Diachron: Provenance for AI-Written Code

The Problem

AI is writing a meaningful chunk of new code, but the engineering workflow still assumes a human authored it. When something breaks, you can git blame the lines, but you can’t answer the questions that matter:

• What was the intent?
• What actions actually changed the repo?
• Was anything verified after the change?
• What’s the rollback surface area?

Most “LLM observability” tools trace inference, not software state change. For engineering teams (and even solo devs), that’s the gap.

The Thesis

Software engineering needs provenance from intent to evidence, not just tokens and latency.

Diachron’s core idea: bind each AI session to a concrete chain:

prompt → tool call(s) → diff hunks → commands/tests → verification → PR/merge

If you can reconstruct that chain, you can generate:

• PR narratives that ship with proof
• “semantic blame” that explains why a line exists
• targeted rollback patches with high confidence

The Wedge: PR Evidence Packs

The first “can’t go back” feature is an evidence pack that turns a PR description into something closer to an audit log:

## What Changed
- src/auth/login.ts: Added refresh token logic (+45 lines)
- src/auth/token.ts: New token storage module (+120 lines)

## Why (Intent)
User asked: "Fix the 401 errors on page refresh"

## Evidence Trail
- Commands executed: npm test, npm run build
- Tests: ✅ 47 passed, 0 failed
- Build: ✅ Success

The goal is to make “trust, but verify” cheap.

PR Evidence Pack Example (Expanded)

Below is the level of detail Diachron is designed to generate as a single PR comment. It’s intentionally skimmable for reviewers, but structured enough to be auditable.

## Diachron Evidence Pack

### Summary
This PR was created with AI assistance. Diachron captured the chain from intent → diff hunks → verification.

### Intent
- Prompt: "Fix the 401 errors on page refresh"
- Scope: src/auth/*

### What Changed (by file)
- src/auth/login.ts (+45 / -12)
- src/auth/token.ts (+120 / -0)
- src/auth/__tests__/token.test.ts (+96 / -3)

### Verification
- Commands: npm test, npm run build
- Tests: ✅ 47 passed, 0 failed
- Build: ✅ Success

### Coverage
- Matched events → commits: 87%
- Unmatched events: 3 (notes/log output)

### Rollback
- Generated reverse patch available for this session

The Linking Graph (Moat)

Diachron isn’t a single log. It’s a linking graph that lets you trace a line of code to a specific AI intent + evidence.

session
  ├─ exchanges (prompts / responses)
  ├─ tool_calls (edit/write/run)
  ├─ events (diff hunks, file paths)
  ├─ verification (tests, builds)
  └─ git_context (branch, commits, PR)

This graph is what enables “semantic blame” and high-confidence rollback.

Key Design Choices

Local-first by default

The data that makes provenance valuable (prompts, diffs, repo context) is also the data developers least want to ship to a vendor. Diachron is designed so the default mode works locally, with exportable artifacts when needed.

Evidence > vibes

Diachron favors concrete signals (tests executed, build succeeded, commands run) over “the model said it’s correct.” It’s not about generating confidence—it’s about recording proof.

What’s Next

• A CLI that posts PR evidence packs automatically
• A diachron blame command that explains code with confidence grading
• A one-click rollback patch per session (and a safety-first “dry run” diff preview)