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20 audited attempts, 0 shipped false accepts, 3 verified auto-accepted wins (tokei class-5: +10.7%, +3.7%, +9.9%; ~1.25x vs upstream with Phase-0 wins). Independent 20-way adversarial re-audit (phase2-final-audit workflow) confirmed every verdict follows its evidence, correct equivalence tier, no harness-gaming, ledger append-only intact. Closeout changes: - case study: full 20-row attempt table, scoreboard, corrected comrak-010 CI upper bound (+12.1%, was +12.2%), exit-criteria section flipped to ALL MET, OS-isolation gap marked closed. - CLAUDE.md Current-phase line: Phase 2 complete, next Phase 3. - verdict: derive the ledger 'phase' column from the run_id namespace (phaseN-*) instead of the hardcoded 1 the re-audit flagged; historical rows are immutable and stay as written (run_id prefix is authoritative). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
84 lines
6.6 KiB
Markdown
84 lines
6.6 KiB
Markdown
# hotpath — profile-guided optimization agent
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> Working name. Rename freely; nothing depends on it.
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## Mission
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An agent that profiles real binaries and services, isolates hot paths, proposes optimizations, and accepts a change **only** when (a) behavioral-equivalence gates pass and (b) the benchmark improvement is statistically significant on trusted infrastructure. Output: verified performance deltas translated into ROI reports nobody can dispute.
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Full engineering spec: `SPEC.md`. Read it before starting any phase.
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## Non-negotiables (re-read every session)
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1. **No unverified performance claims.** A change without passing equivalence gates AND a significant bench delta does not exist. Discard it. Log it.
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2. **Measure before optimizing.** No patch is proposed until profile data identifies the hot path. Never trust a single benchmark run.
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3. **Cheap wins first, always.** Build flags → LTO → PGO → allocator swap → *then* code changes. Check the ledger before re-attempting any class of change on a target.
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4. **Equivalence gates are hard gates.** Changes touching floating-point ordering, concurrency primitives, or anything UB-adjacent are NEVER auto-accepted — verdict is `needs-human-review`.
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5. **Every attempt goes in the ledger**, including failures and rejections. Failures are future training data.
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6. **The agent never modifies the trust layer.** `crates/`, `config/`, `corpora/`, and upstream test suites are read-only to the agent. Patches may touch only allowlisted paths under `targets/<name>/`. See SPEC.md §10.
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7. **Sandbox everything.** Target code runs only inside no-network containers. Never on the host.
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8. **Statistical bar:** accept only if the bootstrap 95% CI lower bound of speedup ≥ threshold in `config/accept.toml` (default 2%), from interleaved A/B runs on the same pinned hardware.
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## Architecture map
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- `crates/bench-runner` — trust layer: containerized, CPU-pinned, interleaved A/B benchmarking with bootstrap confidence intervals and A/A self-tests
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- `crates/diff-test` — behavioral equivalence: upstream test suite + golden I/O replay + differential fuzzing of changed functions + sanitizers
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- `crates/ledger` — append-only SQLite record of every attempt: hypothesis, patch, gate results, bench deltas, verdict, cost
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- `crates/report` — ROI generator: cores saved × $/core-hr, latency percentile deltas, CIs, workload caveats printed on every number
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- `agent/` — Claude Agent SDK (Python) loop, prompts, tool definitions
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- `targets/` — vendored OSS targets for case studies (permissive licenses only)
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- `playbook/` — optimization classes, ordered, with preconditions and known risks
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- `results/` — generated reports and flamegraphs
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## Current phase
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**Phase 2 COMPLETE — all SPEC §5 exit criteria met.** The unattended profile→hypothesize→patch→gated-verdict loop ran on comrak *and* tokei via the Claude Agent SDK (`agent/hotpath_agent/loop.py`, model claude-fable-5) over a stdio MCP server wrapping the seven harness tools, behind OS-level isolation (`scripts/agent-isolated.sh` mountns/user modes, `just isolation-check` 19/19). **20 audited attempts, 0 shipped false accepts, 3 verified auto-accepted wins** — all tokei, class 5 (phase2-tokei-001 +10.7%, -003 +3.7%, -008 +9.9%; compounded ~1.25× vs upstream v14.0.0 with the Phase-0 wins), each fuzz-clean (`scripts/diff-fuzz-*.py`, 10k inputs) and ASan/LSan-clean. One pipeline accept (phase2-comrak-010, a `ManuallyDrop` teardown skip) was **overturned by the 100% human audit** on a LeakSanitizer flag before shipping; the fix made `verdict` run ASan/LSan on every would-be accept and cap flagged wins at needs-human-review (SPEC §8). Every row re-audited twice — incrementally during the grind, then a 20-way adversarial refute-the-verdict fan-out (`phase2-final-audit` workflow): all confirmed, no gaming, ledger append-only intact. Case study: results/phase2/case-study-agent-loop.md. **Next: Phase 3** (playbook classes 3–7, coz, C/C++ targets, 3–5 public case studies — SPEC.md §5).
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## Commands
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Fill these in as the harness is built; keep them current:
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```
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just profile <target> # perf record + flamegraph for a target's benchmark workload
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just bench <target> <ref> # interleaved A/B: pristine baseline vs working tree
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just gates <target> # test suite + golden replay + diff-fuzz + sanitizers
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just verdict <target> # runs gates + bench, writes ledger row, prints verdict
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just report <run-id> # ROI report from ledger
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just aa <target> # A/A calibration run (must show null result)
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```
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Built so far (command-level, pre-target-integration):
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```
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just build / test / lint # trust-layer workspace: cargo build/test/clippy+fmt
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just aa [cmd] # A/A self-test of bench-runner on a shell command
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just compare <a> <b> # interleaved A/B of two shell commands, bootstrap CI
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just gates <target> # corpus pin + upstream tests + golden replay
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just pin-check <target> # verify corpus MANIFEST.sha256
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just calibrate <cmd> <out> # automated A/A + regression-injection calibration
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just verdict <t> <bin> ... # gates + bench vs pristine-rebuilt baseline + ledger row
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cargo run -p harnessd # agent IPC daemon: six-tool JSON surface (SPEC §3.5)
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```
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## Definition of done — one optimization attempt
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- [ ] Hypothesis logged in ledger *before* patching
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- [ ] Patch touches only allowlisted paths under `targets/<name>/`
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- [ ] Upstream test suite green
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- [ ] Golden replay byte-identical (or within the target's explicit FP tolerance policy)
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- [ ] Differential fuzz on changed functions: no divergence (60s or 10k iterations minimum)
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- [ ] ASan + UBSan clean; TSan if the patch touches anything threaded
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- [ ] Interleaved A/B bench passes the CI-lower-bound threshold
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- [ ] Ledger row written with verdict and full evidence
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- [ ] If FP-ordering / concurrency / UB-adjacent: verdict `needs-human-review`, never auto-accept
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An attempt that fails any gate is a **valid, complete outcome** — write the ledger row and move on. Do not iterate on a rejected patch more than twice without a new hypothesis.
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## Conventions
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- Rust workspace (edition 2021+) for all harness crates; `just` for task running
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- Python 3.11+ for `agent/`; Claude Agent SDK — verify current package name and API at https://docs.claude.com/en/docs/claude-code/overview before scaffolding
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- Every number shown to a human carries its confidence interval and workload description. No naked percentages, ever.
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- Commit style: `phase0:`, `bench:`, `gates:`, `agent:`, `playbook:` prefixes
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- When in doubt between shipping a feature and hardening measurement, harden measurement. The product is trust.
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