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peltier.io/docs/src/architecture.md
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Claude 5d6c259b30 docs: full mdBook site + GitHub Pages deploy; rewrite README as project front page
- docs/: an mdBook documentation site — thesis, architecture, measurement
  discipline, equivalence gates, agent loop, isolation/anti-reward-hacking,
  playbook, ROI, reproduce, roadmap, and the five case studies (pulled from
  their single source in results/ via {{#include}}). Builds clean (21
  pages).
- .github/workflows/docs.yml: builds mdBook and deploys to GitHub Pages on
  push to main (one-time setup: Settings → Pages → Source = GitHub Actions).
- README.md: rewritten as the project front page — the thesis, the verified
  results table, layout, quick start, how it stays honest, links to the
  book. Replaces the stale Phase-0-era text.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
2026-07-12 00:20:20 +00:00

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Architecture

hotpath is split into a trust layer (Rust, the agent cannot write it) and an untrusted proposer (the agent). The split is structural, not advisory: the guarantees hold even if the proposer is adversarial.

crates/            Trust layer — agent has NO write access (SPEC §10)
  bench-runner/    Interleaved A/B timing, bootstrap CIs, A/A calibration,
                   service-mode latency (coordinated-omission correct)
  diff-test/       Equivalence gates, corpus hash-pinning, per-target spec
  ledger/          Append-only SQLite attempt ledger (enforced by triggers)
  report/          ROI: speedup CI → cores → dollars, methodology inline
  verdict/         The pipeline in one command: gates → bench → ledger row
  harnessd/        The one door the agent talks through (7 JSON ops)
agent/             Untrusted proposer (Claude Agent SDK, Python)
playbook/          Optimization classes 1–7, tried strictly cheapest-first
config/            accept.toml (thresholds), pricing.toml (ROI inputs)
targets/           Vendored OSS targets — the only agent-writable path
corpora/           Hash-pinned golden-replay inputs (read-only to agent)
results/           Calibration evidence, case studies, generated reports

The seven-tool boundary

The agent never runs a shell, never writes a file, never touches the ledger directly. It speaks to harnessd — the only door — through exactly seven JSON operations:

Tool What it can do
read_profile Read a target's ranked hotspots (read-only)
read_ledger Read prior attempts — the anti-double-attempt memory
read_playbook Read an optimization class's preconditions/procedure
read_target_source Read a workspace file at the pinned commit (windowed)
propose_patch Submit a unified diff — path-allowlisted, then git apply
run_verdict Launch the gate+bench pipeline (detached)
read_verdict Poll the append-only ledger for the result

Note what is absent: no write outside targets/<name>/workspace, no shell, no way to run the bench or write the ledger except through the verdict binary, which only records a pass after the full gated pipeline.

The data flow of one attempt

profile ─▶ agent hypothesizes ─▶ propose_patch ─▶ [path allowlist + git apply]
                                                          │
   ledger row ◀─ verdict ◀─ bench (interleaved A/B) ◀─ gates (tests, golden,
   (append-only)             vs pristine-rebuilt         fuzz, sanitizers)
                             baseline

The baseline is rebuilt from a pristine checkout every session — never from the agent's workspace — so a patch cannot poison its own comparison. The verdict is decided by the CI-lower-bound rule and written to an append-only ledger the agent cannot mutate.

The next chapters walk each stage: measurement, equivalence gates, the agent loop, and the isolation that makes the boundary load-bearing.