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An AI agent can return a polished answer, finish with a 200 status, and keep latency, error-rate, cost, and token dashboards within normal ranges while still failing the user’s task. It may choose the wrong tool, act on stale context, omit a required handoff, or claim a side effect that never happened. Reliable production monitoring therefore has to follow the agent’s whole trajectory and verify the real-world outcome, not just confirm that a model request completed.
Why an agent can look healthy and still be wrong
Conventional service monitoring is strongest when request paths and expected outputs are bounded. An API call either returns an accepted response or an error; a queue either drains or backs up. Agents introduce a different measurement problem: they select actions at runtime, may take several steps, and often produce plausible language even when an intermediate decision is incorrect.
A dashboard can show normal latency while an agent calls a search tool instead of a billing tool. Error rates can remain at zero when the tool call is syntactically valid but uses the wrong account. Token usage can be exactly on budget while the agent answers from missing or stale context. Even a final message such as “Your flight has been booked” is only a statement in a transcript until the reservation system confirms that a booking exists.
The word “most” in the headline is an editorial warning, not a measured failure-prevalence statistic. No broadly representative rate for silent agent failures has been established in the cited guidance. The practical conclusion is narrower and actionable: service-health signals are necessary, but they cannot establish task success on their own.
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Monitoring, observability and evaluation answer different questions
Teams often use these terms interchangeably, which creates gaps. Treat them as three complementary layers.
| Capability | Question it answers | Typical evidence |
|---|---|---|
| Monitoring | Is the running system inside its operational limits? | Latency, availability, error rate, cost, token volume, queue depth and alert thresholds |
| Observability | What happened during this particular run? | Correlated traces containing model turns, tool calls, handoffs, guardrails, retrieved context and custom workflow events |
| Evaluation | Was the behavior good, safe, efficient and successful? | Graders, assertions, outcome checks and repeatable tests over single steps, traces or conversations |
A healthy monitor does not make an unobservable run understandable, and a detailed trace does not automatically prove that the task was completed correctly. Evaluation supplies the decision rule. Keep all three layers so an incident can be both diagnosed and judged.
What a useful agent trace should contain
Record one trace for every meaningful agent task, not merely every underlying model request. A trace should let an engineer reconstruct the run from the user’s intent to the final side effect.
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- Assign a run ID and connect it to the conversation, user or tenant, request ID and deployment version.
- Record model, prompt or policy version, tool registry version and relevant feature flags so a regression can be tied to a change.
- Preserve parent-child relationships between a conversation, agent run, model turns and tool spans.
Decision and action events
- Capture each model turn, selected tool, arguments after validation, tool result, retry and fallback.
- Represent handoffs between specialist agents and the guardrails that allowed, blocked or modified an action.
- Log retrieval queries and document identifiers when context is supplied by search or a knowledge base; store content only when policy permits.
- Emit custom workflow events for approvals, database writes, payment attempts, form submissions and other business steps.
Timing and resource data
- Measure duration for the whole run and each span, including time waiting on external systems.
- Keep token counts, model-call cost and tool-call counts next to quality evidence. A cheaper run is not better if it fails the task.
- Record termination reason: completed, blocked by policy, timed out, cancelled, escalated or failed an assertion.
OpenAI’s tracing documentation describes traces that include model calls, tool calls, guardrails and handoffs. OpenTelemetry’s emerging GenAI agent conventions aim to give those events common span semantics, but the conventions currently carry “Development” status. Keep your schema versioned and make assumptions visible because field names and recommended attributes may change.
Verify the outcome instead of trusting the transcript
The strongest success signal is a postcondition in the system where the task matters. Define that postcondition before you write an alert or grader.
Examples of postconditions
- Booking: query the reservation system for a confirmed record with the expected traveller, route and date.
- Order or payment: verify a transaction ID, final status and amount in the authoritative ledger.
- Form submission: confirm the target system accepted the submission and returned a durable receipt.
- Data change: read the affected record back and check required fields, permissions and version.
- Generated artifact: test that the file exists, opens, meets format requirements and is attached to the correct case.
If an external system cannot provide a trustworthy check, mark the run as unverified rather than successful. The user-facing response can say that a request was submitted and is awaiting confirmation. This avoids converting an agent’s assertion into a false operational fact.
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Choose the evaluation level that matches the failure
OpenAI’s evaluation guidance and LangChain’s evaluation material describe a progression from isolated decisions to complete conversations. Use the smallest level that can expose the failure, then add broader tests when context or side effects span multiple steps.
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| Evaluation level | Best for | Example checks |
|---|---|---|
| Single run or step | One decision in isolation | Correct tool selection, valid arguments, policy compliance, citation or schema validity |
| Full trace | Workflows whose result depends on several actions | Context was carried forward, retries were safe, required handoff occurred, and the final side effect matches the request |
| Multi-turn thread | Goals that depend on retained conversation context | The agent asks for missing information, remembers a correction, and reaches the intended outcome without contradicting earlier turns |
Anthropic’s engineering article “Demystifying evals for AI agents,” published January 9, 2026, defines an evaluation as “a test for an AI system: give an AI an input, then apply grading logic to its output to measure success.” For agents, “output” should include observable actions and environmental state, not only text.
Build a production monitoring and evaluation loop
- Write the task contract. State the user goal, allowed tools, required approvals, prohibited actions and measurable postcondition. If success cannot be observed, document the limitation and use an explicit “unverified” state.
- Instrument the complete trajectory. Create a root run span and nested events for model turns, retrieval, tools, handoffs, guardrails and custom business steps. Ensure asynchronous workers preserve the same correlation ID.
- Separate operational and quality fields. Put latency, errors, cost and tokens beside tool-choice, argument-validity, grounding and outcome fields. Do not let a technical success flag overwrite a task-quality result.
- Add deterministic assertions first. Check schemas, required fields, permission decisions, destination identifiers and postconditions with code wherever possible. Reserve model-based grading for qualities that cannot be checked reliably with rules.
- Create targeted graders. Build separate graders for tool selection, argument correctness, instruction or policy adherence, groundedness, required handoff, side-effect success and overall completion. A single “good response” score hides which control failed.
- Sample representative traces. Include ordinary successful runs, escalations, retries, ambiguous requests, missing context, stale records and tool outages. Stratify by workflow, model version, tenant and risk level rather than sampling only the busiest path.
- Turn incidents into a dataset. When a production trace reveals a quiet failure, redact it as needed, add the expected outcome and grader rationale, and store it as a repeatable evaluation case. Run the dataset after prompt, routing, tool, model or guardrail changes.
- Alert on quality and outcome signals. Set thresholds for rising unverified completions, wrong-tool calls, failed postconditions, policy violations and escalation loops, in addition to latency and error alerts.
- Review and tune. Investigate false positives, grader disagreement and sampling bias. Retire cases that no longer represent the product, and add new cases when workflows or tools change.
Failure modes that ordinary dashboards miss
Wrong tool, valid request
The selected tool accepts the arguments and returns a normal response, so infrastructure metrics remain green. A tool-choice grader can compare the selected operation with the task contract before any side effect occurs.
Missing or stale context
An agent may answer confidently from an incomplete retrieval result or an old record. Trace retrieval spans, document timestamps and identifiers, then grade whether required evidence was present and current for the decision.
Silent partial completion
The agent updates one system but fails to notify another, or creates a draft instead of submitting it. Model-call success is irrelevant unless every required postcondition is checked.
False confirmation
The final text claims success after a timeout, rejected transaction or ambiguous tool response. Require a durable receipt or authoritative read-back before allowing a success message.
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Correct answer, unsafe process
An agent can produce the right final text after bypassing an approval, exposing restricted context or using an unapproved integration. Grade policy adherence and guardrail events independently from answer quality.
Conversational drift
In a long thread, the agent may forget a constraint or apply a correction only to one turn. Multi-turn evaluations should test retained requirements and the final conversation-level goal.
Use operational signals and quality signals together
Plot technical and behavioral measures on the same release dashboard. A deployment can be technically healthy but behaviorally degraded when wrong-tool or failed-postcondition rates rise. The reverse also occurs: outcomes remain correct while latency, token use or cost becomes unacceptable.
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|---|---|---|
| Reliability | Availability, timeout rate, retry count and tool error rate | That the chosen action was appropriate or the user’s goal was met |
| Efficiency | Latency by span, token use, model cost and number of tool calls | That a fast or inexpensive run was correct |
| Behavior | Tool-choice accuracy, argument validity, grounding, policy compliance and handoff rate | That an external side effect actually persisted |
| Outcome | Postcondition pass rate, confirmed receipts, artifact tests and human-verified completion | Why a failure occurred without the underlying trace |
Data handling, privacy and retention
Detailed traces can contain prompts, responses, retrieved records, audio, credentials accidentally supplied by a user and personal data. OpenTelemetry guidance recommends opt-in content capture because payloads increase privacy risk and storage size. Design the trace so metadata remains useful when content is omitted.
- Capture content selectively by workflow and risk tier; redact secrets and direct identifiers before export.
- Keep immutable event metadata separate from payload storage so access to debugging content can be more restricted.
- Define retention periods, deletion procedures, tenant isolation and audit logs before enabling production capture.
- Use access controls that distinguish operators who need timing data from reviewers allowed to read prompts or documents.
- Test sampling rules against rare, high-severity failures; a low overall sample rate must not discard every risky run.
The OpenAI Agents SDK documents a sensitive-data capture control and notes that tracing is unavailable for organizations operating under its Zero Data Retention policy. Confirm the current behavior and policy terms for the exact SDK and account configuration you deploy; do not assume that a disabled trace can be recovered from ordinary service logs.
How to compare an observability architecture
Whether you assemble internal instrumentation, use a hosted platform such as LangSmith, export to OpenTelemetry, or combine these approaches, compare capabilities rather than brand names.
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| Axis | Questions to ask |
|---|---|
| Trace depth | Does it show the end-to-end run, tool activity, handoffs, guardrails, retrieval and custom workflow events? |
| Evaluation support | Can it grade individual steps, full traces and multi-turn outcomes, then rerun those checks against a dataset? |
| Outcome verification | Can it call assertions or read authoritative state, rather than scoring only transcript text? |
| Interoperability | Can traces be exported through OpenTelemetry or another supported path and correlated with existing systems? |
| Data handling | What content is captured, how is it redacted, where is it stored, and can retention and access be controlled? |
| Operational overhead | How will sampling, export, storage, alert maintenance and grader updates fit the team’s workflow? |
OpenTelemetry is a vendor-neutral instrumentation route, but its agent conventions are still developing. OpenAI’s tracing and evaluation tools document one integrated approach. LangChain describes trace analysis, monitoring, online evaluation, cost tracking, alerting and OpenTelemetry integration for its ecosystem. Product details change, so verify current implementation, retention and deployment terms before standardizing on any option.
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A concrete runbook for a booking agent
Suppose an agent must find a flight, obtain approval and book it. A useful trace and evaluation plan looks like this:
- Record the requested route, dates, traveller identifier and approval requirement as the task contract.
- Trace search calls and grade whether filters match the request, including timezone and cabin constraints.
- Before booking, record the selected itinerary and approval event; block the tool if approval is missing.
- Capture the booking request, provider response and returned confirmation identifier.
- Read the reservation back from the authoritative system and compare traveller, route, dates and status.
- Allow the final message to claim “booked” only when the read-back passes. Otherwise report submitted, pending or failed with the next action.
- Add any mismatch, timeout or false confirmation to the regression dataset and rerun it after changes.
This runbook catches failures that a green model-call status cannot: a valid but wrong itinerary, a skipped approval, a provider timeout followed by a false confirmation, or a reservation that was never persisted.
What good alerting looks like
Alert on changes in behavior, not just infrastructure. Examples include a sudden increase in runs ending without a postcondition check, a rise in wrong-tool selections for one model version, repeated handoffs for a single intent, or a growing gap between “success” messages and confirmed side effects. Pair each alert with a trace link, the failed assertion and the last authoritative state observed so responders can act without replaying the entire conversation.
Keep thresholds tied to workflow risk. A low-risk drafting assistant may tolerate human review of a sample, while a payment or account-change agent may require deterministic blocking whenever verification is absent. The same metric can therefore have different alert and release criteria in different products.
The practical standard for declaring success
Declare an agent run successful only when three conditions align: the trajectory followed the permitted process, the response satisfies the task’s quality criteria, and the intended external outcome is verified. If any condition is unknown, record the uncertainty explicitly and route the run for retry, human review or follow-up.
That standard turns observability from a collection of model logs into an operational control system. Monitoring tells you when the service is running; traces show what it did; evaluations and postconditions determine whether it actually helped.
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