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Agents7 October 2026·6 min read

A catalogue of the ways agents fail in production

Knowing the common failure patterns lets you design against them before launch rather than discover them in front of customers.

Agents fail in recurring, recognizable ways. A short catalogue turns surprises into checklist items.

Common failure modes

  • Looping — repeating a failing action without changing approach. Cap steps and detect repeats.
  • Tool misuse — wrong tool or malformed arguments. Validate inputs and return useful errors.
  • Context loss — dropping an earlier constraint in a long task. Restate key constraints at each stage.
  • Overreach — doing more than asked. Narrow permissions and require approval for side effects.
  • False completion — reporting success without verifying. Check results against the real system.
  • Silent degradation — quality drops after a model or data change. Keep a standing test set and sample live output.

For each failure mode, decide the control (limit, validation, approval, monitor) and the owner before launch.

Related capability

This is what our Autonomous AI Agents practice is built around.

See how it's scoped