agent-assure

Output equivalence is not process equivalence.

Same approval. Missing evidence link. Configured CI gate blocked.

agent-assure catches declared, observable process regressions in agent releases that final-answer-only checks can miss. It turns privacy-filtered run evidence into reproducible comparisons, reviewer-facing artifacts, portable evidence packets, and ordinary CI gate signals.

Local-first · offline flagship demo · versioned artifacts · CI-native · no hosted control plane required

Run the offline demo · Inspect the reviewer output

For AI leaders · For architects · For engineers

10 deterministic fixture cases · 0 decision fields changed · claim-duration: linked → missing · classification: new_failure · configured CI gate: blocked

[!NOTE] This is a bundled deterministic fixture demonstration, not a model benchmark, live-model result, or customer outcome. It requires no provider API key, network call, or token spend.

Read the flagship demonstration

Choose your path

What it can surface

A final-answer-only check can see no decision-field change while a declared release expectation regresses:

• Evidence and RAG support: a required source or material claim-to-evidence link disappears, or declared corpus and retrieval identity changes. Example: MATERIAL_CLAIM_MISSING_EVIDENCE → new_failure.
• Human review: a required route or performed-review record is missing.
• Provider, tool, and privacy boundaries: a forbidden provider or tool appears, or declared route, redaction state, or detector identity changes.
• Usage and reliability: retries, tool calls, tokens, latency, rate-limit events, or declared estimated cost change materially.
• Streaming integrity: events are replayed, duplicated, conflicting, or outside the declared sequence contract.
• Protocol-bound live behavior: repeated observations drift outside a declared protocol or comparison boundary.

A surfaced difference may be blocking, review-only, or informational. Usage and reliability deltas block only when a suite or policy declares that behavior.

Quickstart

Requires Python 3.11 or newer.

pip install agent-assure
agent-assure demo flagship --out .tmp/demo/flagship --clean

The installed package runs the bundled deterministic fixture from any directory—no repository clone, provider API key, network call, or token spend.

output equivalence: preserved
missing evidence link: claim-duration
reason code: MATERIAL_CLAIM_MISSING_EVIDENCE
classification: new_failure
CI gate: blocked as expected

The demo wrapper exits 0 only when it verifies that the expected regression was caught. The underlying candidate evaluation, comparison, and CI commands remain strict and exit nonzero for the blocking finding.

Actual reviewer output

_{Screenshot of the reviewer-facing evidence-diff.html produced from the same bundled fixture. Open the image to inspect it at full resolution.}

Reviewer-facing artifacts

Key artifacts are written under .tmp/demo/flagship:

flowchart LR
    subgraph OutputCheck["Ordinary visible-output check"]
        BOut["Baseline output<br/>recommendation=approve<br/>outcome=approve"]
        COut["Candidate output<br/>recommendation=approve<br/>outcome=approve"]
        Same["Visible answer unchanged"]
        BOut --> Same
        COut --> Same
    end

    subgraph InvariantCheck["agent-assure invariant check"]
        BEv["Baseline evidence<br/>claim-duration linked"]
        CEv["Candidate evidence<br/>claim-duration missing link"]
        Pass["Baseline evaluation: pass"]
        Fail["Candidate evaluation: fail<br/>MATERIAL_CLAIM_MISSING_EVIDENCE"]
        BEv --> Pass
        CEv --> Fail
    end

    Same --> Tension["Output unchanged<br/>but governance invariant regressed"]
    Equiv["Fixture equivalence: pass"] --> Compare["Baseline-to-candidate comparison"]
    Pass --> Compare
    Fail --> Compare
    Tension --> Compare

    Compare --> NewFailure["Classification: new_failure"]

Where it fits

agent-assure complements the evaluation, observability, runtime-control, and governance systems teams already use.

It is a particularly strong fit when release review must be local, reproducible, CI-enforceable, and traceable without a required hosted control plane.

How it works

Declare → Observe (privacy-filtered) → Evaluate
        → Compare (when equivalent) → Packet → Gate

Declared expectations and canonical run evidence remain distinct. The candidate is evaluated first; equivalent-baseline context is added only after comparison prerequisites pass. The evidence packet then supports a CI signal and human release review.

agent-assure is a local-first Agent Release Assurance Compiler: controls and evidence stay bound to method identity, prerequisites, provenance, assumptions, and limitations.

The assurance model is deliberately bounded:

• Deterministic and reproducible in fixture mode: fixed, versioned fixtures, canonical serialization, schemas, and digest-bound manifests make checks repeatable; that reproducibility does not estimate production prevalence.
• Scoped invariance claims: results cover only declared, observable fields and explicit prerequisites—not hidden reasoning or all production behavior.
• Traceable lineage: expectations connect to RunSets, findings, comparisons, evidence packets, and configured gate state; provenance records participating material.
• Fail-closed: malformed, conflicting, incompatible, ambiguous, or unbound evidence does not silently become a passing review.
• Statistically bounded: live conclusions about probabilistic provider behavior remain tied to a declared statistical protocol, with its data boundary, configuration, window, sampling noise, dependence, and limitations explicit.

Architecture choices and evidence boundaries are documented in architectural decision records (ADRs), including deterministic fixture versus stochastic live semantics.

Challenge the assurance controls

The development-RFC core/v1 mutation catalog runs seven deterministic challenges across evidence linkage, human-review routing, tool boundaries, provenance identity, privacy redaction, duplicate replay, and budget-stop integrity:

agent-assure controls mutate \
  --suite assurance/suite.yaml \
  --runset runs/baseline.json \
  --catalog core/v1 \
  --seed 0 \
  --today 2026-08-02 \
  --full-report \
  --out reports/control-challenge

Every selected operator runs independently against the same immutable source. The output binds the canonical catalog digest, normative expected detector, observed and prohibited substitute findings, exact changed paths, provenance, independence class, seed, and limitations. The campaign itself remains a finite challenge record. controls efficacy can derive an exact catalog-relative detector kill ratio over completed applicable outcomes while preserving inapplicable, invalid, and error counts outside its denominator. That ratio is not a safety score, statistical confidence interval, or universal-coverage claim.

Only deterministic caught and survived campaign outcomes may contribute a control-efficacy verdict. An applicable critical threat with no completed challenge emits CRITICAL_THREAT_UNCOVERED and requires review under the default profile. Required and critical survivors, invalid/error outcomes, and required non-verdict outcomes are always blocking. Efficacy-aware ci gate uses strict verification by default when efficacy evidence is present; pass a verifier-owned controls-mutation YAML with --efficacy-policy. That option also requires the evidence to be present, while --require-efficacy provides a separate presence requirement. Strict CI accepts only complete, all-caught, all-applicable-challenged evidence and pins the catalog, selected and required operators, and threat manifest. Use --allow-advisory-efficacy only for an explicit review flow.

The packaged offline demonstration exercises both a strong and deliberately weakened assurance control, then verifies that an unrelated failure cannot substitute for the expected detector:

agent-assure demo assure-the-assurance \
  --out .tmp/demo/assure-the-assurance \
  --clean

For a minimal editable workflow, start with agent-assure init controls-mutation, run the read-only doctor controls-mutate preflight, then produce a campaign and control-efficacy-report.json.

Inspect the exact seven-operator catalog · Measure control efficacy · Read "Who assures the assurance?" · Review the evidence contracts

Integrate your agent

agent-assure integrates through declared YAML expectations, versioned run evidence, the documented CLI, and the framework-neutral AgentRunRecord producer contract.

The integration contract has three parts:

1. Declare observable process expectations in YAML.
2. Produce versioned run records from fixtures or privacy-filtered observations.
3. Evaluate the candidate, compare equivalent baseline evidence when available, and gate the resulting evidence packet.

A real expectation from the bundled flagship suite:

cases:
  - case_id: shared-source-multi-claim
    fixture_id: shared-source-multi-claim
    expectation:
      expected_recommendation: approve
      required_evidence_refs:
        - ref-shared-clinical-note
      material_claim_ids:
        - claim-eligibility
        - claim-duration

agent-assure does not infer material claims from rationale text. Authors declare the oracle, and run-record producers emit explicit claim-to-evidence links for the material claims they intend to satisfy.

Author expectations · Understand the CLI contract · Review the public API surface · Understand evidence packets

name: agent-assure
on: [pull_request]

jobs:
  assure:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - run: python -m pip install agent-assure==0.6.3
      - uses: acblabs/agent-assure/.github/actions/agent-assure@v0.6.3
        with:
          suite: examples/prior_auth_synthetic/suite.yaml
          baseline-variant: examples/prior_auth_synthetic/variants/baseline.yaml
          candidate-variant: examples/prior_auth_synthetic/variants/candidate_evidence_normalization.yaml
          report-mode: full

Integrations and maturity

Current maturity: Release Candidate (RC, v0.6.3).

The CLI, YAML authoring format, persisted versioned JSON artifacts, and AgentRunRecord producer contract are the primary integration surface. Framework adapters, streaming, and live execution remain experimental. The RC label applies only to the primary surface; development-RFC contracts remain non-stable. PyPI's Development Status :: 4 - Beta is the closest standardized classifier to an RC and does not widen that surface.

Framework adapters project only privacy-filtered agent_assure metadata into the framework-neutral run-record model. They ignore raw prompts, messages, completions, tool arguments, token chunks, and unredacted summaries.

Governance crosswalks

Packet-resident evidence can be mapped to selected concepts in the NIST AI RMF, OWASP Top 10 for LLM Applications 2025, ISO/IEC 42001, and MITRE ATLAS 2026.06.

These crosswalks are planning and review aids. They do not establish framework conformance, complete coverage, third-party assurance, or endorsement.

Claim boundary

Measured evidence, not a blanket trust claim.

This project is not a compliance attestation.

Generated artifacts make declared inputs, findings, limitations, and gate state traceable and auditable for human review. Whether the release decision is defensible remains a human and organizational judgment. agent-assure does not determine safety or replace domain, legal, regulatory, clinical, security, provider-quality, model-quality, or business-impact review.

Pattern redaction is a guardrail, not comprehensive DLP or de-identification. Live conclusions remain bounded by the declared protocol, data boundary, provider/model configuration, and execution window. Review the claim boundary, limitations, threat model, privacy model, and security guidance.

Learn more

• Start: Documentation · For AI leaders · For architects · For engineers
• Demos: Assure the assurance · Flagship · RAG provenance · Expense approval
• Integrations: LangGraph · Google ADK · Adapter contract
• Assurance: What this measures · Control efficacy · Evidence packets · Live calibration
• Evidence-carrying releases: Core mutation catalog · Minimal evidence graph · Contracts and campaign guide · Architecture · CLI contract
• Security and governance: Claim boundary · Threat model · Governance crosswalks
• Project: Contributing · Changelog · License

pip install -e ".[dev]"
git config core.hooksPath .githooks
python scripts/check_docs_alignment.py
ruff check .
mypy src scripts
pytest
python -m build

Citing

This project ships a CITATION.cff. Use GitHub’s Cite this repository control for generated citation formats.