Closing the Reliability Loop After an AI Agent Launch

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    Video summary

    Raphael Kalandadze describes why a task that eventually succeeds can still conceal a fragile agent. Retries, accidental recoveries and incorrect intermediate decisions make a simple success counter insufficient. A travel-booking example illustrates how apparently successful behavior can still produce the wrong result.

    Raphael Kalandadze proposes connecting production traces with the relevant code so an investigating agent can identify a plausible root cause and propose a narrow correction. A fresh reviewer or human check should challenge that diagnosis rather than trusting the same agent's account of its own work.

    Raphael Kalandadze distinguishes a fast failure-repair loop from broader session-health analysis. Browser-level tests can expose user-visible problems but are slower and more expensive, so the talk treats them as part of a layered process. The broader goal is a maintainable harness that learns from observed failures without assuming that every patch improves the whole system.

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    Raphael Kalandadze on black beside the blue and white headline After Agent Launch. Framed in blue with WWW.ARTIFICIAL-INTELLIGENCE.VIDEO, 5 July 2026 and duration 19m 33s.

    Raphael Kalandadze argues that shipping an agent is only the beginning: useful reliability work connects observed failures, code-level diagnosis, independent review and subsequent verification.