Wrong route
Check that a request reaches the right specialist and workflow, not only that the endpoint responds.
Mibo gives engineering teams a repeatable way to run scenarios before release, inspect the evidence behind each result, and evaluate real production traces with the same tests.
An agent can return a successful HTTP response while choosing the wrong tool, inventing a completed action, or leaving a customer request unresolved. Mibo turns those expectations into evidence you can review.
Check that a request reaches the right specialist and workflow, not only that the endpoint responds.
Verify calls, arguments, and other observable facts in the agent trace.
Evaluate whether the response is clear, safe, and aligned with the outcome your customer needs.
Define expectations once, then use them to check changes before release and inspect real interactions afterward.
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Describe the expected outcome with semantic and procedural checks.
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Run selected scenarios against the connected agent and review the response with its execution evidence.
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Send traces through OpenTelemetry or Mibo's API for asynchronous evaluation after each interaction.
Evaluate meaning, safety, completeness, and alignment with the outcome you described instead of requiring a fixed string.
Inspect execution facts such as tool calls, arguments, attributes, HTTP status, and schema.
Mibo evaluates the behavior you describe, including the response and observable execution evidence such as routes, tool calls, values, and status. Rule-based checks verify facts, while AI-powered checks score responses against plain-language criteria.
Yes. Active testing sends chosen scenarios to a connected agent. After real interactions, your system can send traces for passive evaluation against applicable active test cases.
No. Your system handles the interaction, then sends a trace to Mibo. Mibo evaluates the trace asynchronously in the background.
Learn how semantic and procedural checks make agent behavior testable before release.
Explore evaluation →Evaluate the behavior users experience in production through real traces.
Explore monitoring →Turn agent expectations into concrete scenarios with semantic and procedural assertions you can run and inspect.
Explore AI agent test cases →Test safety-related behavior before release and inspect bounded evidence from real interactions.
Explore AI agent safety testing →Connect defined scenarios to real traffic and identify conditional tests that sit idle or match nothing.
Explore test coverage →Run the canonical live reliability check with a synthetic customer request.
Run a live check →