## n8n: connecting and inspecting data

n8n passes arrays of data items between connected nodes. Items contain JSON
data and may also contain binary data. Authors map fields from incoming items
into node parameters; dragging a field into a parameter creates an expression.
Thus, a connection participates in data flow as well as execution order
[@n8n-data-2026].

For an author combining report records, n8n's Merge node exposes a concrete
choice: append the incoming collections, match records by fields or position,
or produce combinations. The node's configuration and worked examples make
these operations distinguishable. Append waits for connected inputs and emits
their items in input order [@n8n-merge-2026].

[@fig:n8n-merge-append] shows the Append setting and the resulting two
output records. The fields remain in separate records; selecting Append
does not combine them into one record.

<!-- markdownlint-disable-next-line MD013 -->
![n8n Merge configured to append two inputs, with its output preview. Author's screenshot.](assets/comparison/n8n-merge-append.png){#fig:n8n-merge-append width=100%}

Selecting a merge mode changes which records appear in the output.
For the report task, joining actions by owner is different from appending two
action lists.

In this thesis, a **data binding** is a declared mapping from a value's source
location to its destination. The prototype represents data movement through
these bindings. Its reducers
define how writes update state fields. Joining records and waiting for branch
completion are separate operations.
Branching does not necessarily imply simultaneous execution. For workflows
created from n8n 1.0, the documented default completes one branch before
starting another, with ordering affected by canvas position and workflow
settings [@n8n-order-2026]. The author therefore needs both item-level data
inspection and an account of branch execution, not only a connected diagram.

## Zapier: configuring a decision

Zapier exposes the output fields of earlier steps for mapping into later
steps. Test records supply the values shown during configuration; live runs
use their own data. This makes the mapping an explicit reference to a previous
step, rather than an update to an author-declared shared state field
[@zapier-mapping-2026].

An author using Zapier Paths selects fields, conditions, and values, then tests
the rules against sample data. Applied to the report task, those rules could
distinguish complete records from records needing attention. Multiple paths
can qualify, so exclusivity must follow from the rules rather than the
branching appearance alone [@zapier-paths-2026].

[@fig:zapier-path-condition] shows a rule testing whether the selected
`Hello` field contains `world`. The test record matches, and the interface
reports that this path would have continued. This preview lets the author
check a rule against a concrete value before running the workflow.

<!-- markdownlint-disable-next-line MD013 -->
![A Zapier Paths condition and its matching test record. Author's screenshot.](assets/comparison/zapier-path-condition.png){#fig:zapier-path-condition width=75%}

The same documentation describes sequential execution of qualifying paths.
The rules can permit several branches to execute in one run. An exclusive
choice requires conditions that cannot qualify together.
Paths does not provide a shared action after all branches; common steps can be
duplicated or placed in a Sub-Zap. These constraints affect how the author
organizes a common report-rendering step [@zapier-paths-2026].

The interface therefore needs to communicate both the condition being tested
and the consequence of a match. In lda.chat, an ordinary outcome selects
one successor, whereas several Zapier Paths may qualify.

```{=latex}
\begin{minipage}{\linewidth}
```

## LangGraph: describing decisions in Python

LangGraph's Graph API lets an author define state, add node functions and
routing, compile the graph, and invoke it. For the report task, a developer can
represent extracted fields in state and write a routing function that selects
what happens next [@langgraph-graph-api-2026].

In this small example, `clean` returns an update to the `text` field.
The separate `choose` function reads that updated state and selects either
the rendering step or the end of the graph:

<!-- langgraph-comparison -->
```python
from typing import TypedDict
from langgraph.graph import START, END, StateGraph

class ReportState(TypedDict):
    text: str
    report: str

def clean(state: ReportState):
    return {"text": state["text"].strip()}

def choose(state: ReportState):
    return "render" if state["text"] else END

def render(state: ReportState):
    return {"report": "# Report\n\n" + state["text"]}

builder = StateGraph(ReportState)
builder.add_node("clean", clean)
builder.add_node("render", render)
builder.add_edge(START, "clean")
builder.add_conditional_edges("clean", choose, ["render", END])
builder.add_edge("render", END)
graph = builder.compile()
```

```{=latex}
\end{minipage}
```

Calling `graph.invoke({"text": " Notes ", "report": ""})` cleans the text,
then produces a report. Whitespace-only input ends after `clean`. The state
update and the routing decision are expressed in separate functions here;
LangGraph also offers `Command` to return both from a node
[@langgraph-graph-api-2026].

This authoring style exposes more behavior as code. Nodes produce state
updates, reducers determine how updates combine, and conditional routes select
subsequent execution. The documented model supports graph loops and super-step
execution (parallel execution with a synchronization barrier); its checkpoint
facilities are described separately
[@langgraph-graph-api-2026; @langgraph-persistence-2026].

The prototype shares the use of typed state and reducers, but represents
ordinary routing as a mapping from a declared outcome to a successor.
This keeps executable predicates out of ordinary edges. The cost is that some
decisions need a dedicated condition step or an additional declared outcome.
The relevant comparison is where authors express and inspect decisions, not
whether Python or a canvas is inherently the better interface.

## Saved definitions, configuration, and execution records

Lifecycle separation is not unique to the prototype. n8n distinguishes saved
edits from the published version used for production execution, and separates
workflow history from execution history. Its execution view supports status
filtering and inspection of previous attempts
[@n8n-publish-2026; @n8n-executions-2026].

Zapier allows draft editing while a published Zap remains active. Publishing
creates a version, and run details identify the version used and the data
received and sent by individual steps. Connected application accounts are
managed separately through app connections
[@zapier-versions-2026; @zapier-history-2026; @zapier-connections-2026].

For LangGraph's library API, the graph is defined and compiled in application
code. Checkpointers store execution snapshots organized by thread identity;
the application can retrieve current state and state history. Application
configuration and dependency provision remain part of the surrounding code.
This library-level comparison does not cover hosted deployment products
[@langgraph-graph-api-2026; @langgraph-persistence-2026].

The prototype groups available operations into configured **sources**, such as
a collection of Python functions or an external tool service. It exposes
saved definitions, source selection, and run inspection
as artifact, deployment, and run objects in its Python client. Its deployment
mapping selects a configured provider that must satisfy the saved source
requirements. An app connection or credential is therefore only a partial
analogy: the source also supplies operations and their contracts.

The system combines these lifecycle responsibilities with its typed graph
and Python client. Each system must
distinguish an edit to future work from the recorded
definition and data of a past execution.

## Implications for this design

[@tbl:positioning-summary] compares the documented mechanisms with the
prototype's ordinary execution model. Its rows describe selected mechanisms,
not every extension available in each product. The sources for n8n, Zapier,
and LangGraph are discussed in the preceding subsections.

| System | Data | Control selection | Combination |
| --- | --- | --- | --- |
| n8n | Item arrays | Branch connections | Merge modes |
| Zapier | Prior-step fields | Matching Paths | Explicit later actions |
| LangGraph | State updates | Edges and routers | Field reducers |
| Prototype | Output-to-state mappings | One outcome edge | Field reducers |

: Data and control mechanisms compared. {#tbl:positioning-summary}

For the report task, these models put different work on the author. n8n
requires attention to which items reach each node; Zapier requires mappings
from earlier steps and rules for qualifying paths; LangGraph requires state
and routing code. The prototype instead requires explicit output-to-state
mappings and declared routing outcomes. These differences concern where the
procedure's meaning is expressed, not just whether its editor is visual.

Pausing also has a separate contract. n8n's Wait node can resume on a time,
webhook, or form condition [@n8n-wait-2026]. LangGraph's dynamic interrupt
uses a checkpoint and thread identity; resuming restarts the interrupted node,
so code preceding the interrupt executes again [@langgraph-interrupts-2026].
The prototype uses an explicit interruption boundary and a declared resume
payload. A failed operation is not automatically such a pause: for example,
Zapier documents that an errored step produces no output fields for subsequent
mappings [@zapier-mapping-2026]. These observations do not establish equivalent
retry or side-effect guarantees across the systems.

The design prioritizes Python authoring, explicit mappings, and saved execution
records. An external agent can use the same client operations. Evaluating these
interfaces requires observing how authors discover operations, repair
definitions, and interpret results.

A configured Model Context Protocol (MCP) source supplies operations and
readable content; the workflow runtime determines routing and state updates
[@mcp-tools-2025; @mcp-lifecycle-2025].

# Conceptual Model

The report procedure connects operations that read notes, extract report
fields, and render a document. This chapter explains how the workflow
represents those operations, where their data is stored, and how execution
chooses the next step. It then follows the definition through saving,
configuration, and execution.

## Operations, data, and decisions

Suppose report preparation asks for missing information before rendering.
[@fig:report-branch] shows the two possible routes through that decision.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:report-branch width=95% caption="Alternative routes to report rendering."}
%%{init: {"flowchart": {"rankSpacing": 20, "nodeSpacing": 20}}}%%
flowchart LR
  Read["Read<br/>notes"] -->|ok| Extract["Extract<br/>report"]
  Extract -->|ok| Check{Complete?}
  Check -->|ready| Render["Render<br/>report"]
  Check -->|needs_information| Ask["Request information<br/>(interrupt)"]
  Ask -->|submitted| Render
  Render -->|ok| Finish([End])
```

Each operation or control step occupies a **node** in the workflow. An operation
node performs work, such as parsing notes into report fields. On completion,
it returns **output** data and an **outcome**, a label used to choose what
executes next. An **edge** connects that node and outcome to a successor.
The runtime follows the edge whose label matches the returned outcome.

In [@fig:report-branch], successful extraction returns `ok`, which leads to
the completeness check. That check selects `ready` or `needs_information`.
The first leads directly to rendering, and the second leads to a request for
input. Each decision selects one route. An exception while reading the notes
instead fails execution and is recorded as an error.

The edge labels explain execution order. The author separately declares which
data each node receives and which results it stores. Extraction stores report
fields, the request can supply missing fields on resume, and rendering reads
the resulting report. The following section describes those data connections.

**Schemas** declare the shapes of accepted inputs and produced results.
They help the author see which fields an operation requires and allow the
validator to detect incompatible mappings. The factual accuracy of extracted
information and the availability of a remote service require separate checks.

A node output is the data returned by one step. The workflow output is the
public data selected when the procedure completes. A run records that workflow
output alongside status, diagnostics, execution identity, and trace information.

## Workflow state and data bindings

**State** is the workflow's working data. In the report procedure, it contains
the notes, extracted report, and rendered Markdown. Each run starts with its
own working data and updates it as its nodes execute.

On a node's input and output, **data bindings** map values between named
locations. An input binding supplies an argument from workflow input, state,
or execution context, runtime information such as the current iteration item.
An output binding selects a returned value to write
into state. For example, extraction reads `state.notes` into its `text`
argument, then writes its returned report to `state.report`.

[@fig:report-state] shows the linear report procedure and its data access.
Solid arrows labeled `ok` are execution edges. Dashed arrows are data bindings,
labeled with the fields they read or write. The shared State block makes the
stored intermediate results visible alongside the operations that use them.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:report-state width=95% caption="Report nodes read and write workflow state through declared bindings."}
flowchart TB
  Input(["Workflow input: text"])
  subgraph Steps["Operations in execution order"]
    Read["Read notes"] -->|ok| Extract["Extract report"]
    Extract -->|ok| Render["Render Markdown"]
  end
  State["State<br/>notes · report · markdown"]
  Output(["Workflow output: report + markdown"])
  Input -.->|text argument| Read
  Read -.->|write notes| State
  State -.->|read notes as text| Extract
  Extract -.->|write report| State
  State -.->|read report| Render
  Render -.->|write markdown| State
  State -.->|select public result| Output
```

A **reducer** specifies how a write changes a state field. A replace reducer
stores the new report in place of the previous value. An append reducer can
instead accumulate reports from several documents in a list. These choices
determine the effect of a write when a node executes more than once.

The workflow's final output bindings select the public result from the
completed working data. Here they expose the report and Markdown, leaving the
intermediate notes in state. A node with no output bindings stores none of its
returned fields in workflow state.

## Saving, configuring, and running the procedure

While authoring, an **editable workflow** is the mutable definition being
revised. Saving creates an **artifact**: an immutable workflow version
containing its graph and declared requirements.

A **deployment** selects a saved version and connects its logical service
requirements to concrete configured services. For example, the same report
procedure could use a test notes source in one deployment and a production
notes source in another, provided both satisfy its required contracts.
Changing the bindings is different from changing how the report is assembled.

A **run** records one execution of a deployment. Last week's successful report
and this week's interrupted attempt are different runs, even if both use the
same artifact and deployment. Inspecting a run should identify the version,
inputs, status, and available execution evidence without changing the saved
procedure.

[@fig:lifecycle-records] relates the saved version, its deployments, and their
runs. The separate records preserve the distinction between changing a
procedure and examining an execution of it.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:lifecycle-records width=95% caption="One saved version can serve several deployments and runs."}
classDiagram
  direction LR
  class EditableWorkflow {
    mutable graph
  }
  class ArtifactVersion {
    artifact_id
    version
    saved definition
  }
  class Deployment {
    deployment_id
    source bindings
  }
  class Run {
    run_id
    input and result
    status and trace
  }
  EditableWorkflow ..> ArtifactVersion : saves
  ArtifactVersion "1" <-- "0..*" Deployment : selects
  Deployment "1" <-- "0..*" Run : started from
```

## Available operations and deployment binding

A **capability** describes functionality available from a source. The report
example uses operation capabilities, such as extraction and rendering. A
source catalog can also describe a resource, such as a readable text document.
Reading that resource is an operation that produces data for the workflow.

A **source** groups capabilities under a configured identity. The extraction
operation might come from trusted Python code, while another operation or
document is supplied by an external service.

A **deployment binding** connects a
logical source requirement in the workflow
to a concrete source in the environment. Validation checks whether that source
exists and matches the saved requirements. **Source drift** means those
requirements no longer match the currently available capabilities, for example
after an input schema changes.

Built-in sources have fixed platform identities and do not require those
deployment bindings. Configured sources remain explicit operator choices.
Portability is limited to environments with compatible code, credentials,
and services.

## Inspecting failure and resuming an interruption

A validation diagnostic concerns the definition or its dependencies before
execution. A failed run records an operational problem encountered during
execution. An interrupted run records an explicit request for input, together
with the state needed to continue. The interface should distinguish these
situations because they call for different actions.

In the report example, `needs_information` routes to a request step. The run
then saves its position and waits for the requested data. Resuming validates
the supplied response, applies the request step's declared data bindings,
and follows its continuation to rendering.

A **trace** records execution evidence associated with the run. It identifies
executed nodes and their outcomes, helping the operator locate a failed step
or follow a decision through the graph. The implementation chapters explain
how the runtime records this information.

## Working glossary

[@tbl:working-glossary] summarizes the core terms through the report example.

| Term | Meaning in the report example |
| --- | ------ |
| Capability | An available operation or resource, such as a text document |
| Node | One use of an operation or control step in the procedure |
| Output | Data produced by a step |
| Outcome | A declared label selecting the next step |
| State | Working data retained during execution |
| Artifact | An immutable saved version of the procedure |
| Deployment | A saved version connected to concrete services |
| Run | One execution with its own status and evidence |
| Source | A configured collection of available operations |
| Data binding | A mapping into node input, workflow state, or public output |
| Deployment binding | A mapping from a required source to a configured source |

: Working glossary for the thesis terminology. {#tbl:working-glossary}

The architecture chapter follows these concepts into the service and runtime.

# System Architecture

The weekly-report example needs more than a graph executor. An author must
discover the available operations, connect them, check the resulting workflow,
and choose which saved version to run. An operator must then distinguish a
bad definition from an unavailable service or an interrupted execution. The
architecture separates these responsibilities without requiring each caller
to implement the workflow lifecycle.

Three boundaries organize the system: authoring versus server operations,
saved definitions versus individual executions, and workflow execution versus
provider-specific calls. These boundaries are visible in the user-facing
objects as well as in the implementation.

## From authoring to server operations

The Python client is the main programmatic authoring interface. Its `App`
object represents a connection to the workflow service. An author can inspect
a capability, use the returned object in an editable workflow, validate that
workflow, and save it. The client reconstructs server responses as Python
objects with methods for further editing and inspection.

Local editing does not require a server request for each graph change.
Discovery and persistence do: the service owns the available capability
inventory and stored records. Validation therefore has both a local part,
which checks the authored structure, and a server part, which checks it
against the service's contracts.

[@fig:architecture-spine] shows this separation. The Python client and CLI
provide independent entry points to the same service.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:architecture-spine caption="Clients share lifecycle services and provider-independent execution."}
flowchart TB
  subgraph Authoring["Authoring and clients"]
    Python["Python App and editable workflow"]
    Client["Client port"]
    CLI["Command-line interface"]
    Python --> Client
  end
  subgraph Service["Server-composed services"]
    API["Workflow API"]
    Stores["Artifacts, deployments and run records"]
    Inventory["Available operation contracts"]
  end
  subgraph Transport["Transport boundary"]
    RPC["HTTP JSON-RPC adapter"]
  end
  subgraph Execution["Workflow execution"]
    Core["Workflow execution core"]
  end
  subgraph Integrations["Provider integrations"]
    Providers["Configured source providers"]
    Handlers["Execution handlers"]
  end
  CLI --> RPC
  Client --> RPC
  RPC --> API
  API --> Stores
  API --> Core
  Inventory --> API
  Providers --> Inventory
  Core --> Handlers
  Providers --> Handlers
```

The JSON-RPC adapter translates network requests into API calls and serializes
their responses. The API coordinates validation, storage, and execution.
The runtime handles graph behavior, including iteration returns and state
updates, for all clients of that API.

An agent can author or operate a workflow through these interfaces. During a
run, the runtime follows the saved graph. Agent participation within that
execution requires an authored operation that invokes the agent.

## Keeping a definition separate from its use

The client owns the editable graph. The service owns saved artifact,
deployment, and run records. The API coordinates validation and persistence
through their respective stores. Execution reads a selected definition and
its resolved environment rather than the client's mutable editor contents.

For the report workflow, this boundary prevents an unfinished edit to the
extraction step from becoming the definition of an already-created run.
Inspection retrieves the definition identity and execution evidence recorded
for that particular run.

The client exposes this progression through workflow artifact, deployment,
and run objects. A run object contains a snapshot of the stored execution.
Refreshing it requests a new snapshot. This makes network
activity explicit, although applications must decide when to refresh and how
to present progress.

A local/static server can also start deployment runs on a schedule. The
optional scheduler supports one-time and recurring starts and records each
scheduled occurrence for inspection. The Python client's `App.create_schedule()`
registers a schedule for an existing deployment; `App.schedule()` retrieves
its configuration, and `App.schedule_occurrences()` retrieves its history.
The server must have scheduling enabled to start runs without a connected
client. The Limitations chapter discusses scheduling's storage and
concurrency constraints.

The draft API and CLI also support persisted editing workspaces, which retain
unfinished definitions between requests. Python authoring can save directly
from its editable object. Both routes produce a saved definition for deployment
and execution.

## Executing the graph

The execution core is the runtime that runs the saved graph. It holds the
workflow input, working state, and the active node for each in-flight item
or call. To execute
an ordinary operation node, it resolves the node's input bindings, calls the
selected operation, checks its result, applies output bindings, and follows
the edge selected by the outcome. Repeating that cycle advances the run until
it completes, fails, or pauses for input.

Some nodes control execution directly. A condition chooses a route, a foreach
executes a body for each item, and an interrupt requests additional input.
A subgraph node calls another saved workflow and uses its result when that
call completes. The called workflow is referred to as the **child workflow**.
Its input and state belong to that invocation, just as a function call has
its own arguments and local working data.

Within a workflow, input bindings supply a step's arguments. Output bindings
select returned values to write into workflow state. Reducers determine how
those writes combine with existing values. The returned outcome selects the
next edge. Rendering therefore requires both a route that reaches the renderer
and an input binding that supplies the report it consumes.

[@fig:node-execution-cycle] follows one operation node through this cycle.
The later iteration examples show how the runtime manages additional
execution positions while a controller or child call is active.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:node-execution-cycle caption="A step updates state before following its selected route."}
flowchart TB
  Validation["Input validation"] --> Call["Invoke operation"]
  Call --> Result["Checked result"]
  subgraph Data["DATA: what becomes visible"]
    Output["Output payload"] --> Bind["Output bindings"]
    Bind --> Reduce["Reducers update state"]
  end
  subgraph Control["CONTROL: where execution goes"]
    Outcome["Declared outcome"] --> Route["Select matching edge"]
    Route --> Next["Next node"]
  end
  Result --> Output
  Result --> Outcome
  Reduce -.->|state available to next step| Next
```

A declared outcome such as `needs_information` is a workflow decision. A
handler exception or exhausted node-execution allowance is an execution
failure. A workflow can complete with an outcome such as `needs_information`
without failing the run. Where iteration supports collecting item
errors, that policy must be explicit; errors do not automatically become
ordinary outcome edges.

The runtime performs the illustrated operations in sequence: it applies the
result's writes before advancing along the selected route.

The trace makes the sequence inspectable, but a fixed graph does not imply
identical external results. Language-model calls, remote services, and
concurrent completion order can vary between runs. The runtime's defined
routing and state-update rules should not be confused with reproducibility
of every operation it invokes.

## Iteration and child workflow boundaries

Suppose the report now covers two documents, `A.md` and `B.md`. The
workflow must render a report for each, then assemble the two reports.
For this first example, foreach is configured to process one document at
a time. An output binding appends each rendered report to a `reports`
state field, which the assembly step reads.

[@fig:foreach-graph] shows the authored graph. The `loop` edge enters the
item body. Its return edge (a back-edge), `render.ok -> each`, ends that
item's work.
The `done` edge leads to the continuation after the collection is processed.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:foreach-graph width=90% caption="The item body returns to foreach; done continues to assembly."}
%%{init: {"flowchart": {"curve": "stepAfter"}}}%%
flowchart LR
  Prepare["Prepare documents"] -->|ok| Each["each: Foreach"]
  Each -->|loop| Render["render: Render item"]
  Render -->|ok: item return| Each
  Each -->|done| Assemble["Assemble reports"]
  Assemble -->|ok| Finish(["End workflow"])
```

In serial mode, A's return advances the foreach to B. B's return completes
the collection and permits the `done` transition to assembly. Returning to
the active foreach controller therefore finishes one item. Entering that
controller from `Prepare documents` starts an invocation over the collection.

[@fig:foreach-region] follows one invocation from start to finish. Read
downward for time. Solid arrows request work or apply a data binding;
dashed arrows report completion. Here line style distinguishes requests from
completion; in [@fig:report-state], it distinguishes execution from data
bindings. The labels identify the data and routes.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:foreach-region width=95% caption="Serial iteration finishes both documents before assembly."}
sequenceDiagram
  participant Each as Foreach
  participant Body as Render item
  participant State as reports state
  participant Assemble as Assemble
  Note over Each,State: Start once: documents = [A.md, B.md], reports = []
  Each->>Body: loop: process A.md
  Body->>State: Output binding: append reportA
  Body-->>Each: ok: A is finished
  Each->>Body: loop: process B.md
  Body->>State: Output binding: append reportB
  Body-->>Each: ok: B is finished
  Note over Each: Both items finished
  Each->>Assemble: done: continue after the loop
  Assemble->>State: Input binding: read reports
  State-->>Assemble: [reportA, reportB]
```

The same authored render node executes twice. The runtime distinguishes
those executions so that finishing A advances to B, while finishing B permits
assembly. Each item returns to the foreach invocation that started it.

The return edge in [@fig:foreach-graph] records the item's return. Its output
binding appends the report before that return. A node in the body belongs to
that loop's control region. The validator rejects using the same node both
inside and outside the body, or returning past the immediate enclosing loop.
Ordinary cycles within one region remain valid, subject to the run-wide
node-execution limit described in the implementation chapter.

### Calling a child workflow for one document

Now replace the rendering work with a call to a saved child workflow that
summarizes one document. Consider only the item for `A.md`. The parent may
know the whole collection, but the child receives only the input explicitly
mapped into its call: `{"document": "A.md"}`.

The child has its own input, working state, and execution context. Access to
the parent's current item or `reports` field requires an input binding that
supplies the needed value.

[@fig:scope-boundaries] shows the call and its return. The item waits while
the child runs. The child's `END` returns its result to the calling node.
The parent item then continues to its own return edge.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:scope-boundaries width=95% caption="Child completion precedes the parent item's return to foreach."}
sequenceDiagram
  participant Item as Parent item A
  participant Child as Child workflow
  participant Result as A's pending report writes
  participant Each as Foreach
  Item->>Child: Input binding: document = A.md
  activate Child
  Note over Item: Wait for child
  Note over Child: Own input,<br/>state and context
  Child->>Child: Produce summaryA
  Child-->>Item: END: return summaryA
  deactivate Child
  Item->>Result: Output binding: append summaryA to reports
  Item-->>Each: ok: item A is finished
  Note over Each: Continue according<br/>to foreach mode
```

Input and output bindings cross the child workflow boundary explicitly.
The two completion points are separate:
child `END` returns to the calling node, and the calling node's route back
to foreach finishes the item.

### When items run concurrently

In concurrent mode, A and B may be in progress together. A returning from
its child does not permit assembly while B is still running. The foreach
waits for its required item completions before following `done`.

Concurrent items retain separate pending writes until their results combine.
In [@fig:scope-boundaries], “A's pending report writes” represents these pending
writes. In serial mode, the output binding updates enclosing state so the next
item can read it.

For the successful two-item concurrent case, the foreach combines A's and
B's writes using the declared reducers, then assembly reads the combined
state. An append reducer and a replace reducer have different effects;
neither the arrows nor the fact that both items completed chooses a merge
policy. The serial ordering in [@fig:foreach-region] is not a promise
about concurrent completion order.

If a child or item reaches an explicit interrupt, the run preserves which
document was active and where it was waiting. Resume applies the response
to that saved position and continues the same item.

## Connecting external operations

Providers adapt external functionality to contracts the workflow system can
inspect and invoke. Discovery supplies schemas and declared outcomes;
execution supplies a handler for the selected binding. These are separate
responsibilities because discovering an operation does not mean its service
is currently reachable or authorized.

The server composes providers, stores, and the API. MCP-backed operations
may need sessions, authentication, and remote catalog handling. Python-backed
operations use configured, trusted imports. These concerns remain outside
the graph scheduler. An experimental OpenAPI provider explores another
source family without making it the system's product identity or implying
that all API descriptions are interchangeable.

This boundary reduces provider-specific logic in workflows, but cannot
erase provider differences. A schema describes a call's shape; it does not
guarantee availability, cost, side-effect safety, or semantic equivalence to
another operation with the same fields.

# Implementation

Running the report procedure involves two kinds of work. The author first
uses the Python client to construct, validate, and save a graph. The service
then executes that saved graph with supplied input and records the result.
This chapter follows those operations into the implementation, including
the additional bookkeeping needed when steps repeat, call child workflows,
or pause for input.

## Following a Python authoring request

The client package, `wf_client`, exposes the `App` connection object introduced
in the architecture chapter. Its `App.from_http_jsonrpc(...)` constructor
records the service address. A later capability lookup contacts that service,
checks the returned identity and contract, and constructs a Python capability
object for the author to inspect or add to a workflow.

The editable workflow object reuses the graph-building methods of
`WorkflowBuilder`, the authoring layer's builder. It adds validation through
the service and saving. Validation first checks the graph locally. If a
binding references an undeclared state field, the client returns that error
immediately and records that server validation was not run. Otherwise, it
submits the graph for the server to check against its available operations.

Saving validates first, submits the plan, checks the save response, and
re-inspects the exact artifact version. The resulting artifact object is
therefore reconstructed from the stored definition. Identity checks reject
responses identifying a different artifact or deployment.

The Python client's `Run.refresh()` method retrieves a stored run by its
identifier through the service's `inspect_run` operation. It verifies the
run and deployment identities and returns a new run snapshot. `Run.resume()`
submits the requested response for an interrupted run and also returns a new
snapshot. The caller retains the returned object to see the updated status
and result.

Requests and responses are serialized at the service boundary. Reconstructing
the returned objects in the client gives authors the same inspection methods
after discovery, execution, and later refreshes.

## Executing nodes and limiting a run

The `wf_core` package implements the execution cycle shown in
[@fig:node-execution-cycle]. It selects a ready execution position and handles
the selected node according to its kind. An operation node invokes the
callable chosen through the deployment. A control node instead performs its
built-in behavior, such as choosing a condition route or starting iteration.

A cycle can revisit nodes indefinitely if its condition never selects an
exit. To bound this work, the run has a **node-execution limit**, with a default
of 10,000. Immediately before dispatching a node, the runtime checks that
capacity remains and increments the run's counter. This check and increment
is called admission. The counter therefore counts admitted executions,
including control nodes, repeated visits, and executions that subsequently
fail. It does not count only successful operations.

The policy is represented by `RunLimits.max_steps`, and the stored counter
is `steps_executed`. Iteration and child workflows share this run-wide limit.
Asynchronous execution reserves capacity before launching the selected work.
When no capacity remains, the runtime fails the run before ever dispatching
another node. The limit and counter persist with saved execution state, so
resume continues with the remaining allowance.

This bounds the number of admitted node executions. The duration of a single
operation and the cost of its external requests require separate controls.
A handler that blocks indefinitely can still prevent progress after admission.

## Preserving state across nested execution

Consider the two-document iteration in [@fig:foreach-graph]. In serial mode,
document A appends its report to state before document B starts. B can read
that updated state. In concurrent mode, A and B each work with a separate
view of state. An item can read its own updates while its sibling's pending
writes remain isolated. The foreach combines their updates after the required
items finish, using the state fields' reducers.

Nesting introduces an additional case. Suppose each document is processed
concurrently, but its sections are processed serially. Section 2 can read the
work of section 1 in the same document. Those updates still belong to that
document's separate view until the outer foreach combines the documents.
Publishing every serial section's write directly into shared workflow state
would expose unfinished document results to the other concurrent items.

The runtime tracks the active node, the workflow invocation that owns its
data, and the updates held separately by concurrent items. A **frame** records one
execution position, including its current node and owning foreach when it is
an iteration item. A **scope** contains the input, working state, and context
of one workflow invocation. A child workflow has its own scope. A **lineage**
records state updates associated with an execution, allowing concurrent items
to retain their separate views.

An output binding produces a **patch**, a set of state writes together with
the information needed to apply their reducers. The runtime follows the
item's enclosing iterations to determine where that patch belongs. Serial
iterations pass their writes outward. At the nearest concurrent item boundary,
the writes are buffered in that item's lineage. With no such boundary, they
update the workflow scope's state. The runtime checks the complete ownership
chain before applying a patch, so malformed parent records cannot cause a
partial update.

Merging nested results also requires distinguishing an accumulated value from
new writes. Suppose state already contains report A, and an inner group adds
B and C. The resulting view is `[A, B, C]`. If an outer merge appends this
whole view to the existing `[A]`, it produces `[A, A, B, C]`, which is undesirable.

The patch therefore retains the new writes, or **contributions**, separately
from the accumulated value visible to readers. In this example the new
contributions are B and C. A later merge applies those writes using the
declared reducer, preserving A once. This bookkeeping supports nested
concurrent iteration even when its intermediate results are merged again.

## Giving expressions a consistent context

Suppose an outer foreach named `documents` processes documents, and an inner
foreach named `actions` processes each document's action items. A node in the
inner body may need both the document and the current action. A single
unnamed current-item value would leave it unclear which one the node receives.

**Execution context** supplies runtime information to expressions and input
bindings. Its foreach entries are keyed by the authored controller's identity.
Inside the inner body, two paths distinguish the enclosing document from
the current action:

```text
context.foreach.documents.item  # enclosing document
context.foreach.actions.item    # current action
```

The runtime constructs these entries from the active frames. Repeated
executions of the same node therefore resolve the paths to their own items.

A condition checking an item's fields and an input binding reading those
fields receive the same context view. Validation checks these references
against the corresponding schema. This avoids accepting a path that a
condition would treat as absent while another reader can resolve it.

Context construction stops at the current workflow scope. If a child workflow
needs the parent's document, its caller supplies that value through child
input, as shown in [@fig:scope-boundaries]. On resume, persisted frame ownership
identifies the active items. Missing owners, parent cycles, or conflicting
aliases are reported as errors before those records are used to construct
context.

## Validation, persistence, and diagnostics

Validation first checks declarations that can be evaluated without running
operations. For example, two output bindings on the same node might write
`foo` to `state."1"` and `bar` to `state."1"."2"`. The first targets an entire
field and the second targets a location inside it. Applying both could replace
the container that the nested write needs. The validator rejects overlapping
destinations within that binding list, avoiding a dependency on their order.
Here `"1"` and `"2"` are literal field names. The rule also applies to ordinary
names such as `state.report` and `state.report.title`.

This check concerns one set of mappings. Successive nodes can intentionally
update related state fields, and concurrent iteration combines writes through
its separate reducer rules. Validation also checks graph structure and rejects
illegal iteration returns. Server validation checks the available operations
and service connections. At execution time, the runtime checks actual values
and execution records as they become available.

Diagnostics carry a code, a location, a message, and, where available, a
repair hint. For an overlapping destination, the location identifies the
node's output-binding list. These fields let a caller locate the faulty
mapping and decide how to repair it. The revised definition then passes
through validation again.

The API lifecycle layer stores stopped runs and their **checkpoints**, saved
representations of execution state. An interrupted checkpoint contains the
position and working data needed to resume. Restoration validates those
records before execution continues from the explicit interruption boundary.
The limits of recovery for external operations are discussed in Limitations.

Separating artifact, deployment, and run storage also keeps inspection
focused. Definition inspection explains what was saved; deployment
inspection explains environment selection; run inspection and trace explain
what happened during a particular run.

## Server and provider responsibilities

The remaining package boundaries put these operations into a service.
`wf_api` coordinates lifecycle operations, `wf_artifacts` supplies storage
contracts and implementations, and `wf_platform` supplies shared platform
contracts. `wf_server` composes these dependencies. `wf_transport_rpc_http` exposes
the JSON-RPC interface, while `wf_cli` provides terminal operations.

Provider implementations retain their own lifecycle requirements. MCP
support manages remote discovery and invocation through configured
connections. Python support loads trusted configured callables; it is not
a sandbox for arbitrary submitted code. OpenAPI support remains
experimental and is not evidence that every described HTTP service can
already be used without adaptation.

The case study follows these components through one public authoring session:
discovery, revision, validation, persistence, execution, and inspection.

# Case Study: Deterministic Report Workflow

Consider an author who receives weekly notes and needs two deliverables: a
structured report for further processing and Markdown for a reader. The
example workflow makes this small procedure reusable:

The three operations read notes, extract a report, and render Markdown.

The input is deliberately constrained. Notes contain named sections and
action lines with owner, task, and due-date fields. Extraction parses that
format using deterministic Python code. Fixed input and local operations
allow the result to be checked independently of remote credentials, service
quotas, or variation in generated text.

The bundle at
[`examples/report_workflow/`](../../examples/report_workflow/) supplies the
operations, fixture notes, server configuration, and a saved raw-plan example.
The walkthrough below expresses the same procedure through the current
Python client. The example README retains a command-line route for operators
who need that interface.

## Starting with available operations

The operator supplies the source identities through the example configuration.
This walkthrough discovers operations within those known sources; source
administration is outside the session.

The example configuration registers three trusted Python operations under
`local.report` for discovery and `local.report_runtime` for execution.
Their Pydantic models describe the input and output
contracts. The author consumes those operations from the service inventory;
the client does not import their implementations to execute them locally.

The Python blocks form one asynchronous session. The retained test runs that
session against the real service in process. It substitutes an in-process
connection for the HTTP connection assignment; remote server startup is not
tested.
For HTTP use, the blocks assume a
server using `examples/report_workflow/wf.config.json`, reachable at its
configured address, and a fresh artifact name or unused version. The fixture
read assumes the client is running from the repository root.

```python
from pathlib import Path

from pydantic import BaseModel

from wf_authoring import input_from, input_path, output_to, state_path
from wf_client import App

app = App.from_http_jsonrpc("http://127.0.0.1:8771/rpc")
available = await app.capabilities(source_id="local.report")
for operation in available.items:
    print(operation.qualified_name)
read_notes = await app.capability("local.report.read_notes")
extract_report = await app.capability("local.report.extract_report")
render_report = await app.capability("local.report.render_markdown_report")
print(extract_report.output_schema)
```

Each lookup returns a capability object with its schemas and declared
outcomes. The extraction schema exposes `title`, `summary`, `action_items`,
`risks`, and `followups`. The title names the report and the summary records its
overview. Action items describe work to do: `owner` identifies the responsible
person, `task` describes the work, and `due` records its due-date text. Risks
list potential problems, and followups record matters needing later attention.
The author can use the schema directly or declare corresponding local models.
This session uses local models without importing provider implementation code.

The listing contains `local.report.read_notes`, `local.report.extract_report`,
and `local.report.render_markdown_report`. Their inspected contracts describe
how each can be connected. The author selects the operations and constructs
the graph from those contracts.

Before connecting operations, the author checks their input and output
contracts for compatible fields.

## Describing the workflow's data

The workflow has one public input, intermediate state, and two public
outputs. They are declared separately so that intermediate notes do not
accidentally become part of the result contract.

```python
class ActionItem(BaseModel):
    owner: str
    task: str
    due: str


class ReportOutput(BaseModel):
    title: str
    summary: str
    action_items: list[ActionItem]
    risks: list[str]
    followups: list[str]
```

These author-defined models represent the discovered report fields. The
workflow then declares its own input, working state, and public output:

```python
class NotesInput(BaseModel):
    text: str


class ReportState(BaseModel):
    notes: str = ""
    report: ReportOutput | None = None
    markdown: str = ""


class ReportResult(BaseModel):
    report: ReportOutput
    markdown: str


graph = app.new_workflow(
    "report_python_showcase",
    input_schema=NotesInput,
    state_schema=ReportState,
    output_schema=ReportResult,
)
```

The models export schemas that are stored with the workflow contract. This
lets the service validate its data after the author's Python session ends.
The report starts as absent in intermediate state. The public result requires
a report because the successful pipeline produces one before completion.

## Connecting data and decisions

The author now creates three node uses. Each use selects an operation and
declares its data bindings. In the first mapping, `input_path("text")` selects
the workflow's public input field, and the second `"text"` names the reading
operation's argument. They happen to share a name. The extraction mapping
instead reads the state field `notes` into an argument named `text`.

In an output binding, `"."` selects the complete returned object. Extraction
stores that whole report in `state.report`, while reading and rendering store
only selected output fields:

```python
read = graph.use(
    read_notes,
    id="read",
    input=[input_from(input_path("text"), "text")],
    output=[output_to("text", state_path("notes"))],
)
extract = graph.use(
    extract_report,
    id="extract",
    input=[input_from(state_path("notes"), "text")],
    output=[output_to(".", state_path("report"))],
)
render = graph.use(
    render_report,
    id="render",
    input=[input_from(state_path("report"), "report")],
    output=[output_to("markdown", state_path("markdown"))],
)
```

The author connects each operation's `ok` outcome to the next node, ending at
a node that completes the workflow with outcome `ok`. Final output bindings
then select the report and Markdown from state:

```python
end = graph.end("ok", id="finished")
graph.set_entry_point(read)
graph.connect(read, "ok", extract)
graph.connect(extract, "ok", render)
graph.connect(render, "ok", end)
graph.set_output([
    input_from(state_path("report"), "report"),
    input_from(state_path("markdown"), "markdown"),
])
```

Using `"."` selects all fields of that node's output. An empty output-binding
list would store none of them in workflow state. The `connect` calls determine
execution order independently of these data selections.

The author declares data mappings and execution routes separately. Changing
one leaves the other unchanged. This requires additional declarations even
for a linear three-step procedure; typed helpers construct the serialized
representation from those declarations.

## Diagnosing and repairing a binding

An editable graph can temporarily be invalid. Suppose a final output binding
reads `missing_report`, although the declared state contains `report`:

```python
# Deliberately refer to an undeclared state field.
from textwrap import fill

graph.set_output([
    input_from(state_path("missing_report"), "report"),
    input_from(state_path("markdown"), "markdown"),
])
broken = await graph.validate()
assert not broken.ok
assert broken.remote_status == "not_run"  # Local rejection skips the server check.
for issue in broken.local.errors:
    print(issue.code, issue.path)
    print(fill(issue.message, width=72))
```

The example prints the diagnostic code, its location, and the message wrapped
to fit the page:

<!-- walkthrough-output -->
```text
invalid_source_path output[0].path
source path must start with input., state., or context. and reference a
declared root field when applicable
```

The location identifies the first workflow-output mapping. Comparing its
source with the declared state reveals the misspelled reference. Local
validation rejects the graph before a server validation request is made.
The author repairs that reference and validates again:

```python
# Repair the field reference, leaving the nodes and edges unchanged.
graph.set_output([
    input_from(state_path("report"), "report"),
    input_from(state_path("markdown"), "markdown"),
])
(await graph.validate()).raise_for_errors()
```

The repair changes the workflow's output mapping. The renderer and its
outgoing edge remain unchanged.
This illustrates why data bindings and control routes need separate feedback.
The next section saves only the repaired definition.

## Saving a version and choosing its environment

Before saving, the author can request validation and inspect its diagnostics.
The example stops on errors:

```python
validation = await graph.validate()
validation.raise_for_errors()
artifact = await graph.save(version=1)

deployment = await artifact.deploy(
    "report_python_showcase.local",
    bindings={"local.report": "local.report_runtime"},
)
readiness = await deployment.validate()
if not readiness.runnable:
    raise RuntimeError(readiness.diagnostics)
```

The artifact is the saved version of the authored procedure. The deployment
maps the saved `local.report` requirement to `local.report_runtime`.
The fixture registers compatible operations under both names so this choice
changes the execution source without changing the graph or requiring remote
credentials. Both source identities refer to the example's local operations.

[@fig:python-lifecycle] summarizes the public operations; it omits internal
validation and re-inspection calls made by individual client methods.

<!-- markdownlint-disable-next-line MD013 -->
```{.mermaid #fig:python-lifecycle caption="Local editing leads to saved, configured, and inspected execution."}
sequenceDiagram
  actor Author
  participant Client as Python client
  participant API as Workflow service
  participant Provider as Bound operation
  Author->>Client: Build and revise graph
  Client->>Client: Check structure locally
  Client->>API: Validate and save definition
  API-->>Client: Saved artifact version
  Client->>API: Bind deployment to version
  API-->>Client: Deployment and readiness
  Client->>API: Run with input
  API->>Provider: Invoke bound operation
  Provider-->>API: Output and outcome
  API-->>Client: Run snapshot
  Client->>API: Inspect run and bounded trace
  API-->>Client: Stored execution evidence
  Client-->>Author: Result or diagnostic
```

An edit to the workflow would be saved as another version, leaving this
deployment's artifact unchanged. Selecting a different source environment
changes the deployment's bindings. A readiness check
can reject a missing or incompatible binding before a run is attempted.

## Running and inspecting the result

The client reads the notes and sends their contents. The server therefore
does not need access to the client's file path.

```python
notes = Path("examples/report_workflow/input.md").read_text(encoding="utf-8")
# Permit at most 100 admitted node executions across this run.
run = await deployment.run({"text": notes}, max_steps=100)
if run.status != "completed":
    raise RuntimeError((run.status, run.diagnostics))

result = ReportResult.model_validate(run.output)
assert result.report.title == "Weekly Project Update"
assert len(result.report.action_items) == 3
assert result.markdown.startswith("# Weekly Project Update")

run = await run.refresh()
trace = await run.trace(start=0, limit=10)

print("Status:", run.status)
print("Title:", result.report.title)
print("Action items:", len(result.report.action_items))
print(result.markdown.splitlines()[0])
```

The session prints a compact result summary and the first line of the rendered
Markdown:

<!-- walkthrough-output -->
```text
Status: completed
Title: Weekly Project Update
Action items: 3
# Weekly Project Update
```

The run exposes a status, output, and diagnostics independently of the
editable graph. The output crosses the API as data; the explicit
`model_validate` call reconstructs the example's Pydantic result model.
Refreshing obtains the latest stored snapshot, while the bounded trace
provides step-level evidence when output alone is insufficient.

For this fixed fixture, the result includes three action items, the
recorded risks and followups, and a Markdown report headed
“Weekly Project Update.” Checking these fields establishes that the
example's data reached the intended outputs. The extraction contract requires
the sectioned notes format described at the start of this chapter.

## Registering a scheduled run

The same deployment can be invoked later by the server's scheduler. With
scheduling enabled on the local/static server, the client can register a
one-time start and inspect its stored configuration and occurrence history:

```python
from datetime import UTC, datetime, timedelta

schedule = await app.create_schedule(
    schedule_id="report-once",
    deployment_id=deployment.deployment_id,
    trigger={
        "kind": "oneshot",
        "at": (datetime.now(UTC) + timedelta(hours=1)).isoformat(),
    },
    input_bindings=[{
        "target": "text",
        "expression": {"kind": "literal", "value": notes},
    }],
    max_steps=100,
)
schedule = await app.schedule(schedule.schedule_id)
history = await app.schedule_occurrences(schedule.schedule_id, limit=10)
```

The input binding stores the current contents of `notes`. The scheduled run
receives that text even if the original file changes. A recurring workflow
that needs fresh notes would need an operation that retrieves them when it
runs. Occurrence history records what happened to scheduled starts, including
admission and completion. Immediately after registration, it can be empty.

The session check verifies registration and inspection without waiting for
the scheduled time. Scheduler execution is exercised separately in
[`test_scheduled_deployment_example.py`](../../tests/examples/test_scheduled_deployment_example.py).

## What this case demonstrates

The executable check in
[`test_thesis_python_walkthrough.py`](../../tests/examples/test_thesis_python_walkthrough.py)
reads and runs this chapter's Python blocks, including the rejected binding
and its repair. It checks the stored report, the returned snapshot, and the
two displayed output excerpts against the session's captured output.
The existing tests in
[`test_report_workflow_example.py`](../../tests/examples/test_report_workflow_example.py)
check the source's input rules, rendering and extraction, capability
discovery and invocation, and the artifact/deployment/run lifecycle using
the raw-plan fixture. Those tests are evidence for the report operations
and lifecycle, complementing the Python session check.

The Python session shows the current authoring steps:
inspect operations, declare contracts, connect data and outcomes, save,
select bindings, and inspect an execution. The CLI and draft surface offer
alternative interfaces to related lifecycle operations.

A direct Python script would be shorter for these three local functions.
The additional structure becomes relevant when the definition
must be saved, bound to an environment, validated independently, and
inspected through a shared service. This example demonstrates the integration
of those operations.

The case exercises a linear pipeline. Conditional branches, foreach bodies,
child workflows, and interrupts are covered by separate runtime tests listed
in the evaluation's evidence index. Independent authoring and diagnosis tasks
are needed to evaluate the interaction beyond this scripted session.

# Evaluation

Evaluation distinguishes three questions: whether the runtime follows its
contract, whether the public lifecycle composes correctly, and whether an
author can use that lifecycle effectively. The reported in-process walkthrough
exercises the public lifecycle. Targeted tests for runtime contracts are
mapped in the Evidence Index.
The authoring assessment identifies the operations available through the
interface. Usability remains a separate evaluation question.

## Requirements and evidence

[@tbl:requirements-evidence] relates the authoring requirements R1–R5 and
execution requirements X1–X5 to the available evidence. E1–E5 refer to the
five groups in the Evidence Index appendix: E1 covers lifecycle operations,
E2 Python authoring, E3 validation, E4 runtime execution, and E5 source
providers. These labels locate implementation and test files. The table
distinguishes the walkthrough's observations from listed tests and the
usability measurements still needed.

| Requirement | Evidence | Assessment |
| --- | --- | ---- |
| R1 Discovery | Walkthrough; E2 | Exercised; usability unmeasured |
| R2 Data movement | Worked graph | Explained; comprehension unmeasured |
| R3 Revision feedback | Binding repair; E3 | Exercised; usability unmeasured |
| R4 Editing vs running | Lifecycle; E1–E2 | One saved version exercised |
| R5 Status interpretation | Inspection; E1–E2 | Exposed; usability unmeasured |
| X1 Definition/run identity | Lifecycle; E1 | Lifecycle tests listed |
| X2 Known constraints | Validation; E3 | Constraint tests listed |
| X3 Routing and state | Runtime; E4 | Runtime tests listed |
| X4 Environment choices | Sources; E5 | Provider tests listed |
| X5 Bounds/inspection | Node limits/resume; E1, E4 | Boundary tests listed |

: Requirements and available evidence. {#tbl:requirements-evidence}

The R4 observation covers saving and running version 1. The session does not
edit a later version or check its effect on earlier runs. Understanding the
version distinction remains part of the authoring evaluation.

X5 covers the run-wide node-execution limit, execution inspection, and
interruption resume. Node-execution limit tests check that the counter
persists and that exhaustion prevents further dispatch. Lifecycle tests
exercise stored inspection and resume. The report session exercises inspection
with a limit of 100 node executions. It completes within that limit and
contains no interruption. The evidence index locates the separate tests for
exhaustion and resume.

## Walkthrough method and observations

The retained walkthrough test uses the example server configuration and a
fresh pytest temporary store. It extracts the case study's Python blocks in
document order and substitutes an in-process client connection for the HTTP
connection assignment. All later calls use the real API, provider, and stores.
This setup exercises discovery, graph construction, validation, saving,
deployment, execution,
refresh, and trace inspection, while excluding network startup and transport
behavior from the observation.

[@tbl:prototype-conformance] records the requirement-linked checks performed
by that session. The fixture expectations are fixed independently of the workflow's
result, and stored output is inspected again through the API.

| Check | Expected observation | Requirement |
| ---- | ----- | --- |
| Invalid output mapping | Local rejection at `output[0].path` | R3, X2 |
| Repaired graph | Validation permits saving and running | R3, X2 |
| Persisted report | Expected title and three action items | X1, X3 |
| Persisted rendering | Expected Markdown heading | X3 |
| Run and trace | Completed snapshot; recorded steps | R5, X5 inspection |

: Reproducible report-session checks. {#tbl:prototype-conformance}

The checked session completed with the expected title, three action items,
and Markdown heading in the saved output. The invalid binding was rejected
locally; restoring the mapping allowed validation and execution to continue.
These observations establish the displayed procedure's behavior for this
fixture, not an author's ability to construct it unaided.

The session check and the existing report-example tests can be run with:

```powershell
$suites = @(
    "tests/examples/test_thesis_python_walkthrough.py",
    "tests/examples/test_report_workflow_example.py"
)
uv run pytest @suites -q -n 0
```

The first suite executes the displayed case-study session with the stated
transport substitution. The second checks the operation fixtures and raw-plan
lifecycle independently. Document rendering and generated PDF assets are not
part of this command. The test and manuscript must be taken from the same
repository revision because the test reads the manuscript directly.

## Separating design comparison from evaluation

The earlier comparison of n8n, Zapier, and LangGraph explains different
authoring and execution choices. The same task has not been measured across
those systems under matched conditions. This report therefore cannot rank
their usability, reliability, or performance against the prototype.

Execution tests ask whether contracts, routing, state updates, and persistence
behave as specified. Interaction evaluation asks whether an author can
discover operations, express a procedure, understand errors, and recover
without inspecting implementation code. Passing one kind of test does not
answer the other.

The report walkthrough exposes costs such as explicit bindings and deployment
selection, but does not measure whether those costs are acceptable to new
users. Similarly, structured diagnostics and inspection objects may help an
agent avoid trial and error, but reduced retries, token use, and repair time
remain hypotheses rather than measured outcomes.

## Falsifiability criteria

The implementation would fail its stated contracts if, for example:

- a saved run could not identify the definition and bindings it used;
- an invalid foreach boundary were accepted and executed as another region;
- nested state writes were lost or counted twice;
- resume reset the node-execution counter or resumed the wrong item;
- condition evaluation used a different context model from validation;
- ordinary source invocation required provider-specific graph routing.

These cases support focused regression tests. Broader architectural claims,
such as accommodating future source families without changing the runtime,
remain design expectations to assess as those integrations are built.

# Limitations

The prototype demonstrates a workflow lifecycle under controlled conditions.
Its main limitations concern how much authors must understand, which
execution guarantees are provided, and how far the available evidence can
be generalized.

## Authoring and diagnosis still require technical knowledge

The Python client reduces manual serialization and provides editable graphs
and inspectable objects. It does not remove the need to understand schemas,
state bindings, outcomes, and deployment selection. The report example makes
this cost visible: a short procedure requires more declarations than direct
function calls.

Diagnostics identify many invalid structures and bindings, but an accurate
message is not necessarily an understandable repair instruction. Authors
still need to distinguish a graph error from an environment problem or a
failed external operation. The current evidence does not establish that new
users can make these distinctions without assistance.

Inspection also requires judgment. A trace shows recorded execution, not
whether a report is factually correct or a remote side effect was desirable.

## Execution guarantees have defined boundaries

Foreach iteration, nested workflow scopes, structured context, and run-wide
node-execution limits are implemented foundations. They do not yet provide general
fork/gather control for arbitrary branches. In particular, concurrent
iteration should not be presented as a solution to correlating branches
that split, loop, and later meet at different gather points.

Persisted interrupted runs can resume at explicit boundaries. This is not
arbitrary mid-handler crash recovery, replay of every external call, or an
exactly-once side-effect guarantee. A run's step limit bounds admitted graph
steps; it does not bound a handler's execution time or the cost of its
external requests.

Schema validation checks declared structure, not business truth. A returned
report may satisfy its schema while containing incorrect information.
Similarly, a fixed graph specifies routing but does not make remote
responses or concurrent completion order reproducible.

## Deployment and trust assumptions

The controlled examples assume trusted operators and trusted Python sources.
Python operations execute in the server process without a sandbox. The
thesis does not establish multi-tenant isolation, role-based authorization,
or production-grade credential management. An explicit workflow interrupt
can request a response, but is not by itself an authenticated approval or
access-control mechanism.

Source bindings make environment choices inspectable rather than making
workflows universally portable. A destination environment still needs
compatible operations, credentials, and dependencies. Python sources are
loaded at startup; changing their code requires a server restart. The shared
provider interface does not yet unify every provider's administration,
authentication, and live health behavior.

Filesystem-backed stores support the demonstrated persistence paths. Their
use does not establish production performance, cross-process contention
behavior at scale, or disaster recovery. A future database implementation
would still need to preserve the lifecycle's transaction and ownership
contracts; changing the storage engine alone would not prove those
properties.

Scheduled execution uses the same deployment and run records as direct
invocation. Its ownership model assumes one scheduler over the participating
file stores and is supported only by the local/static server configuration.
This bounds the deployment conditions under which the scheduling mechanism
can be used; it is not a distributed execution service.

The provider interface exposes callable operations. It does not reproduce a
provider's interactive widgets or its complete user interface.

## Limits of the evidence

The deterministic report fixture demonstrates lifecycle integration, not
broad document understanding or graph expressiveness. Targeted tests cover
additional execution mechanisms. The evidence index identifies those tests;
this report records execution results for the report-session suites.

The in-process setup also limits the walkthrough to service composition; it
does not test the displayed HTTP connection. Without independent authoring
tasks or matched cross-system measurements, its results cannot establish
ease of use, repair efficiency, or comparative performance. Those questions
require the interaction evaluation described in Future Work.

# Future Work

The remaining questions concern richer execution semantics, the effectiveness
of authoring and diagnosis, and operation beyond the controlled environment.

## Establish general fork and gather semantics

General fork/gather requires a rule for identifying which concurrent work
belongs to the same invocation. Existing frames, scopes, iteration activations,
and state lineages provide foundations, but their relationships must remain
coherent through nested execution and resume.

A fork creates concurrent execution branches; a gather must determine
which arriving branches belong together before combining their state.
Loops, partial gathers, and repeated visits make this more than waiting
for a fixed number of arrivals. The design must also preserve contribution
identity so that a write already included in one merge is not applied
again in a later merge.

This work remains planned, with reference-model verification preceding
production implementation. Pressure cases should become executable tests
for correlation, merge behavior, and recovery. The authoring contract also
needs to make clear which graphs are rejected before execution and which
decisions remain the author's responsibility.

## Evaluate the authoring and recovery experience

A focused usability study should ask participants to discover an operation,
build a small workflow, change its contract, diagnose a broken binding, and
inspect a failed or interrupted run. Useful measures include task completion,
time to a correct repair, unnecessary retries, and reliance on source-code
inspection.

Execution and interaction should be evaluated together without conflating
them. For example, a validation rule may correctly reject a graph while its
diagnostic fails to explain the ownership boundary that was crossed.
Conversely, a convenient editing operation must not hide a change to the
workflow's execution meaning.

The Python client and CLI should receive evidence appropriate to their own
interaction styles. For agent trials, fix the interface, allowed operations,
task fixtures, and success criteria before collecting results. Check an
agent's report against saved artifacts, deployment identity, run output, and
recorded interactions. Human evaluation can test whether the lifecycle
vocabulary and data-binding model are understandable without implementation
knowledge.

## Durable waiting within a run

Scheduling starts a new run of a saved deployment. Waiting until a time or
event during an existing run would instead require a durable suspension point
and rules for resuming it. Future work should distinguish these two forms of
timed execution rather than treat a wait operation as another schedule.
Distributed execution would additionally require an ownership model beyond
the current single-scheduler arrangement.

## An assistant-backed authoring application

A companion local-first assistant application has an execution backend for
persistent Python shells, streamed code output and images, execution approvals,
and saved conversations. Its chat interface is under development. The shells
allow an agent to retain Python objects across interactions, which provides
an environment in which it could use the workflow client. Code executes on
the local machine without a sandbox. Saved history survives backend restart,
but previous shells are not restored and unfinished work is not replayed.

Integrating this application with workflow authoring and administration remains
to be evaluated. An author needs to inspect and correct a proposed procedure
before running it, then follow its status and respond to interruptions.
Dedicated views of the client's artifact, deployment, and run objects could
support that interaction. The present case study evaluates the workflow
client directly and includes no agent-driven authoring session.

## Preserving contracts across operational changes

The source catalog already represents resources, and a built-in workflow
operation can read resource text with a size bound. Further authoring work
could make these resources easier to select and inspect alongside operations,
including clear treatment of content types and truncated results.

Additional providers and storage backends would test whether the architectural
boundaries hold beyond the demonstrated implementations. The question is
whether an integration can preserve source compatibility, run identity, and
recovery behavior without changing the graph's execution rules. Such work
needs failure tests and deployment evidence as well as a working adapter.

Richer debugging should clarify what can safely be resumed or repeated,
especially around external side effects. Showing more trace information is
different from promising that an earlier action can be undone.

# Conclusion

This report examined how a useful procedure can become a reusable workflow
that an author can define and an operator can inspect. The implemented
system separates the saved definition, its deployment bindings, and each
execution into artifacts, deployments, and runs. Those distinctions give
workflow use a record beyond the lifetime of an editing session or a single
script invocation.

The graph model separates data movement from control movement. Contracts
and bindings describe what a step receives and writes. Outcomes select
transitions, while runtime constructs govern iteration, child scopes,
and interruption. The Python client exposes this model through authoring
and inspection objects, while the API and provider boundaries connect it
to configured operations.

The report case demonstrates that these representations compose into a
save–deploy–run–inspect lifecycle for the fixed fixture. The repository also
contains targeted tests covering execution requirements X1–X5; the evidence
index locates them without reporting a combined execution result.
For the authoring objectives R1–R5, the
work identifies and exercises supporting interfaces, but their effectiveness
for independent human or agent authors remains an open evaluation question.

Explicit contracts expose data mappings, saved versions, and run records,
while requiring authors to learn more concepts than a sequence of function
calls. The implemented contribution is the integration of these contracts
into a programmable lifecycle, demonstrated by saving, configuring, executing,
and inspecting the report procedure through the public client.

<!-- References -->
# References {#sec:refs .unnumbered}

::: {#refs}
:::

\appendix
<!-- Appendices -->
# Evidence Index

This appendix maps the evaluation's evidence identifiers to implementation
and tests. Paths identify inspectable evidence; they are not a claim that
all listed suites passed in one newly recorded full-system run.

## Core workflow lifecycle

**E1**: artifacts, deployments, scheduled execution, stopped runs, and explicit
resume boundaries.

- `src/wf_artifacts/models.py`
- `src/wf_artifacts/runs/`
- `src/wf_api/run_lifecycle.py`
- `tests/wf_api/test_artifact_api.py`
- `tests/wf_api/test_run_api.py`
- `tests/examples/test_scheduled_deployment_example.py`

## Python authoring and inspection

**E2**: reconstructed client objects, editable workflows, and the report fixture.

- `src/wf_client/`
- `tests/wf_client/test_authoring.py`
- `tests/wf_client/test_deployments.py`
- `tests/wf_client/test_runs.py`
- `examples/report_workflow/`
- `tests/examples/test_report_workflow_example.py`
- `tests/examples/test_thesis_python_walkthrough.py`

The example's README retains the command-line route for operators who need
it. That alternative interface is not an additional evaluated case in the
current thesis.

## Validation and diagnostics

**E3**: structural validation, source compatibility, and repair information.

- `src/wf_core/validation/`
- `src/wf_artifacts/validation.py`
- `tests/artifacts/test_validation.py`
- `tests/core/test_structured_context_validation.py`
- `tests/core/test_foreach_control_regions.py`

## Execution ownership and node limits

**E4**: nesting, structured context, concurrent iteration, and persisted
node-execution limits.

- `src/wf_core/runtime/`
- `tests/core/test_foreach_back_edges.py`
- `tests/core/test_concurrent_foreach_interrupts.py`
- `tests/core/test_structured_runtime_context.py`
- `tests/core/test_run_step_budget.py`
- `tests/core/test_run_step_budget_codec.py`
- `tests/core/test_run_step_budget_async.py`

## Source provider boundary

**E5**: source contracts and provider-specific execution behind server composition.

- `src/wf_platform/sources.py`
- `src/wf_server/config.py`
- `src/wf_sources_python/`
- `src/wf_sources_mcp/`
- `src/wf_api/source_helpers.py`
- `tests/wf_sources_python/test_loader.py`
- `tests/wf_sources_mcp/test_runtime.py`
- `tests/wf_transport_rpc_http/test_mcp_backed_server_rpc.py`

The existing providers demonstrate this separation for their implemented
operations. They do not establish equal lifecycle features across providers
or prove compatibility with every future source family.