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Use OpenTelemetry for cross-service context and transport, add OpenInference semantics for agent activity, and send the resulting traces through an OpenTelemetry Collector. Attach Kubernetes workload attributes and a stable run or session ID to every span. This lets you follow one request from ingress through an agent worker, model gateway, retrieval system, queue, and tool service, then connect a bad answer or provider timeout to the exact pod and deployment involved.
Kubernetes CPU, memory, and restart metrics show that something is unhealthy; AI-aware spans explain what the agent was doing when it failed. A useful design therefore combines traces for causality, metrics for alerting and capacity, and logs for detailed tool and policy events.
The reference architecture
Model an agent run as one distributed trace rather than as a collection of unrelated application logs. The root span starts at the user-facing ingress or API service. Child spans represent agent planning, model calls, retrieval, embedding generation, tool execution, queue waits, database work, and response generation. Each service keeps the W3C trace context when it forwards an HTTP request, gRPC call, or message.
Telemetry path
Instrument the ingress, agent workers, model gateways, tool services, databases, and queues with OpenTelemetry SDKs or supported auto-instrumentation. Export spans, metrics, and logs using OTLP to an OpenTelemetry Collector in the cluster, or directly to a compatible backend. The Collector is the policy and routing point: it can batch data, retry failed exports, sample traffic, redact fields, enrich resources, and send different signals to different systems.
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Phoenix combines a collector and a user interface for real-time troubleshooting. Its OpenTelemetry and OpenInference integrations add AI-oriented information to otherwise generic spans. A Grafana stack paired with Prometheus and OpenTelemetry provides broad Kubernetes dashboards and alerting. Amazon OpenSearch Service is a managed AWS option that supports end-to-end monitoring and debugging for AI-agent and LLM workflows. These systems can receive the same vendor-neutral OTLP stream rather than forcing every service to use a proprietary SDK.
Context propagation across protocols
Require every boundary to preserve the W3C trace context. For HTTP and gRPC, propagate the standard trace headers through ingress, service meshes, gateways, and internal clients. For queues, put the context in message headers or metadata and extract it when a worker consumes the message. A consumer that starts a fresh trace without linking it to the producer breaks the causal chain and makes queue delay look like unexplained worker time.
Retries need special treatment. Keep the original trace and create a child span for each attempt, recording the attempt number and backoff. If a retry is dispatched as a new asynchronous job, carry the same run or session identifier and add a span link to the producing span. This preserves both the user-visible operation and the individual delivery attempts.
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Enrich spans and metrics with the Kubernetes resources that ran them. At minimum, capture:
- cluster and namespace
- service, workload, and deployment version
- pod, container, and node
- agent name or worker role
Use deployment version or image identity rather than only a pod name, because pods are ephemeral. Resource enrichment allows an operator to start with a deployment alert, open a representative trace, and see whether the failing step was a model call, retrieval request, tool invocation, or queue consumer on a particular rollout.
Stable run, session, and tenant identifiers
Trace IDs identify one distributed operation, but an agent may perform several turns, retries, or background jobs. Add a stable agent.run.id for the logical task and an agent.session.id for the conversation or workflow session. If your system is multi-tenant, add a non-sensitive tenant identifier. Keep these values consistent across retries and asynchronous hand-offs, while retaining the normal parent-child trace relationships for timing analysis.
What to capture
AI observability requires more than HTTP status and pod health. Capture the following fields at the span or event where they occur, then derive service-level metrics from them.
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| Area | Recommended data | Why it matters |
|---|---|---|
| Timing | End-to-end run latency, per-step latency, queue delay, and timeout duration; calculate p50, p95, and p99 in your dashboards | Separates a slow model, a backed-up queue, and a slow tool from an otherwise healthy request path |
| Reliability | HTTP and gRPC status, exception type, timeout count, retry count, and final run outcome | Shows whether failures are caused by your service, a dependency, or recovery logic |
| Model usage | Provider and model name, input and output token counts, throttling responses such as 429, provider 5xx responses, and estimated cost | Connects spend and answer latency to a particular model or provider |
| Tools | Tool name, arguments after redaction, authorization decision, duration, return status, and failure reason | Explains an agent’s actions and identifies unsafe, unavailable, or slow tools |
| Retrieval | Retriever, index or collection, query identifier, number of candidates, selected documents, and a quality or relevance score when available | Distinguishes a bad answer caused by missing context from one caused by generation |
| Agent behavior | Step type, route or plan decision, turn number, termination reason, and final task outcome | Shows why the agent selected a tool, looped, or stopped early |
| Evaluation | Offline evaluation score, human feedback, policy result, or rubric label as trace annotations | Separates infrastructure health from answer quality |
Use traces to establish causality, metrics to trigger alerts and plan capacity, and logs for verbose tool, policy, and audit events. Link an alert or metric sample to representative trace IDs so an operator can jump from a spike in provider errors or deployment latency to an actual run.
Protect prompts, tool data, and personal information
Prompt and tool payloads can contain credentials, personal data, customer records, or proprietary documents. Instrumentation must not become an uncontrolled copy of application data.
- Redact secrets, authorization headers, API keys, and unnecessary personal data before export.
- Prefer identifiers, hashes, document IDs, and bounded previews over complete prompts or tool results when full content is not required for debugging.
- Apply different retention periods to development, staging, and production data.
- Restrict access to traces that contain prompts, retrieved documents, or tool arguments, and audit access to them.
- Define whether model and evaluation annotations may contain customer text before enabling them in production.
Put redaction in the Collector where possible, but also remove sensitive fields in the application so they do not appear in local logs, exception messages, or intermediate exporters.
Choosing a backend
Evaluate a backend on four dimensions: AI semantic depth, OpenTelemetry and Kubernetes interoperability, self-hosted versus managed operation, and evaluation, prompt, and debugging workflows.
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| Phoenix | Teams that need AI-specific trace detail and evaluation workflows | Open-source observability for AI and LLM applications; OpenTelemetry/OpenInference integrations; trace visualization, annotations, evaluations, and framework integrations; can be self-hosted on Kubernetes | Requires operating the service and its storage when self-hosted; infrastructure dashboards may still need a general monitoring system |
| Grafana with Prometheus and OpenTelemetry | Kubernetes fleet health, alerting, and capacity management | Strong dashboards, alerting, Prometheus metrics, and OpenTelemetry compatibility; fits existing cluster operations practices | Generic traces may not expose model, retrieval, or tool semantics without careful instrumentation and dashboard design |
| Amazon OpenSearch Service | AWS teams preferring a managed service | Managed operation and AWS-described end-to-end monitoring, debugging, and optimization for AI-agent and LLM workflows; auto-instrumentation for major frameworks and providers | Cloud-service choice and data residency considerations; operating model differs from a self-hosted backend |
| Combined pattern | Organizations needing both fleet operations and AI debugging | Keep OpenTelemetry/OpenInference vendor-neutral; route infrastructure metrics to Grafana/Prometheus and detailed AI traces or evaluations to an AI-focused backend | More than one system means additional retention, access-control, and ownership decisions |
No single backend has to own every signal. A common arrangement is Grafana and Prometheus for cluster availability, saturation, and alerting, with Phoenix or another AI-focused destination for model, retrieval, tool, and evaluation detail. The Collector can route selected streams while preserving the same trace and run identifiers.
Implementation sequence
- Define naming and attributes. Choose conventions for service names, span names,
agent.run.id,agent.session.id, tenant, model, provider, tool, queue, and Kubernetes resources. Decide which values are low-cardinality metric labels and which belong only on spans or logs. Do not put unbounded prompt text or user IDs into metric labels. - Instrument agent and tool services. Install the OpenTelemetry SDK or supported auto-instrumentation in each service. Add OpenInference instrumentors for the agent framework, model client, retriever, embedding library, and tool framework you actually use. Verify that model invocations, retrieval, embedding generation, tool execution, and routing appear as distinct spans rather than one opaque worker span.
- Instrument asynchronous components. Add propagation to queue producers and consumers. Record enqueue time, dequeue time, delivery attempt, visibility timeout, and dead-letter outcome. A queue-depth metric without a trace link cannot show which run was delayed.
- Deploy the Collector in Kubernetes. Configure receivers for OTLP, processors for batching, retry, sampling, redaction, and resource enrichment, and exporters for your selected backends. Size it for peak span volume, and monitor its own dropped-data, queue, export-error, and memory-pressure metrics.
- Export and validate. Send OTLP to Phoenix, OpenSearch, Grafana-compatible storage, or a combination. Start one test request and confirm that its trace crosses ingress, agent, model gateway, retrieval, tool, database, queue, and response services. Check that every span has the expected cluster, namespace, workload, pod, and version attributes.
- Build service dashboards. Create views for availability, request and step latency, error rate, provider 429 and 5xx responses, queue depth and delay, token usage, estimated cost, tool failures, and Collector health. Show p50, p95, and p99 rather than an average alone.
- Build agent dashboards. Group runs by model, route, tool, retriever, deployment version, and outcome. Add evaluation scores or human feedback as annotations, and expose a drill-down from a chart point to a representative trace.
- Add alerts and exemplars. Alert on sustained latency or error regressions, provider throttling, queue backlog, tool failure rate, and Collector export loss. Include a trace exemplar or trace ID in the notification so the responder can inspect the failed run immediately.
- Run controlled failure drills. Inject a provider timeout, tool 500 response, malformed retrieval result, pod restart, and queue backlog in a non-production environment. Confirm that each drill produces the expected span status, retry and timeout fields, Kubernetes resource attributes, and final run outcome.
Designing useful dashboards and alerts
Fleet and service views
Start with request rate, success rate, latency percentiles, pod restarts, resource saturation, queue depth, and Collector health. Break down failures by namespace, workload, deployment version, dependency, and status class. A deployment-level error alert should link to traces from the affected version, not merely to the dashboard for the whole cluster.
Model gateway views
Track calls and failures by provider and model. Display input and output tokens, estimated cost, latency, timeout rate, 429 rate, and 5xx rate together. This reveals distinct failure modes: throttling may require rate-limit or capacity changes, while rising latency with normal provider status may point to queueing or local gateway contention.
Tool and retrieval views
Rank tools by call volume, p95 duration, failure rate, and authorization denials. For retrieval, track empty-result rate, candidate counts, selected-document counts, and available relevance or evaluation scores. Link a poor score to the run trace so an engineer can inspect the query, retrieved context, model call, and final answer as one sequence.
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Alert thresholds
Use sustained windows and environment-specific baselines rather than a single universal number. Alert separately on user-visible run failures, dependency failures, provider throttling, and data-quality regressions. A healthy pod with a rising empty-retrieval rate is an application-quality incident, not a Kubernetes availability incident.
Failure modes and recovery checks
The trace stops at ingress
Check that the ingress or gateway injects W3C context and that the first internal client forwards it. Service-mesh configuration, custom HTTP clients, and proxy hops commonly replace or drop headers. Capture the incoming trace ID and the outgoing headers in a safe diagnostic environment, then verify that the next service creates a child span rather than a new root.
Asynchronous work appears as a separate request
Inspect queue message metadata. The producer must inject context and the consumer must extract it before starting its processing span. For fan-out or delayed work, retain the run identifier and use a span link when strict parentage is not appropriate.
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Only generic HTTP spans are visible
Install the OpenInference instrumentor for the model, retriever, embedding, or agent framework in use, and check that the instrumentation is initialized before clients are created. Add explicit spans around custom planners and tools that are not covered automatically. Record semantic attributes such as model, tool, operation, and outcome without exporting sensitive payloads.
Traces are too expensive or too large
Reduce payload detail first: redact or hash content, keep bounded previews, and move verbose events to logs with a shorter retention period. Apply tail or head sampling in the Collector according to environment and outcome, while retaining errors, high-latency runs, and selected exemplars. Monitor Collector queue size, memory, export retries, and dropped spans so sampling does not become silent data loss.
An alert has no useful trace
Check metric-to-trace exemplars, clock synchronization, trace retention, and permissions. Make sure the alert carries the same deployment, model, or service dimensions used by the trace search. If a metric is aggregated across many runs, include a representative trace ID rather than pretending that one trace explains every event in the window.
Good infrastructure, bad answers
Inspect retrieval quality, tool results, route decisions, prompt-policy outcomes, and evaluation annotations. Kubernetes health and model latency can be normal while the agent receives empty or irrelevant context. Treat evaluation scores and human feedback as a separate quality signal correlated with, but not substituted for, infrastructure metrics.
Operational ownership and rollout
Assign ownership for instrumentation libraries, Collector configuration, backend retention, dashboard definitions, and redaction policy. Version attribute conventions with application code so a deployment cannot silently rename the fields that dashboards and alerts depend on. Test telemetry in CI or a staging cluster by asserting that representative runs contain required spans and attributes.
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Roll out in stages: first ingress and agent spans, then model and retrieval semantics, then tools, queues, and evaluations. Compare trace volume, export cost, and dropped-data metrics after each stage. Keep a minimal emergency dashboard that remains usable if the AI-focused backend is unavailable; the Collector and Kubernetes metrics should still show whether services are accepting work and exporting telemetry.
What this approach makes possible
With consistent context propagation, one run can be followed across synchronous calls, retries, queues, and multiple agent turns. Kubernetes resource attributes identify the exact workload and version involved. AI-specific spans expose why the agent selected a tool, what retrieval contributed, how many tokens a model consumed, and whether the final task met an evaluation criterion. That combination turns an opaque “the chatbot failed” report into a bounded investigation: identify the affected run, locate the failing step, map it to the responsible pod or dependency, and decide whether the remedy is code, capacity, provider configuration, data quality, or policy.
Frequently Asked Questions
Do I need both OpenTelemetry and OpenInference?
Use OpenTelemetry for distributed context propagation, transport, and backend interoperability. Add OpenInference where available to describe model calls, retrieval, embeddings, tool execution, and agent routing; it complements rather than replaces OpenTelemetry.
Can Kubernetes metrics alone explain an incorrect agent answer?
No. Cluster metrics can show resource pressure or restarts, but they do not record route decisions, retrieved context, tool results, token use, or evaluation outcomes. Those require AI-specific spans and annotations.
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Redact in the application before logging or exporting, and enforce a second policy in the OpenTelemetry Collector. Limit payloads, access, and retention according to environment and data sensitivity.
Should one backend receive every signal?
Not necessarily. Route fleet metrics and alerting to a Grafana/Prometheus system and detailed AI traces or evaluations to Phoenix or another suitable backend, while keeping the same OpenTelemetry and run identifiers.
How do I prove that tracing works across a queue?
Run a controlled job, verify context in the message metadata, and confirm that the consumer span is connected to the producer or linked to it. Then inspect enqueue delay, delivery attempts, worker identity, and the final run outcome in one trace view.
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