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DialogAssert documentation

DialogAssert is the independent acceptance layer for AI agents. The business states what an agent must and must not do; DialogAssert runs those controls against the agent you actually deploy, verifies what happened using the evidence your environment already produces, reports what it could not verify, compares the result with the approved baseline, and produces a defensible release decision.

  1. Install the runner.
  2. Connect the agent and your trace source.
  3. Run discovery to see what this environment can prove.
  4. Write a journey and its acceptance criteria.
  5. Run it, repeated, against the real agent.
  6. Read the report.

The quickstart does all of this against a local sample agent, with no API key and nothing to connect.

Object What it is
Control set An approved, versioned set of controls with an owner and an approval record.
Journey The multi-turn conversation those controls are exercised through.
Evidence What was observed, and how strongly — claimed, observed, dependency-confirmed or independently verified.
Finding A place where the observed behaviour and the approved expectation disagree.
Inconclusive A control that could not be evaluated here. An assurance gap, not a failure.
Baseline The last approved release, and the thing a candidate is compared against.

The system under test may teach DialogAssert how to observe it. It may never teach DialogAssert what “correct” means.

Discovery supplies identifiers — that “block card” is currently a tool named block_debit_card. Only an approved control set can say that blocking requires confirmation. Generated checks that use observed values absent from the business requirement are rejected.