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An LLM agent can finish a request and still fail the task: it may choose the wrong tool, hand off at the wrong time, ignore an instruction, or report success without producing a usable result. A successful response or clean infrastructure log does not prove the workflow worked. Detecting these failures takes end-to-end traces, task-specific evaluations, and production monitoring that considers quality alongside status, duration, and usage.
What counts as a silent agent failure?
A silent failure is a run that looks successful at the request or infrastructure level but does not meet the user’s actual goal. The model may return fluent text, and every API call may complete, while an intermediate decision has already broken the workflow.
For example, an agent might call a plausible but incorrect tool, pass invalid arguments, hand work to another agent unnecessarily, or misunderstand an intermediate result. It might then produce a confident final answer that misstates what happened. OpenAI’s agent-evaluation guidance uses concrete diagnostic questions such as whether the agent selected the right tool, handed off at the right time, and followed instructions or safety policies.
This is harder to assess than a single-turn answer because agent workflows can involve multiple model turns, tool calls, state changes, and decisions based on intermediate results. Anthropic’s Demystifying evals for AI agents, published January 9, 2026, describes this complexity and the value of evaluating the workflow rather than just its final response.
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Why ordinary monitoring can miss the problem
Request success is not task success
An HTTP success, completed job, or non-empty model response shows that some part of the system ran. It does not establish that the requested outcome occurred. A task may require a verifiable state change, a correctly selected tool, or a response that accurately reflects what the tools returned.
Final text hides the path taken
The final answer may not reveal an earlier tool error, a mistaken handoff, or an instruction violation. Without the sequence of events and relevant inputs and outputs, operators may see the symptom but not the decision that caused it.
Operational health and behavioral quality are different signals
Status, duration, and usage are useful for understanding execution, but they cannot independently grade correctness. A fast, inexpensive run can still be wrong; an unusual duration can be worth investigating without proving a quality failure. OpenAI’s observability and tracing documentation treats execution details as trace context, while its agent-evaluation guidance addresses workflow behavior.
Build a detection loop around complete traces
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Instrument the entire workflow
Record a trace or equivalent structured event sequence that covers model activity, tool calls, handoffs, guardrail events, and meaningful custom events. Where permitted, retain relevant inputs and outputs, event order, status, and duration. OpenAI’s Agents SDK documentation describes traces that include generations, tool calls, handoffs, guardrails, and custom events; its Agents API tracing documentation describes recorded inputs, outputs, duration, and status.
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Inspect representative runs
When a user report, alert, or quality check reveals a problem, follow the trace from beginning to end. Identify whether the cause was an incorrect tool choice, invalid tool arguments, an unexpected handoff, instruction handling, a failed intermediate action, or an inaccurate final report. Anthropic calls the full record of an agent trial—including outputs, tool calls, intermediate results, and other interactions—a trajectory or transcript, and describes transcript review as a practical debugging method.
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Convert incidents into evaluation cases
Save a representative input, the relevant workflow context, and a clear description of acceptable behavior. Turn that example into a repeatable check rather than relying on someone to remember the incident. OpenAI distinguishes trace grading, which helps surface workflow-level issues, from graders used to find regressions and failure modes across examples.
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Evaluate before and after changes
Run the evaluation set when prompts, models, routing, tools, or workflow logic change. Compare results against the prior baseline and inspect failures, not only the aggregate score. Anthropic warns that without evaluations, teams can fall into reactive production-fix cycles in which correcting one issue creates others.
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Sample production behavior
Where appropriate, review a selected sample of live interactions and apply quality checks continuously. OpenAI’s cookbook example on evaluating agents with Langfuse describes online evaluation. Keep cost, latency, and usage associated with the same workflow or trace where the system supports it, so operational context can be examined alongside outcome quality.
What to evaluate in each run
Build checks from the actual success condition of the workflow. The following are useful dimensions, not universal metrics or automatic alert thresholds:
- Outcome: Did the task reach a verifiable successful state, and did the agent describe that state accurately?
- Tool behavior: Did it choose the appropriate tool, provide valid arguments, use the result correctly, and recover appropriately if the tool failed?
- Handoffs and control flow: Did a handoff occur when needed, and at an appropriate point in the workflow?
- Instruction and policy adherence: Did the agent follow the applicable instructions and safety constraints? Use human review for ambiguous or high-impact cases.
- Execution context: What were the event sequence, status, duration, usage, and relevant inputs and outputs? These details help explain a result but do not replace a quality judgment.
- Change over time: Are evaluation results shifting, and do trace patterns suggest a change in usage, agent behavior, or failure modes? LangChain describes online evaluation and trace analysis for identifying such patterns.
There is no evidence here for a universal acceptable latency, tool-error rate, quality-score change, or regression threshold. Set alert levels using the workflow’s measured baseline, service objectives, risk, and likely user impact.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose tracing and evaluation tools by workflow fit
Some teams start with tracing integrated into their model provider or agent framework; others use a separate observability and evaluation platform. Compare options against the system you actually run, not a feature checklist in isolation.
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- Debugging detail: Can an operator inspect event order, inputs and outputs, intermediate results, status, and duration?
- Evaluation loop: Can useful examples become datasets, graders, offline evaluations, or online checks?
- Integration and export: Does it work with the SDK and framework in use, and can traces be exported to the monitoring stack the team needs?
- Privacy and retention: Are trace contents and retention compatible with the organization’s data policy?
Examples documented in the cited materials include OpenAI’s Agents SDK tracing and evaluation tools, LangSmith tracing and online evaluation, Arize Phoenix—which Anthropic identifies as an open-source tracing, debugging, and evaluation platform—and Langfuse, which appears in an OpenAI cookbook evaluation example. These are examples, not a complete market survey or an endorsement.
Check data-retention constraints before enabling traces
Traces may contain prompts, tool arguments, outputs, or other sensitive workflow data. Decide what may be recorded, who can access it, and how long it can be retained before enabling production capture. OpenAI’s Agents SDK documentation says tracing is unavailable to organizations using OpenAI APIs under a Zero Data Retention policy. Confirm that constraint against the organization’s policy and the current documentation before choosing an instrumentation path.
Turn the checks into an operational policy
For each agent workflow, define its verifiable success condition, required trace events, evaluation examples, and the person or process responsible for investigating a quality change. Keep operational alerts and quality evaluation connected but distinct: an execution alert can identify a slow or failed run, while a grader or review process can detect a run that completed but did the wrong thing.
When a failure is found, preserve the trace and turn the relevant behavior into a regression case. This gives the team a way to test whether a fix addresses the incident without quietly damaging another part of the workflow.
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