An LLM agent can return a fluent answer, finish a workflow, and leave clean infrastructure logs while still doing the wrong thing. It may choose the wrong tool, hand off at the wrong moment, ignore an instruction, or report success without reaching the requested outcome. Detecting these failures takes more than request-status monitoring: trace the whole workflow, inspect real runs, and turn failures into repeatable evaluations.
What does a silent agent failure look like?
A successful request is not necessarily a successful task. Conventional service monitoring can show that a request completed, but that alone does not establish that the agent reached the right result or followed the right process.
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In a multi-step workflow, a locally plausible decision can derail the end result. The agent might call an unsuitable tool, pass control to another agent or system at the wrong point, mishandle an intermediate result, violate an instruction or safety constraint, or describe an incomplete task as finished. OpenAI’s agent-evaluation guidance uses tool selection, handoffs, instruction adherence, and safety-policy adherence as concrete questions for reviewing a trace. Anthropic’s January 9, 2026 engineering article, “Demystifying evals for AI agents,” explains why these workflows are harder to evaluate than a single-turn exchange: they can call tools, change state, and adapt to intermediate results.
There is no supported cross-deployment statistic in the cited guidance for how often agents fail silently, or for which failure type is most common. Treat the examples as failure modes to check in your own workflow, not as a ranking of industry-wide risks.
What should a production trace capture?
A trace should let an operator reconstruct the run from the incoming request through the outcome, rather than showing only the final model response. OpenAI’s Agents SDK documentation describes tracing generations, tool calls, handoffs, guardrails, and custom events; its Agents API tracing documentation describes recorded inputs, outputs, duration, and status. Anthropic calls the full record of an agent trial—including outputs, tool calls, intermediate results, and other interactions—a transcript or trajectory.
For each workflow, decide which events are meaningful and preserve enough structured context to follow their order. A useful trace may include:
- The workflow or trace identifier, so related activity can be examined as one run.
- Model activity and relevant inputs and outputs.
- Tool calls, their arguments and results, and whether a failure or recovery occurred.
- Handoffs and other control-flow changes.
- Guardrail events and relevant custom events.
- Event status and duration, plus usage data where available and appropriate.
Capture only data that is appropriate under your privacy and retention rules. The OpenAI Agents SDK documentation states that tracing is unavailable to organizations using OpenAI APIs under a Zero Data Retention policy. Check the applicable provider and organization settings before relying on a trace pipeline.
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- Instrument the full workflow. Configure traces or structured events to cover model calls, tools, handoffs, guardrails, and meaningful custom events. Confirm that an operator can follow the event sequence and inspect the inputs, outputs, status, and duration that matter to the workflow.
- Review representative runs. Inspect normal runs as well as incidents. Follow the sequence to locate where behavior went wrong: tool choice, tool result handling, a handoff, instruction handling, or a later step. Anthropic reports that its team invested in transcript viewing and regularly reads transcripts; the practical point is that a trace is useful only if people can examine it.
- Define task-specific success checks. Write down what a correct outcome means for the actual task. Check whether the requested state was reached and whether the agent reported that state accurately. The sources support workflow-level grading, but do not prescribe a universal outcome metric that fits every agent.
- Turn incidents into evaluation cases. Keep a representative input and specify the expected behavior. Grade the run against criteria such as correct tool selection, appropriate handoff, instruction adherence, and task outcome. OpenAI’s “Evaluate agent workflows” guidance describes trace grading for finding workflow-level issues and graders for detecting regressions and failure modes at scale.
- Re-run evaluations after changes. Evaluate before and after changes to prompts, models, routing, tools, or workflow logic. Anthropic warns that without evaluations, teams can become reactive: fixing one production issue may create another.
- Sample production behavior. Where appropriate, evaluate a sample of live interactions over time. OpenAI’s cookbook example on evaluating agents with Langfuse describes online evaluation; LangChain describes online evaluation and trace analysis as ways to identify usage patterns, agent behavior, and failure modes.
Which signals should you monitor?
Use operational signals to explain a run, not as substitutes for evidence that the task succeeded. OpenAI’s Agents API documentation describes trace status, duration, and recorded data; its observability guidance also covers events and usage. Pair those signals with checks tied to the workflow’s actual risks.
- Outcome: Did the task reach a verifiable end state, and did the agent describe that state accurately?
- Tool behavior: Was the right tool chosen, were its arguments valid, was its result handled correctly, and did the workflow recover appropriately from a tool failure?
- Control flow: Did a handoff occur when it should, and at the right point?
- Instructions and policy: Did the run follow the applicable instructions and safety constraints? Use human review when criteria are ambiguous or the consequences are significant.
- Execution context: What were the event sequence, status, duration, usage, and relevant inputs and outputs?
- Change over time: Are evaluation results shifting, and do the traces show a meaningful change in behavior?
There are no authoritative universal thresholds in the cited sources for agent latency, tool-error rate, quality-score movement, or acceptable regression rate. Set alert thresholds from your service objectives, baseline measurements on the actual workflow, user impact, and risk; do not treat a generic number as a standard.
How should you choose tracing and evaluation tools?
There are two broad approaches: use tracing and evaluation features integrated with a provider or agent framework, or add a separate observability and evaluation platform. The sources establish examples, not endorsements or a complete market survey: OpenAI documents built-in Agents SDK tracing and evaluation tools; LangSmith describes tracing and online evaluation; Anthropic identifies Arize Phoenix as an open-source tracing, debugging, and evaluation platform; and an OpenAI cookbook example uses Langfuse.
Compare candidates against the workflow and the operating constraints, not just a feature list:
- Workflow coverage: Can it capture all relevant model calls, tool calls, handoffs, guardrails, and custom events?
- Debugging detail: Can an operator inspect event order, inputs and outputs, intermediate results, status, and duration?
- Evaluation loop: Can representative runs become examples, graders, offline evaluations, or online checks?
- Integration and export: Does it work with the SDK and agent framework you use, and can traces move into the monitoring stack you need?
- Privacy and retention: Do trace contents, access, and retention fit your policies? In particular, account for the documented Zero Data Retention restriction on OpenAI Agents SDK tracing.
What to do when a production run looks wrong
- Find the trace for the affected workflow and read the event sequence, not just the final answer.
- Identify the first point where observed behavior diverged from the expected behavior: a tool choice, argument, result, handoff, instruction, or outcome.
- Record the input and expected behavior as a representative evaluation case, avoiding sensitive data that should not be retained.
- Run the case against the relevant workflow version and compare results after a proposed change.
- Review related sampled runs to see whether the issue is isolated or appears in other cases; use the evidence to adjust monitoring or evaluation criteria.
This process separates a one-off trace diagnosis from a repeatable check. Traces explain what happened in an individual run; evaluations help establish whether the behavior is acceptable across examples and whether changes have introduced regressions.
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