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Monitor an AI agent as a traceable workflow, not as a single API response. Give every user request a trace ID, record retrieval, reranking, model, tool, memory, retry, and fallback spans, and attach timestamps, token counts, model versions, cost, errors, and quality scores. Measure retrieval quality separately from answer quality; break latency into queue, search, model, tool, and post-processing time; and attribute cost to each call and retry. Use representative evaluations offline, then score sampled production traffic so regressions become visible before users report them.
This approach answers the three questions a success status cannot: Did the agent find the right evidence? Did the model use it correctly? And what did the complete run consume in time and money?
What should an AI-agent observability trace contain?
A useful trace represents one complete execution from the incoming request to the final response. Intermediate spans make the run diagnosable; without them, a failed answer looks identical whether the cause was an empty index, a bad tool call, a slow provider, or a generation error.
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Create a unique trace ID for each user request. Create child spans for retrieval, reranking, model calls, tool calls, memory reads and writes, retries, parallel branches, fallbacks, and final response assembly. Preserve parent-child relationships so a waterfall view shows both sequence and parallel work.
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At the trace level, store the request, final answer, tenant or user segment, region, deployment version, start and end timestamps, overall status, and links to redacted input and output payloads. At the span level, record the operation name, provider, model and version, prompt or policy version, inputs and outputs (or secure references to them), status, error details, start and end times, and token usage.
Include context that lets you compare runs
- Environment, region, tenant, application and deployment version.
- Retriever, index or knowledge-base version, embedding model, reranker and top-k settings.
- Tool name, arguments, result status, timeout and retry count.
- Prompt-template version, model parameters and routing decision.
- Input, output, cached-input and reasoning-token counts when the provider exposes them.
- Sampling or redaction policy applied to sensitive content.
Keep provider pricing metadata with the usage record. Rates change, so storing only a precomputed dollar value makes historical reports impossible to recalculate. Save the currency, rate-card version or effective date, and the fields used in the calculation.
How do I monitor search quality separately from answer quality?
Instrument retrieval and generation as two related evaluations. A successful endpoint response proves only that the workflow completed; it does not prove that the search returned useful evidence or that the answer was grounded in it.
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For each search span, log the user query after approved normalization, retriever name, candidate document IDs, rank, relevance score, selected chunks, source metadata, index version, and retrieval latency. If your system reranks, retain both the initial and reranked positions so a ranking change can be investigated.
Useful retrieval signals include:
- Hit/miss ratio: the share of questions for which at least one relevant item appears in the candidate or selected set. Define “relevant” with a labeled set rather than assuming the top result is correct.
- Grounding relevance: whether the returned passages actually support the expected answer, not merely whether they contain matching words.
- Evidence coverage: how many required facts or subquestions have supporting passages.
- Stale-source rate: the share of retrieved items outside your freshness policy, when source dates are available.
- Retrieval latency: elapsed time for each search and rerank operation, including queueing and network waits.
Store the evidence IDs that were passed to the model. This lets reviewers distinguish “the right passage was retrieved but ignored” from “the passage never entered the prompt.”
Score the final answer independently
Evaluate the response against the request and the approved evidence. Track correctness, factuality, grounding, citation validity, completeness, helpfulness, refusal or fallback behavior, and explicit user feedback. A response can cite a real document yet make a claim that the document does not support, so citation presence alone is not a grounding score.
Use deterministic checks wherever possible: required fields in a JSON response, citation format, prohibited-content rules, arithmetic checks, and tool-result consistency. Use human review or an LLM judge for semantic questions such as whether the answer reconciles conflicting sources. Keep the judge prompt and rubric versioned; changing the judge changes the meaning of historical scores.
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Maintain a versioned evaluation set
Start with representative questions covering common intents, long and short queries, ambiguous wording, multilingual or regional variants that you serve, and known edge cases. Attach expected evidence and an acceptable answer rubric. Every important production failure should become a new case after sensitive data is removed.
Run the set whenever you change a prompt, retriever, reranker, model, tool policy, or routing rule. Compare both retrieval and answer scores, because an overall score can hide a trade-off in which search improved while generation regressed.
Which metrics reveal whether retrieval, tools, or generation caused a bad answer?
Use a failure matrix that joins the final quality score to intermediate evidence. The combinations below point to different fixes.
| Observed pattern | Likely cause | Next check |
|---|---|---|
| Retrieval miss, answer confidently wrong | Index coverage, query rewriting, filters, embedding or reranker failure | Inspect candidate IDs, rank scores, index version and freshness |
| Relevant evidence retrieved, answer omits it | Prompt assembly, context truncation, model instruction conflict or attention limits | Compare selected chunks with the exact model input and token budget |
| Tool error or timeout, graceful fallback absent | Tool contract, authentication, network, timeout or retry policy | Inspect tool arguments, response status, retry count and fallback span |
| Evidence and tool outputs correct, answer still unsupported | Generation, model routing or prompt-policy problem | Compare model/version, temperature or other parameters and judge reasons |
| All quality signals stable, users report failure | Rubric gap, unobserved intent, UX issue or latency tolerance | Review feedback text and add the intent to the evaluation set |
This diagnosis is why trace-based monitoring is more useful than server dashboards alone. AWS describes the principle as monitoring that is “trace-based, not server-based,” while Databricks calls tracing the foundation for agent observability and quality.
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Report both end-to-end latency and a per-step breakdown. Averages hide tail behavior and can conceal a small group of very slow, expensive runs, so publish percentile summaries such as p50 and p95; use p99 when the tail materially affects users or contractual objectives.
Break the request into timing segments
- Queueing and admission: time waiting for a worker, rate-limit slot or concurrency permit.
- Retrieval and reranking: query transformation, vector or keyword search, filtering and rerank time.
- Model time: provider queue, time to first token and generation duration for every model call.
- Tool and subagent work: network time, execution time, parallel branches and nested agents.
- Retries and fallbacks: each attempt gets its own span; do not hide failed attempts inside one duration.
- Post-processing: validation, citation formatting, safety checks and response streaming to the client.
The sum of spans will not always equal wall-clock duration when branches run in parallel. Record parent duration and child overlap so a waterfall can explain the difference.
Use the correct streaming measures
| Metric | Definition | Why it matters |
|---|---|---|
| TTFT | Time to first token after the request is accepted | Indicates how quickly the interface begins responding |
| TTFA | Time to first answer content that is meaningful to the user | Separates an early status token from useful output |
| TTLT | Time to last token and complete response | Captures total wait for a finished answer |
| End-to-end latency | Request arrival to final response delivery | Includes queueing, search, tools, retries and post-processing |
Google Cloud’s agent views document TTFT, TTFA and TTLT and summarize latency with p50 and p95. Apply the same definitions consistently across providers; otherwise a “faster” model may simply be measured from a different start point.
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Segment latency before setting alerts
Break dashboards and alerts down by model, provider, tool, geography, tenant, query type, workflow, and deployment version. A global p95 can remain healthy while one region or one retrieval index degrades. Set service-level objectives from your own baseline and user tolerance; the published guidance defines metrics but does not establish universal latency thresholds.
How do I calculate token cost per agent run?
Calculate cost at the span level, then roll it up to the request, session, user, workflow and model. Keep input and output tokens separate and include cached-input, reasoning or other billable categories when the provider reports them.
Use a recomputable cost formula
For each model call, calculate:
call_cost = (input_tokens × input_rate) + (output_tokens × output_rate) + (cached_tokens × cached_rate) + (reasoning_tokens × reasoning_rate) + provider_compute_or_tool_charges
Then sum every call in the trace, including retries, fallbacks, parallel branches and subagent calls:
trace_cost = Σ call_cost + Σ tool_charges + Σ applicable platform_compute
If a provider bundles a category or exposes no separate rate, record the usage as “not separately stated” and retain the provider’s billing field rather than inventing an allocation. Recompute historical totals when rates change by using the rate metadata stored with each span.
Find the sources of cost growth
- Prompt growth caused by larger retrieval contexts or accumulated memory.
- Repeated tool calls, failed retries and fallback models.
- Model-routing changes that send more traffic to a higher-priced tier.
- Long outputs or hidden reasoning usage where reported.
- Subagents or parallel branches that multiply model calls.
Dashboard cost per request and per successful outcome, not only aggregate spend. Pair high-cost traces with quality and latency so an optimization does not remove evidence or increase retries. AWS recommends inference cost per user or session and total input/output tokens; OpenAI’s guidance emphasizes counting root and subagent work, retries, tool charges and applicable compute.
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How do I instrument an agent step by step?
Implement observability in this order so each later metric has the context it needs.
- Define the trace contract. Specify required IDs, timestamps, status values, model and prompt versions, token fields, cost metadata, tenant and deployment context, and redaction rules. Make the contract part of code review.
- Capture the root request and response. Store secure references or redacted payloads, the user-visible status, and the final quality or feedback fields.
- Wrap every intermediate operation. Add spans around retrieval, reranking, model calls, tools, memory, retries, fallbacks and post-processing. Record errors on the span where they occur.
- Persist evidence lineage. Keep candidate and selected document IDs, ranks, relevance scores, source dates and the exact context sent to the model, subject to privacy controls.
- Record usage and timing. Capture start and end timestamps, queue waits, TTFT, TTFA, TTLT, token categories and provider request IDs when available.
- Attach scores. Run deterministic validators immediately when safe; enqueue semantic judges or human review asynchronously. Store scorer and rubric versions.
- Build dashboards and alerts. Show quality, latency percentiles, token and cost trends, error rates, retries and fallback rates. Segment by the dimensions that can change behavior.
- Test the instrumentation. Create synthetic traces for retrieval misses, tool timeouts, retries, streaming responses and parallel branches. Verify that redaction works and that totals reconcile with provider usage reports.
How do I turn production failures into continuous improvement?
Use a closed evaluation loop rather than treating monitoring as a passive dashboard.
- Capture a failed or low-confidence production trace and its user feedback.
- Remove personal data and preserve the relevant request, evidence, tool outputs, timings and model versions.
- Add the case to a versioned dataset with an expected evidence set and scoring rubric.
- Choose a code-based scorer for deterministic requirements or an LLM judge or human review for semantic quality.
- Change one workflow component at a time when possible: prompt, retriever, reranker, tool policy, model or routing.
- Run the offline set and compare retrieval, answer, latency and cost deltas.
- Release with a controlled sample, score live traffic, and watch for regressions by segment.
LangSmith documents online judge and code evaluations, trajectory monitoring, cost tracking and alerts. Databricks describes the same progression of feedback, datasets, scorers, evaluation and production monitoring. The important design choice is to keep offline and online scores tied to the same trace fields and rubric versions.
Which observability platform fits an AI-agent workload?
Choose by operational fit rather than by a single feature. Confirm current pricing, retention, regional availability, privacy terms and integrations before adoption because these details change.
| Option | Documented strengths | Best fit | Verify before purchase |
|---|---|---|---|
| LangSmith | Framework SDK coverage, trace search, cost tracking, online evaluations, trajectory monitoring and alerting | Teams that want an application-focused agent workflow view | Pricing, retention and partner terms |
| Grafana Cloud Agent Observability | OpenTelemetry-oriented traces, generation/tool/workflow capture, token and cost analytics, online guards and offline experiments | Organizations already operating Grafana and OpenTelemetry | Cloud region, retention and commercial terms |
| Google Cloud agent observability | Managed telemetry using OpenTelemetry GenAI conventions, trace-derived metrics, and Gemini Enterprise views for TTFT, TTFA, TTLT, p50 and p95 | Workloads already running on Google Cloud | Supported services, data location and retention |
| AWS observability for Bedrock, Lambda and Step Functions | Prescriptive metrics for retrieval relevance, hallucination and fallback, token cost, retries and timeouts | AWS-native serverless agent workflows | Service coverage, export options and pricing |
| MLflow and Databricks tracing | Traces connected to feedback, datasets, scorers, evaluations and production monitoring | Teams using MLflow or Unity Catalog for model operations | Workspace governance, retention and deployment model |
Whichever platform you choose, require exportable traces or an OpenTelemetry-compatible path. Your trace contract should remain portable so changing a dashboard vendor does not erase the history needed for regression analysis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should the first monitoring dashboard show?
Start with a small set that connects user outcomes to causes, then add detail as failure patterns emerge.
- Quality: retrieval hit/miss ratio, grounding relevance, evidence coverage, answer correctness, citation validity, refusal or fallback rate and user feedback.
- Latency: end-to-end p50 and p95, TTFT, TTFA, TTLT, retrieval and rerank duration, tool duration, model duration, retry time and queueing.
- Reliability: error rate by span type, timeout rate, retry count, fallback count and incomplete traces.
- Cost: input, output, cached and reasoning tokens; cost per trace, session, user, model and workflow; and cost per successful outcome.
- Change context: model, prompt, retriever, index, tool, region, tenant and deployment versions.
Alert on changes relative to a workload-specific baseline, not on a universal number. Investigate an alert by opening the trace, locating the first abnormal span, and checking whether the quality score confirms a user-visible impact.
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What privacy and retention controls are necessary?
Trace payloads can contain the same secrets and personal data as production requests. Redact or tokenize identifiers before export, never record credentials or raw authorization headers, and separate searchable metadata from sensitive content. Define who can view prompts, retrieved documents and tool results, and set retention by data class rather than keeping everything indefinitely.
When payload capture is too risky, retain hashes, document IDs, token counts, timings and encrypted references that an approved reviewer can retrieve. Test redaction with adversarial examples, and ensure that retries and error logs follow the same policy as successful responses.
What thresholds should I set for quality, latency, and cost?
There is no universal quality, latency or cost threshold in the published guidance. Establish targets from your workload: measure a stable baseline, define what users consider acceptable, and set separate objectives for common and high-complexity workflows.
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For quality, require minimum retrieval and grounding performance on the golden set and monitor drift in production. For latency, set objectives for end-to-end response and for TTFA or TTFT when streaming affects perceived responsiveness. For cost, budget per request or successful task and alert on token or retry anomalies. Revisit targets after major model, index, traffic or pricing changes.
Frequently Asked Questions
How much of an AI-agent trace should I sample?
Capture metadata for every request when feasible, then sample or redact payloads according to risk and cost. Keep all traces that contain errors, retries, fallbacks, low confidence, negative feedback or unusual token and latency values so rare failures are not lost.
Should quality judges run during the user request?
Usually no. Run cheap deterministic checks inline when they protect safety or schema validity, and send semantic judges or human review to an asynchronous queue. This avoids adding judge latency and model cost to every response while preserving a scored production sample.
How can I compare two model versions fairly?
Replay the same versioned evaluation set with identical retrieval context, tool permissions and scoring rubrics, then compare quality, p50/p95 latency, streaming measures and trace cost. A model that improves answer scores but requires more retries or context tokens may have a different operational trade-off.
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What do I do when provider token counts do not match my estimate?
Keep both values: your preflight estimate and the provider-reported usage on the model span. Investigate tokenizer differences, hidden system prompts, cached tokens, reasoning categories and retries, and use the provider value for billing reconciliation when it is authoritative.
Can OpenTelemetry carry agent-specific data?
Yes, use OpenTelemetry traces and add consistent GenAI and workflow attributes for model calls, retrieval, tools, tokens, status and cost. Keep your application’s trace contract documented so attributes remain comparable when spans are exported to different backends.
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