Why AI Agents Fail in Production

The most common production incident is mundane: a CRM API changes a field, a credential expires, a rate limit is hit at month-end volume, and the agent — which worked in the demo — starts failing or, worse, silently skipping steps. Every integration needs contract tests, monitoring, and alerting exactly like any production dependency.

Related class: partial completion. The agent sends the email but fails to log it in the CRM. Design for this with transactional checkpoints and reconciliation jobs that compare intended vs. actual state.

Each step in a chain has an error rate, and chain reliability multiplies. Ten steps at 95% is a coin flip. The countermeasures: keep autonomous chains short, validate structured outputs between steps, and place human checkpoints at the points where errors become expensive.

Watch especially for plausible-but-wrong intermediate results — the wrong customer record matched, the wrong policy cited. These pass format checks and fail on content, so spot-check content, not just schemas.

Agents granted broad permissions "temporarily" during the pilot keep them in production. The review interface that was "coming next sprint" never arrives. Six months later an automated action surprises someone and the postmortem finds no approval gate, no audit log, and no clear owner.

Prevent this structurally: permissions scoped per workflow and per environment, immutable audit logs of every action, and a named owner accountable for the agent's metrics and behavior. If nobody owns it in production, it is not ready for production.

Model providers update behavior; your other systems evolve; the real world drifts from your test set. An agent measured at launch and never re-measured is an unmeasured system. Re-run your scenario library on a schedule and on every model or prompt change, and monitor live metrics — completion rate, override rate, cost per run — for trend breaks.

The teams that operate agents well treat them like employees with a probation period that never quite ends: supervision, periodic review, and a clear path to revocation when performance drops.