agentverity 0.8.1

Decision stability and coverage checks for AI agent tests

AgentVerity

Decision stability and coverage checks for AI agent tests.

AgentVerity tests agents that choose from a known set of decisions: routers, approval or policy gates, and supervisors that select the next agent or tool. It compares the named decision, exposed as verdict, rather than harmless changes in explanation text.

Use another evaluator for open-ended chat with no reviewed decision or ordered tool-path contract. AgentVerity is alpha, with documented pre-1.0 guarantees.

Try it

pip install agentverity

from agentverity import from_callable, run

def route(ticket: str) -> dict:
    # Deliberate defect: every ticket takes the same route.
    return {"text": "route: general", "verdict": "general"}

agent = from_callable(route)
result = run(agent, inputs=[
    "my card was charged twice",
    "the app crashes on login",
    "where is my refund",
    "the checkout button is the wrong colour",
])

print(result.headline)

NOT TRUSTWORTHY - the agent answered 'general' on 100% of the probes,
so a pass says more about the probe set than about the agent.

Those deliberately varied test inputs form the probe set. AgentVerity asks:

• Decision stability: does one case reach the same decision across isolated reruns?
• Decision coverage: do the cases reach more than one decision?

A green quality score answers a different question: whether the selected answers were right. AgentVerity checks how much evidence that score rests on. It complements DeepEval, promptfoo, AgentCore Evaluations, and ordinary assertions rather than replacing them.

Why rerun counts are harder than they look

Three or five reruns chosen by convention can support the wrong conclusion. In one deterministic example, 36 independent pairs produced no decision changes. That was still too little evidence to certify a change rate below 5%. The honest result was undecided, not unstable. Certifying that threshold with no observed changes needed 73 pairs.

AgentVerity preserves deterministic, stochastic, and undecided, then calculates the repeat budget before the run. The default balanced precision does this automatically.

See the executable helper, arithmetic, and exact API mapping.

The evidence gate

A baseline is a reviewed set of expected decisions for later versions. AgentVerity refuses to save one until calls complete, decisions are stable enough, the probe set crosses a decision boundary, and a person approves the reference outputs.

The bundled payment-dispute example runs two test sets:

python examples/payment_dispute_gate.py

Probe set	Exact-match	Verdict stability	Probe coverage	Baseline
Narrow, 6 duplicate-charge cases	✅ 6/6	✅ verdict-deterministic	❌ blind, 1 route	❌ REFUSED
Repaired, 6 dispute categories	✅ 6/6	✅ verdict-deterministic	✅ 6 routes	✅ ADMITTED

Both score 6/6. The narrow set correctly tests one route, but it cannot justify a system-wide baseline. The repaired set reaches all six routes and can be saved as a versioned snapshot.

Create one through the CLI:

agentverity snapshot \
  --agent mymod:build_agent \
  --inputs seeds.txt \
  --output baseline.json \
  --accept-reference

The same checks run before agentverity check reports differences as regressions. Snapshot files retain SHA-256 input fingerprints rather than raw prompts.

Where it fits

AgentVerity is an evaluation runner, not serving-path middleware. It makes controlled calls with reviewed test inputs:

reviewed cases ---> agent ---> quality evaluator: "Was it right?"
             +---> agent ---> AgentVerity: "Can I trust this test?"
                                      |
                           snapshot or release decision

Use it while developing, on a pull request, before release, or as a scheduled synthetic canary. Do not repeat live customer requests. Results can leave as text, JSON, JUnit XML, or one privacy-minimised OpenTelemetry span.

See CI, telemetry, lifecycle, and multi-agent integration.

Measured AgentCore canary

The optional production example combines a Strands payment router on Amazon Bedrock, DeepEval route-quality checks, AgentVerity, AgentCore Runtime, and CloudWatch.

The London canary recorded 6/6 correct routes, no changes across 36 repeat pairs, all six routes reached, and 78 successful cloud calls with no errors or throttles. Its first run was stable but only 5/6 correct, so the example now requires both quality and evidence before snapshot admission.

This is deployment proof, not an AWS requirement. The zero-dependency callable works with any stack.

Run the production example · Read the measured result

Scope

AgentVerity provides:

• pre-flight stability and decision-coverage checks
• evidence-gated snapshots for reviewed decisions
• JSON, JUnit, and optional OpenTelemetry handoffs
• optional metamorphic relations for controlled input changes

It does not judge answer correctness, store traces, host a dashboard, or monitor production traffic. Static tools remain useful for declared branches, route schemas, and expected labels. AgentVerity measures the decisions a model-backed or black-box target actually returns.

Documentation

• Why arbitrary rerun counts fail
• Integrations and AgentCore validation
• API guide
• Design decisions
• API stability and path to 1.0
• Security and data handling
• Contributing
• Release process

Development

pip install -e ".[dev]"
python -m pytest -q
ruff check .

CI covers Python 3.10 through 3.14, lint, package construction, and the generated README evidence.

Status and licence

Alpha. Pin a minor series for production use, for example agentverity~=0.8.0. Patch releases preserve the public API.

Apache-2.0. Contributions are welcome through the pull-request workflow.