AdversaryGate MCP server

AdversaryGate (v2.14.1)

High AI usage ≠ high confidence. The cost of an agentic coding pipeline is

not the model's intelligence — it is the pipeline's self-deception.

Uso alto de IA ≠ confiança alta. O custo de um pipeline com agentes de

código não está na inteligência do modelo, está no autoengano do pipeline.

An evidence-based fail-closed verification gate for AI coding agents where uncertainty is a first-class result (INCONCLUSIVE) instead of a silent approval.

Measures Python/pytest repositories today. On other stacks it runs your suite via --test-command but answers INCONCLUSIVE, never MERGE — see Language support.

pip install adversary-gate

🎬 Watch it decide (60 seconds)

B and C are the same patch. Same code, same tests, same execution. The only difference is that C brought evidence (--diff + --coverage-json); B brought none.

That middle row is the whole product. An unproduced measurement is not a measurement, so it cannot clear a floor — and a number nobody can re-derive is an opinion with a false precision label.

python3 demo/demo.py          # runs the three scenarios; exits non-zero if one regresses
python3 demo/render_gif.py    # rebuilds demo/demo.gif from the captured frames

demo/demo.py is an acceptance test, not a screenshot: it builds the fixtures, runs the real CLI as a subprocess, reads the real exit codes, and fails if any of the three decisions changes. Its evidence artefacts are checked in under demo/out/.

INCONCLUSIVE is not an error. It is the gate saying "I did not measure that", and it is exit 2 so CI treats it as "do not merge yet", not as "pass". See How diff_coverage gets its value for the one-step path from INCONCLUSIVE to MERGE.

🎯 The Thesis

1. High AI usage ≠ high confidence. The more a team leans on agents in its real engineering flow, the more the cost of "looks right" shows. Agents write fluent, plausible code, and naive pipelines that collapse infrastructure errors into "pass" approve patches whose tests are broken or never ran.
2. Switching models is a symptom. Teams move between models looking for PRs they can trust. What is missing is deterministic verification, not a better model. AdversaryGate runs the same test harness with no LLM in the verifier, which is what makes patches from different models comparable at all.
3. The loss is concrete. Merged regressions, tasks reported done that are not, rollbacks, and reviewer hours spent re-reading the same PR.
4. Less pipeline self-deception. The product does not sell a smarter AI. It sells a pipeline that says "I did not measure that" instead of a green check. How to count that honestly is in Provenance & metrics — and the counter this README used to point at is zero by construction on the gate's own logs (AG-029), so it is no longer the pitch.

🔒 The Fail-Closed Model & Double-Filter Rigor

Shortened. The full README is on GitHub.