agent-release-gates 0.1.1

Release-readiness gates for AI agents: replay known incidents, apply policy-as-code gates, and produce ship/warn/block evidence before an agent, prompt, model, or tool-policy change ships.

Agent Release Safety Gates

Release-readiness gates for AI agents — replay known incidents, apply policy-as-code, and produce ship / warn / block evidence before a changed agent, prompt, model, or tool policy ships.

Project page · Evaluation report · Quickstart · Docs

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Quickstart

pip install agent-release-gates

# Run the deterministic release gate on a built-in pack → exits non-zero on a block.
agent-safety release-gate

# Score your own agent: materialize an example pack, convert your logs, gate them.
agent-safety init-example --dest incident_pack_minimal
agent-safety export-candidate-results --input incident_pack_minimal/agent_run_log.jsonl \
  --output candidate_results.jsonl --candidate-id my_agent_v1
agent-safety release-gate --incident-pack incident_pack_minimal \
  --candidate-results candidate_results.jsonl

# Or run the incident-replay suite under Inspect (UK AISI).
pip install inspect_ai
inspect eval agent-release-gates/incident_replay --model openai/gpt-4.1-mini

See the evaluate-your-agent quickstart for the full pip-only workflow.

The core install is intentionally lean (only pydantic) and ships the CLI, the Inspect suite, the real-agent runner, and the scoring logic. The API and dashboard are opt-in extras:

pip install "agent-release-gates[api]"        # FastAPI evidence service
pip install "agent-release-gates[dashboard]"  # Streamlit reviewer dashboard

[!NOTE] These results are engineering evidence over controlled, synthetic benchmarks — not claims of real-world production performance. This project is not a clone, assessment, or reverse-engineering of any company's internal AI system. The operations benchmark is synthetic by design; TechQA and WixQA are used separately as public retrieval-validation datasets.

What it does

Before a changed agent ships, the lab answers five release questions and turns the answers into a reproducible gate:

• Grounding — does the agent retrieve the right evidence and cite it?
• Refusal — does it abstain when evidence is weak, unsafe, or prompt-injected?
• Approval — does it require sign-off before side-effecting tool calls?
• Auditability — does it leave enough trace, audit, and monitoring evidence?
• Replay — does it pass incident replay and policy-as-code release gates?

The first module is an Incident Replay Suite that turns redacted synthetic incidents into regression fixtures, replay results, release gates, and incident memos. The output is a reproducible evaluation artifact — deterministic runners, generated reports, CI checks, a Dockerized runtime, a Streamlit dashboard, and a GitHub Pages report — rather than a one-off dashboard.

How it works

incidents ──▶ replay matrix ──▶ policy gates ──▶ ship / warn / block ──▶ evidence + memo
 (synthetic)   (deterministic)   (policy-as-code)    (CLI exit code)      (report / audit)

Point it at your own agent by exporting candidate results (generic logs or LangChain/LangSmith-style traces) and scoring them against the gates. See the evaluate-your-agent quickstart, incident pack schema, and candidate results schema.

Evidence snapshot

Area	Current result
Controlled benchmark	358 synthetic golden cases, 60 red-team cases, 180 synthetic operations tickets
Retrieval	100.00% synthetic retrieval hit rate@3 with local TF-IDF/vector-style retrievers
Public RAG validation	160 TechQA cases and 80 WixQA cases evaluated separately from the synthetic benchmark
Safety	90.91% classifier recall, 0 high-severity false negatives in the current challenge set
Agent governance	100.00% mock side-effect block rate and approval audit rate
Incident replay	8 seeded synthetic incidents replayed, 100.00% closure rate, 0 replay must-not violations
Intervention study	3 deterministic safety studies plus public RAG grounding and memory/context studies
Hosted judge calibration	Reviewed OpenAI and Anthropic judge runs with public-safe provider comparison

Key findings

• Safety scores are not meaningful alone — the lab reports over-review cost, benign auto-blocks, weak-evidence handling, and unsafe misses beside the headline numbers.
• Layered safeguards reduce selected prompt-injection, unsafe-action, and unsafe-request failures in controlled studies while making review burden visible.
• Public RAG grounding thresholds reduce unsupported answer attempts while keeping abstention and review cost visible.
• Memory/context controls reduce polluted-memory following while preserving benign memory usefulness; goal-conflict arbitration reduces unsafe goal-following while preserving benign task completion.
• The strongest next validation step is independent human labelling, followed by broader multi-model comparison.

What's included

• Evaluation runners for retrieval, extraction, safety classification, controlled-agent behavior, and observability.
• Baseline-vs-intervention studies for instruction hierarchy, action-risk gates, safety-classifier review policy, RAG grounding, memory/context pollution, and goal conflict.
• Incident replay suite with seeded incidents, replay matrix, release gates, regression fixtures, and generated memos.
• Candidate-results exporters for generic agent logs and LangChain/LangSmith-style traces.
• Streamlit dashboard, GitHub Pages report + PDF, and a benchmark/dataset/failure-taxonomy documentation set.
• CI, Docker, Docker Compose, linting, tests, and deterministic report regeneration.

Run from source

uv sync
uv run python scripts/run_all_evals.py

# Release gate (console command); exits non-zero on a blocking failure.
uv run agent-safety release-gate --policy config/incident_release_policy.json

# Interactive dashboard → http://localhost:8510
uv run streamlit run streamlit_app.py --server.port 8510

Run the API and dashboard together with docker compose up --build, then open http://localhost:8510 and http://localhost:8000/health.

Drive a real LLM through the release gate:

# Any OpenAI-compatible / self-hosted open-model endpoint.
$env:AGENT_RUNNER_API_KEY = "..."
uv run python scripts/run_real_agent_replay.py

Verification commands

uv run ruff check .
uv run pytest
uv run python scripts/run_all_evals.py
uv run agent-safety release-gate --policy config/incident_release_policy.json
uv run python scripts/build_public_site.py
docker build -t agent-release-safety-gates:local .

CI runs linting, tests, deterministic report checks, local OpenTelemetry smoke testing, Dockerized collector verification, and Docker build verification.

Documentation

Topic	Link
Evaluate your agent (quickstart)	docs/evaluate_your_agent_quickstart.md
Benchmark card	docs/benchmark_card.md
Dataset card	docs/dataset_card.md
Failure taxonomy	docs/failure_taxonomy.md
Agent-safety intervention study	docs/agent_safety_intervention_study.md
RAG grounding intervention	reports/rag_grounding_intervention.md
Memory/context intervention	reports/memory_context_intervention.md
Goal-conflict intervention	reports/goal_conflict_intervention.md
Incident pack schema	docs/incident_pack_schema.md
Candidate results schema	docs/candidate_results_schema.md
Reviewer handoff pack	docs/reviewer_handoff_pack.md
Technical artifact index	docs/technical_artifacts.md
Dashboard deployment	docs/dashboard.md
Contributing	CONTRIBUTING.md

Limitations

• The controlled benchmark is synthetic and still partly templated.
• Public TechQA and WixQA tracks use compact samples, not the full upstream datasets.
• Human-review labels are currently simulated workflow labels; independent reviewer labels are prepared but not yet published.
• Hosted-model evidence includes reviewed judge-calibration runs, not a broad multi-model agent comparison.
• Provider-backed embedding and reranker adapters are prepared, but credentialed hosted results are not claimed until reviewed.

Roadmap

• Collect independent human labels using the prepared review packet.
• Add reproducible multi-model comparison across hosted and open-source models.
• Expand public RAG validation beyond the current compact TechQA and WixQA samples.
• Add more framework-specific candidate-results exporters.
• Extend the intervention report with external-reviewer disagreement analysis.

[!TIP] Want to help validate this? External review is explicitly invited — start with the reviewer handoff pack and CONTRIBUTING.md.