How to detect silent agent failures in production

the short answer

Send complete production sessions to Failproof AI, including tool calls and results. Add code-based evaluations for exact failures and LLM-based evaluations for semantic failures such as unsupported success claims. Then run audits across the session population to discover recurring patterns outside those known checks. Review each finding, assign the fix, alert on recurrence and keep the evaluations running to verify recovery.

Known failures: Code-based and LLM-based evaluations.
Unknown patterns: Cloud audits across production sessions.
Evidence: Full traces linked to evaluations and findings.
Response: Assign, alert, fix and verify; add a policy only for suitable actions.

1. Know What a Silent Failure Looks Like

A silent failure is a session that completed without an infrastructure error but did not accomplish the task. The model returned successfully, the process stayed up and every API call may have returned 200. The agent still skipped a required step, stopped after a tool failure, looped, used the wrong data or claimed success without evidence.

Failure	Evidence in the Failproof trace
Repeated action	The same tool and arguments appear several times without progress.
Abandoned recovery	A tool fails and no useful recovery or escalation follows.
Skipped step	A required tool call or verification event never appears.
False completion	The final response claims success while tool results show failure or no state change.
Behavior drift	A failure rate, cost or escalation pattern changes across many sessions.

2. Capture the Full Agent Trace

Instrument the agent so Failproof AI receives the model messages, tool calls, tool results and session outcome. A final answer alone cannot show whether the agent used the right tool, ignored an error or invented a completion claim.

Use stable agent IDs and environment values. This lets queries, dashboards, evaluations and audits distinguish one agent or deployment from another. Confirm that the expected events appear in session replay before building checks on top of them.

3. Add Evaluations for Failures You Already Know

Use code-based evaluations for exact conditions visible in the trace: repeated calls, tool errors, missing required actions, excessive steps or sessions that end without a completion event. Use LLM-based evaluations when the trace needs interpretation, such as whether the final answer is supported by the tool results.

Test every evaluation against real sessions before deploying it. Keep each result linked to concise trace evidence and version the evaluation when its scope or criterion changes. Failproof AI can also run your existing evaluation suite in the cloud.

fp evals --since 24h --aggregate
fp evals --agent-id checkout-agent --aggregate
fp --json evals --aggregate --env production

4. Run Audits to Discover Failures You Did Not Define

Known checks only catch behavior somebody anticipated. Create a Failproof Cloud audit to analyze production sessions for a goal such as abandoned tasks, ineffective retries, unsupported completion claims or behavior changes after a deployment. Use an agent contract to state the purpose, expected outputs, definition of done and prohibited behavior.

Failproof AI combines traces, evaluation results and policy decisions across the population. It groups related failures into findings with severity, affected sessions, evidence queries and a recommended fix. This is where a scattered set of individually plausible sessions becomes a visible production pattern.

fp audits list
fp audits findings --status open --limit 20

5. Query and Dashboard the Behavior Over Time

Use Analyze → Queries to compare failure rates by agent, environment and time window. Save queries for repeated calls, tool errors, incomplete sessions, user retries, escalations and the evaluations that matter to the product. Build dashboards from the saved queries so the team can see whether a pattern is growing or confined to one cohort.

fp agent ask "Which production checkout sessions claimed success after a failed tool result in the last 7 days? Group them by agent version."
fp query run <saved-query-id>

6. Assign the Finding, Fix It and Verify Recovery

Review the sessions behind each finding, assign the issue to the team that owns the agent and fix the prompt, tool, model, data or workflow. Alerts can notify that owner by email, Slack, webhook or dashboard when an evaluation, metric, query or event condition fires again.

Keep the evaluation, query and audit running after the change. The failure is fixed when new sessions improve and the recurring finding stops returning. When the finding identifies a recognizable high-risk action in a supported harness, Failproof AI can draft a policy, backtest it against historical calls and deploy it in observe mode before enforcement.

Failproof AI already provides the complete traces, cloud evaluations, automatic recurring-failure discovery, evidence-backed findings, native queries, dashboards, alerts and ownership workflow. Your team validates the finding and changes the agent. You do not need to build separate detectors and clustering jobs before production failures become actionable.

FAQ

What is a silent failure in an AI agent?

It is a session that completes without an exception but fails the user's task. Examples include skipped steps, ineffective retries, wrong tool use and claiming success without supporting tool results.

How does Failproof AI detect silent failures?

Code-based and LLM-based evaluations detect known failure conditions. Cloud audits analyze evaluation results and trace evidence across sessions to discover recurring patterns, group affected sessions into findings and recommend what to fix.

Do I have to define every failure in advance?

No. Defined evaluations remain useful for known requirements, while Failproof audits look across production sessions for recurring failure patterns outside those initial checks. Reviewers validate the findings before acting.

Can Failproof AI prevent a silent failure?

Most findings lead to a prompt, tool, model, data or workflow fix. If a confirmed pattern is a recognizable high-risk action in a supported harness, a tested behavioral policy can steer or block that action at runtime.

Get Started

Failproof AI is free to start. It finds recurring failure modes across agent sessions using code-based and LLM-based evaluations, groups the evidence into findings, and recommends fixes. Bring the eval suite you already have, alert the right owner when behavior drifts, and turn a tested fix into a policy that prevents the failure from recurring. See pricing for the tiers.

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Sources

Checked against each vendor's own site and docs on 2026-09-14. Products change; if a detail here is out of date, tell us at support@befailproof.ai.