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07. AI Delivery Constraints and Roadmap

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This page covers original Sections 14-16.

14. Constraints for AI-Assisted Implementation

This repository will be developed incrementally with AI tools such as Claude or Codex in VS Code.

Therefore the project artifacts must be written so that an AI tool can understand and follow them.

Requirements for documentation and implementation slices:

  • each feature should have one ADR, one spec, and one task
  • tasks should be explicit about scope and non-scope
  • code changes should be incremental and reviewable
  • architecture rules should be restated in task files where relevant
  • future features should not be implemented early without being requested
  • explanations should be practical, not academic
  • slice specs should reuse the canonical terms and state names from this document instead of inventing alternate wording
  • slice tasks should name the exact requirements sections they implement and the acceptance criteria they must satisfy
  • later roadmap themes may be grouped into one umbrella slice when that creates better medium-sized AI tasks without hiding scope

The repository should act as the stable memory of the project so AI tools do not need repeated re-explanation from chat history.

15. Success Criteria

This project is successful if it achieves all of the following:

  • a believable industrial desktop prototype exists
  • core runtime behavior is correct and understandable
  • the code structure is clean enough to evolve
  • the system demonstrates the major topics discussed in this project
  • AI tools can implement feature slices reliably using repo documentation
  • the engineer can use the repo both to build and to learn

16. Future Expansion Areas

These areas were identified as future expansions at project start.

Since then, persistent run history and recipe file management have already moved into prepared or implemented slices.

For planning, the remaining near-term core work may be grouped into one operational maturity slice that covers richer diagnostics, richer fault recovery, configurable simulator profiles, and more detailed inspection results.

The following may still be added later if the core prototype is stable:

  • operational maturity improvements grouped into one core slice
  • more realistic frame generation
  • trend charts and metrics
  • explicit state machine library if needed
  • performance instrumentation and memory profiling hooks

These are future enhancements, not first-step requirements.

Docs-first project memory for AI-assisted implementation.