78 lines
2.8 KiB
TypeScript
78 lines
2.8 KiB
TypeScript
export type PreparedInputFixture = {
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readonly name: string;
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readonly markdown: string;
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};
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// These excerpts mirror examples/lda_report_workflow/documents for an offline
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// presentation preview. Selection is run evidence; this content is not.
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const preparedInputFixtures: Readonly<Record<string, string>> = {
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"project-brief.md": `# Project Brief
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lda.chat is a workflow substrate for AI-agent-facing workspace automation. The
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prototype separates external planning from deterministic workflow execution.
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Key achievements:
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- Typed Draft, Artifact, Deployment, Run, and Trace lifecycle records.
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- Source-provider boundary for platform, MCP, Python, and experimental OpenAPI sources.
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- JSON-RPC and CLI surfaces usable by external agents and human operators.`,
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"architecture-notes.md": `# Architecture Notes
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Lifecycle:
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- Drafts are mutable authoring workspaces with revisions.
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- Artifacts are immutable versioned workflow definitions.
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- Deployments bind logical source ids to configured concrete sources.
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- Runs persist stopped execution records and bounded traces.
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Runtime:
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- The core executes typed graph nodes and routes by declared outcomes.
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- Interrupt nodes pause at explicit human-in-the-loop boundaries.`,
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"evaluation-findings.md": `# Evaluation Findings
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Evidence:
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- Automated tests cover core runtime, artifacts, deployments, CLI, JSON-RPC,
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source providers, and examples.
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- A 36-trial audited agent challenge campaign evaluated the product-facing CLI
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under bounded conditions.
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Limitations:
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- Agent challenge runs are operational evidence, not a controlled model study.
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- The prototype does not claim production security, scheduling, RBAC, or a
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general autonomous planning algorithm.`,
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"risk-register.md": `# Risk Register
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Material risks:
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- Title and product framing can overstate the implemented autonomous-agent layer.
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- Evaluation evidence is stronger as systems evidence than as a controlled study.
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- File-backed stores are useful for auditability but not a production transaction boundary.
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Mitigations:
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- Keep the agent/substrate boundary explicit in the thesis and defense.
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- Present challenge data as bounded operational evidence.`,
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"roadmap.md": `# Roadmap
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Near-term:
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- Add self-describing interrupt request and resume contracts.
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- Build a deterministic lda.chat report workflow with typed issue approval.
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- Build a local Workflow Console over JSON-RPC.
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- Add live-demo replay support for the defense.
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Later:
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- Add production secret stores and transactional persistence.
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- Add a surrounding agent interface and planner loop.`,
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};
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export const preparedInputFixture = (path: string): PreparedInputFixture | null => {
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const name = path.split(/[\\/]/).at(-1) ?? path;
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const markdown = preparedInputFixtures[name];
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return markdown ? { name, markdown } : null;
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};
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