Definitive answer

    What are the failure modes of enterprise AI agents?

    Enterprise AI agents fail from a small set of recurring causes: poor data and broken permission propagation (~85% of failures), tool sprawl beyond ~10 tools per agent, missing or stale evaluation harnesses, prompt and model drift, weak human-in-the-loop boundaries, and absent telemetry. Each has a known mitigation, and closed-loop instrumentation is the meta-mitigation.

    Failure taxonomy

    Mapping each failure to its mitigation is how you turn agent reliability into an engineering problem.

    • Data quality — fix parsing, ACL propagation, contextual chunk prefixes.
    • Tool sprawl — cap at ~10 tools per agent; decompose into sub-agents.
    • Eval rot — sample production failures into the golden set weekly.
    • Prompt drift — version prompts, run regression suite on every change.
    • Permission leaks — query-time ACL filters, never trust embeddings alone.
    • Cost runaway — token budgets per task, cached retrieval, model routing.
    • Hallucinated tool calls — strict JSON schemas, tool result validation.
    • Weak HITL — explicit approval gates for irreversible actions.

    Frequently asked

    What's the single biggest cause of agent failure?+

    Data — specifically broken ACL propagation and document parsing. The model is rarely the bottleneck.

    How do you catch failures before users do?+

    Trace every run, alert on cost/latency/quality anomalies, sample low-confidence outputs into review, and gate deploys on a golden eval set.

    Related reading

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