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Mibo monitors the behavior users actually experience in production.

Mibo evaluates production traces against the active tests you define, stores asynchronous evidence for investigation, and stays outside the customer request path.

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Uptime can show a healthy endpoint while the agent behaves incorrectly.

A request can finish successfully while an agent routes to the wrong specialist, calls a tool with unsafe arguments, or gives an incomplete answer. Mibo turns the production trace into evidence you can investigate instead of treating availability as proof of reliable behavior.

Production traces

Send a canonical trace with the interaction, response, and execution details after a real user request through OpenTelemetry or Mibo's Your API path.

Asynchronous evidence

Mibo stores the trace and schedules applicable active tests in the background. Passive evaluation does not send another request to your system.

Configured checks

Review semantic behavior and procedural facts such as tool calls, arguments, status, schema, tokens, and response time when your trace includes the needed fields.

A monitoring loop for investigating agent regressions

Keep production evaluation separate from the customer interaction while making the evidence and its limits visible to the team responsible for the agent.

01

Capture the interaction

Your system handles the user request, then sends the canonical trace to Mibo with the input, output, and execution details available to evaluate.

02

Evaluate outside the request path

Mibo stores the trace and evaluates it asynchronously against applicable active tests. The ingestion response does not wait for the evaluation to finish.

03

Investigate a regression

Compare results over time and open the evidence behind a failure. Use the findings to prioritize a fix; Mibo does not automatically remediate the agent.

Follow a regression from behavior to execution evidence

Behavior checks

AI-powered semantic assertions compare the response with criteria you describe, such as being grounded, complete, safe, or aligned. They provide evidence for investigation, not a guarantee of truth or overall quality.

Execution checks

Deterministic assertions inspect recorded facts such as node or tool calls, arguments, attributes, HTTP status, schema, token usage, and response time. Missing fields are reported as missing instrumentation rather than a silent pass or fail.

Frequently asked questions

Why is uptime monitoring not enough for AI agents?

An agent can return a successful HTTP response while choosing the wrong tool, inventing an outcome, or giving an incomplete answer. Mibo evaluates configured behavior and trace evidence, not only whether the endpoint was reachable.

What happens when a passive trace reaches Mibo?

Your system sends a canonical trace after the interaction. Mibo stores it, finds the applicable active tests, and evaluates it asynchronously in the background. It does not call your agent again as part of passive evaluation.

Does passive monitoring add Mibo to the customer request path?

No. Your system handles the customer request first and sends the trace to Mibo separately. The ingestion response is separate from the background evaluation.

Can Mibo guarantee complete coverage or automatically fix regressions?

No. Mibo evaluates the active tests and trace fields available to it. Unobserved edge cases, narrow criteria, unavailable classification, and missing instrumentation can limit the signal, and Mibo does not automatically remediate the agent.

How does Mibo handle trace privacy?

Trace payloads are encrypted at rest, and product access is scoped to the account that owns them. Send synthetic or appropriately governed data that fits your own privacy requirements.

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