Knowledge · AI Capabilities

    AI Failure Recovery and Escalation

    What operational AI should do when an action fails: stop, report accurately, avoid duplicate side effects, and escalate to a named human with context.

    What should AI do when an action fails?

    It should stop, report the failure accurately, avoid repeating any step with external side effects, and escalate to a named person with enough context to finish the work. Silent retries, optimistic success messages and unowned failures are the three behaviours that cause real damage.

    Key takeaways

    • The failure path is a feature, not an exception to design later.
    • Never report success on an unverified write.
    • Retries are safe only for idempotent steps.
    • Escalation needs a named owner and a deadline, not a general alert.
    • Partial completion must be stated explicitly, step by step.

    The three dangerous failure behaviours

    • Optimistic reporting: announcing success because the call was made, not because the record exists.
    • Blind retrying: repeating a step that sends a message or takes a payment, producing duplicates.
    • Orphaned failures: an error logged somewhere nobody reads, with no owner and no deadline.

    Partial completion is the normal case

    Most real failures happen mid-chain: the customer was created, the appointment was booked, the confirmation never sent. Reporting that as a flat failure is as misleading as reporting success.

    The correct output names each step and its state, so the person picking it up knows exactly what remains and does not redo work that already landed.

    When to retry and when to stop

    • Retry reads and idempotent lookups freely.
    • Retry an internal write only where a duplicate cannot be created.
    • Never auto-retry sends, charges or signature requests — re-issue them deliberately instead.
    • Stop after a bounded number of attempts and escalate rather than looping.

    Escalation that actually gets resolved

    An escalation should reach a specific role or person, carry the entity it concerns, state what was attempted and what remains, and have a time by which it is chased again if untouched.

    The same principle applies to work the AI declines: refusal without escalation just moves the failure somewhere quieter.

    Where URBLD fits

    URBLD records the status of each Jarvis step individually — success, blocked or failed with the reason — so partial chains are visible rather than summarized away. Side-effect logging is isolated from the core write, and unresolved follow-through escalates to a named owner on a defined schedule.

    FAQ

    Frequently Asked Questions

    Straight answers about how URBLD runs the business end-to-end.

    More in AI Capabilities

    How to tell a chatbot from an assistant from operational AI.

    Browse AI Capabilities
    Share this page