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Missing AI-agent traces in Grafana are usually caused by one of five stages: the application never created or sampled the spans, an exporter or collector failed to deliver them, Tempo rejected them, storage has not made them queryable yet, or Grafana queried the wrong tenant or backend. Isolate those stages in order rather than changing dashboards first. Start with application instrumentation and sampling, then check collector refusal metrics, prove receipt with Tempo distributor and live-store metrics, investigate discard reasons and limits, check Kafka and block-builder health, and finally validate Grafana’s data-source and query path.
What causes missing or incomplete AI-agent traces in Grafana?
A trace can disappear before it reaches Grafana or appear incomplete because only some spans survived the pipeline. Treat the path as five separate checkpoints:
- Application instrumentation and sampling: the AI framework call, model request, tool invocation, HTTP request, database operation, or other work must create a span, and the sampler must keep it.
- Exporter and collector delivery: the SDK must send using the protocol and port the receiver exposes. An OpenTelemetry Collector or Grafana Alloy must accept, buffer, batch, and forward the data.
- Tempo ingestion: the distributor and receivers may refuse spans because of invalid IDs, rate limits, oversized traces, active-trace exhaustion, or other limits.
- Kafka, live-store, and block storage: recent data can lag while live-store partitions catch up; older data depends on block-builder flushes to object storage.
- Grafana and query execution: the data source, tenant headers, credentials, query-frontend connection, and object-storage permissions must all be correct.
This order matters. A collector cannot forward a span that instrumentation never created, and a correct Tempo query cannot display data that sampling or ingestion limits removed.
How do I confirm that the application creates and exports the spans?
Begin at the process that runs the agent. If it does not create a span for the operation you expect, every downstream component can be healthy while the trace remains absent.
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Check the instrumentation method and coverage
Grafana documents four approaches: auto-instrumentation, zero-code eBPF instrumentation, manual instrumentation, and hybrid instrumentation. OpenTelemetry supplies language-specific SDKs and auto-instrumentation libraries. Verify that the selected approach supports both the AI framework and the surrounding work you want to see. A model call may be instrumented while a tool’s outbound HTTP request or database query is not, producing a trace that looks incomplete rather than entirely missing.
- Confirm that the agent process actually loads the SDK or auto-instrumentation libraries at startup.
- Check that the AI operation creates a parent span and that downstream calls use the same trace context.
- For manual spans, verify that the span is ended on success, error, and timeout paths; an unended span can be absent or incomplete.
- When using eBPF or hybrid coverage, identify which processes and protocols are in scope. Kernel-level capture does not automatically understand every application-level AI framework event.
Verify protocol, port, and endpoint
Match the SDK exporter protocol to the receiver configuration. OTLP over gRPC and OTLP over HTTP use different endpoints and commonly different ports; a protocol mismatch can look like a silent lack of traces if application logs are not monitored. Confirm the complete endpoint, including scheme and path where applicable, and make sure the receiver is reachable from the application’s network namespace.
Docker and Kubernetes frequently cause endpoint errors. Inside a container, localhost refers to that container, not the host or a separate collector pod. Use the service DNS name and exposed receiver port for a separate collector, and verify network policies, service ports, and sidecar configuration. Look at SDK exporter logs for connection refusal, DNS failures, TLS errors, authentication failures, and queue overflows.
Check sampling before assuming data loss
Probabilistic samplers intentionally retain only a fraction of traces. A healthy application can therefore appear empty during a short test, especially when the sampling probability is small. Record the effective sampling decision and rate, then run enough requests to make an expected sample likely. If you need every diagnostic request, temporarily use an appropriate always-on or targeted policy in a non-production-safe way, while accounting for the additional ingestion cost and limits.
Sampling can also create an apparently broken trace when a parent and child are sampled inconsistently. Ensure the SDK and any downstream services honor propagated sampling flags. A collector or Tempo cannot reconstruct spans discarded at the source.
How do I tell whether Grafana Alloy or an OpenTelemetry Collector dropped spans?
Inspect the collector or Alloy that receives application telemetry before checking Tempo. A production pipeline should receive spans, batch them, retry writes, and forward them. Its logs and metrics distinguish a transport problem from an instrumentation problem.
Use exporter and receiver refusal metrics
Monitor exporter_send_failed_spans_ratio_total for failed sends from the collector and receiver_refused_spans_ratio_total for spans rejected on intake. In a healthy pipeline, both should normally remain at zero. A non-zero exporter failure ratio points to the Tempo endpoint, network, TLS, authentication, or downstream availability. A non-zero receiver refusal ratio indicates malformed or over-capacity input before forwarding.
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Check the time window around the missing trace rather than only the current value. A brief outage can leave no current error while explaining a historical gap. Correlate collector retry, queue, batch, and process-restart logs with the application’s request timestamps.
Decide whether direct export or a collector is appropriate
Direct application-to-Tempo export has fewer moving parts, which can simplify a small deployment, but each application must handle batching, retries, and back-pressure correctly. Alloy or an OpenTelemetry Collector provides a central buffer and decouples application restarts from backend interruptions. It still cannot restore spans that were never created or intentionally sampled out, and its own queues can fill during a prolonged outage. Set alerts on failed and refused spans and retain enough logs to identify when a queue was exhausted.
How can I prove that Tempo received the spans?
Once the exporter is healthy, use Tempo’s receipt metrics to establish whether data entered the backend. This is the boundary between delivery and ingestion/query diagnosis.
Check distributor and live-store counters
Enable Tempo distributor debug logging with distributor.log_received_spans.enabled when a short diagnostic window is needed. After the application starts generating traffic, tempo_distributor_spans_received_total and tempo_live_store_traces_created_total should become greater than zero within minutes. Examine receiver-specific traffic with tempo_receiver_accepted_spans.
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If the distributor count stays at zero, focus on the SDK endpoint, protocol, port, network route, authentication, and sampling. If distributor receipt rises but live-store creation does not, investigate the distributor-to-ingester/live-store path, tenant routing, and ingestion errors rather than Grafana’s dashboard.
Use a controlled trace for correlation
Send one request with a distinctive attribute or known trace ID, note its UTC timestamp, and follow that ID through application logs, collector logs, and Tempo debug output. This avoids confusing a delayed trace with a missing one and makes it easier to distinguish multiple tenants or similarly named services.
Which Tempo limits can intentionally remove spans?
Tempo can be reachable and still reject data. Inspect both refusal and discard metrics, then use the discard reason to select the fix.
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Read refusal and discard metrics
Check tempo_receiver_refused_spans and tempo_discarded_spans_total. The discard reason identifies rate limiting, oversized traces, active-trace exhaustion, invalid trace or span IDs, compaction limits, or unexpected errors. For a five-minute view grouped by reason, run:
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sum by (reason) (rate(tempo_discarded_spans_total[5m]))A non-zero rate during the incident confirms that the missing data was removed intentionally by a configured or built-in safeguard, not lost by Grafana rendering.
Interpret the documented defaults carefully
Current Tempo documentation lists a default limit of 10,000 concurrent active traces per tenant per live-store and a default maximum of 5,000,000 bytes per trace. Verify the running Tempo version and tenant configuration before quoting or changing either value; defaults can differ across releases and deployments.
An agent that emits large prompts, tool transcripts, or many parallel child spans can exceed the per-trace byte limit. High concurrency can exhaust active-trace capacity even when individual traces are small. Reducing unnecessary attributes, splitting pathological workflows, adjusting limits with capacity planning, or changing sampling may be safer than simply raising limits.
Turn on the right diagnostic logs
For distributor rate limiting, enable log_discarded_spans. For live-store size or active-trace drops, inspect live-store debug logs. Preserve the reason field and tenant identity when exporting these logs so that one noisy tenant does not obscure another.
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Why are recent searches incomplete when older traces work?
Recent results often depend on live-stores consuming Kafka partitions. If those stores lag, a query that overlaps the lagging range can be incomplete even though older blocks are healthy.
Measure Kafka and live-store lag
Monitor tempo_live_store_lagged_requests_total. A non-zero rate means requested ranges overlap lag. With fail_on_high_lag: true, the documented default, Tempo returns an error instead of silently returning data it cannot guarantee. With it set to false, partial results may be returned. Treat a partial response as a freshness problem, not proof that the spans never arrived.
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Check Kafka consumer health, partition assignments, ingestion throughput, and resource saturation on live-store components. Wait for the affected partitions to catch up, then repeat the same query and time range. If the result changes without new application traffic, the original gap was query freshness rather than data creation.
Separate live data from completed blocks
Run a narrow query for the newest interval and another for a period old enough to be in object-storage blocks. A failure confined to the newest interval points toward Kafka/live-store lag; a failure confined to older data requires block-builder or object-storage investigation.
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Older traces become queryable only after block-builder components consume the stream and flush blocks to object storage. A healthy live-store does not prove that historical blocks were written or can be read.
- Inspect block-builder errors and Kafka consumption progress.
- Check
tempo_block_builder_flushed_blocksfor successful flush activity. - Check
tempo_block_builder_fetch_errors_totalfor failures reading required data. - Verify object-storage credentials and permissions, including the ability to list and get the relevant bucket paths.
- Confirm retention has not legitimately removed the requested time range.
If flush counters stop while Kafka consumption continues, investigate block-builder resource limits, serialization or compaction errors, and object-storage connectivity. If blocks flush successfully but historical queries fail, continue with querier, query-frontend, tenant, and object-storage read checks.
How do I validate Grafana’s Tempo query path?
When ingestion and storage metrics show data, move to the query path. A correct trace can be hidden by a wrong data source URL, tenant, credentials, or network route.
Check Grafana’s data source and tenant context
Verify the Tempo data-source URL, authentication credentials, and any TLS settings in Grafana. In a multi-tenant deployment, confirm that the query includes the expected tenant ID and Authorization headers. A missing or incorrect tenant header can return an empty result that looks identical to absent telemetry.
Run the same trace-ID or time-range query from a controlled client and from Grafana. If the controlled query succeeds but Grafana fails, inspect the data-source configuration, proxy path, and Grafana server logs. If both fail, continue with Tempo query-frontend and querier logs.
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Inspect query-frontend and querier connectivity
Check query-frontend and querier logs for timeouts, rejected requests, and backend errors. The cortex_query_frontend_connected_clients metric helps show whether queriers are connected to the query-frontend. Also verify network reachability between Grafana, the query-frontend, queriers, and object storage. Object-storage LIST and GET permissions are both relevant: listing discovers blocks, while getting reads their contents.
Rule out a query-window illusion
Use an explicit UTC time range that includes the request timestamp and search by the exact trace ID when available. Grafana’s relative ranges, browser time zones, retention boundaries, and service-name filters can hide a valid trace without any backend failure. Remove filters one at a time, then restore them after the trace is found.
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|---|---|---|
| No application export activity and no distributor receipts | Instrumentation, sampling, endpoint, protocol, or network | SDK startup/exporter logs, sampler configuration, receiver URL and port |
| Collector receives spans but exporter failures rise | Collector-to-Tempo delivery | exporter_send_failed_spans_ratio_total, retry and TLS/authentication logs |
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| Recent data works but older ranges fail | Block builder or object storage | tempo_block_builder_flushed_blocks, tempo_block_builder_fetch_errors_total, storage LIST/GET logs |
| Backend metrics are healthy but Grafana is empty | Query URL, tenant, credentials, filters, or query routing | Data-source settings, headers, query-frontend/querier logs, exact trace-ID query |
What is the fastest end-to-end troubleshooting procedure?
Use one controlled request and record timestamps in UTC. Stop at the first stage that fails; later checks cannot repair an earlier gap.
- Trigger a single AI-agent workflow that should create a parent span and at least one child operation. Record its trace ID if the application exposes it.
- Confirm the SDK loaded, the sampler made a keep decision, and the exporter used the intended protocol, endpoint, port, and credentials.
- Check collector or Alloy logs and the exporter/refusal ratios. Resolve queue, retry, TLS, authentication, or receiver errors before moving on.
- Check Tempo distributor receipt, receiver acceptance, and live-store trace-creation counters. Enable short-lived distributor debug logging if counters are ambiguous.
- Query
tempo_discarded_spans_totalby reason and inspect refusal counters. Compare the trace’s size and concurrency with the running version’s limits. - For a recent time range, check live-store lag and Kafka consumption. Repeat the query after lag clears; test the
fail_on_high_lagbehavior rather than treating partial results as definitive. - For older ranges, verify block-builder flushes, fetch errors, retention, and object-storage LIST/GET access.
- Finally, validate Grafana’s data-source URL, credentials, tenant headers, time zone, filters, and query-frontend/querier connectivity with an exact trace-ID query.
How should I choose an architecture for more complete traces?
No architecture guarantees every span. Compare the trade-offs that determine completeness and operational effort.
| Decision axis | Questions to ask | Practical implication |
|---|---|---|
| Instrumentation coverage | Are AI framework calls, tool calls, downstream HTTP, and database operations all instrumented? | Auto, zero-code, manual, and hybrid methods cover different boundaries; verify the actual operations rather than assuming framework support. |
| Delivery reliability | Who batches, retries, and buffers during a Tempo interruption? | Direct export is simpler; Alloy or an OpenTelemetry Collector adds decoupling and buffering but introduces another component to monitor. |
| Completeness versus cost | What sampling rate, trace-size, active-trace, and ingestion-byte limits are acceptable? | Higher retention and larger traces consume more resources and can increase rate limiting or storage pressure. |
| Freshness and correctness | How quickly do live-stores consume Kafka, and what happens under lag? | Monitor lag, understand fail_on_high_lag, and maintain healthy block-builder and object-storage paths. |
| Operational ownership | Who maintains Tempo, collectors, Kafka, and storage? | Self-managed Tempo and Alloy provide control; Grafana Cloud Traces is a managed, Tempo-backed option for teams that prefer the provider to operate the tracing backend. |
How can I prevent missing AI-agent traces?
- Keep a documented map of every instrumentation boundary, exporter protocol, receiver endpoint, tenant, and sampling policy.
- Alert on non-zero collector exporter-failure and receiver-refusal ratios, Tempo receiver refusals, discard rates, live-store lagged requests, and block-builder fetch errors.
- Track distributor receipts and live-store trace creation so an outage is visible before a user reports an empty search.
- Load-test realistic agent workflows, including long prompts, parallel tools, retries, and large attribute payloads, against the active-trace and per-trace-size limits.
- Use a synthetic trace with a known ID and schedule searches across both recent live data and older object-storage blocks.
- Keep diagnostic logging scoped and temporary where it is verbose, especially distributor received-span and discarded-span logs.
- Document retention, tenant routing, object-storage LIST/GET permissions, and the expected behavior when live-store lag is high.
Frequently Asked Questions
Can Grafana show a partial trace even when Tempo is healthy?
Yes. Sampling may keep only some spans, and instrumentation may cover the parent AI operation without covering a tool, HTTP call, or database query. A healthy query proves only that the retained spans are searchable; it does not prove that uninstrumented or sampled-out spans existed.
How long should I wait before concluding that a new trace is missing?
Use the live-store receipt and lag metrics rather than a fixed waiting period. If tempo_live_store_lagged_requests_total is non-zero, the query range overlaps unconsumed Kafka data; retry after the affected partitions catch up or investigate the consumer backlog.
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It usually points to tenant routing or authorization rather than global ingestion. Compare the tenant ID and Authorization headers sent by Grafana with those used by the application and any direct query client, then check per-tenant receipt and discard metrics.
Could object-storage permissions affect only old traces?
Yes. Recent traces can still be served from live-stores while historical queries require block listing and reads. Missing LIST permission can prevent block discovery, and missing GET permission can prevent reading discovered blocks.
Should I raise Tempo limits immediately when spans are discarded?
Not automatically. First identify whether the reason is oversized traces, active-trace exhaustion, rate limiting, invalid identifiers, or compaction. Raising a limit without capacity planning can increase memory, Kafka, and object-storage pressure; reducing excessive attributes or concurrency may solve the underlying problem more safely.
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