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Control agent-telemetry spend at the point where data is created and processed: remove low-value events and attributes, aggregate metrics that do not need request-level detail, and sample traces according to diagnostic value. Treat retention as a separate policy decision. The right retention period depends on which investigations must remain possible, data sensitivity, backend pricing, query performance, and contractual or regulatory obligations; there is no universally correct number of days for prompts, tool calls, outputs, or traces.
Start with the questions your telemetry must answer
High-volume agent systems can emit a trace span for every model call, retrieval operation, tool invocation, retry, guardrail check, and user-visible response. They may also attach large attributes such as prompts, tool arguments, and generated output. Keeping every event at full detail is expensive, but deleting indiscriminately can make an incident impossible to reconstruct.
Write down the operational questions before changing collection. Examples include:
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- Where is latency accumulating: model inference, retrieval, a tool, or retries?
- How often do policy violations, timeouts, and malformed tool calls occur?
- Can an operator correlate a user request with its agent steps without reading the raw content?
- What evidence is required for security, reliability, customer-support, or regulatory investigations?
These questions define the minimum useful signal. Cost controls should remove data that does not help answer them, not simply target an arbitrary percentage of telemetry.
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Find the actual cost driver before changing collection
Inventory logs, metrics, and traces separately. For each signal, record the producing service, events or points per request, average serialized size, attributes, collector processing, network transfer, backend ingestion units, indexing or query charges, and retention tier. An agent workload can be inexpensive in one backend and costly in another because providers count different units.
For example, the documented CloudWatch OpenTelemetry metrics model charges per gigabyte of ingested data, includes 15 months of storage, offers free console PromQL queries, and charges programmatic PromQL queries per million samples scanned. The page does not state a numeric rate for those scanned samples. It also says billing is based on ingested data volume rather than unique-series count. High-cardinality labels still increase the serialized bytes in each point, so “not billed by series” does not make excessive labels free. These terms apply to that AWS offering and can change; verify current pricing before relying on them.
Measure a representative peak period rather than averaging away bursts. A useful baseline includes:
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- events or spans per request and requests per second;
- bytes per event after serialization;
- the largest attributes and their frequency;
- metric series count and samples per series;
- collector CPU, memory, queue depth, and dropped-data counters;
- backend ingestion, indexing, storage, and query charges; and
- the percentage of incident investigations that require raw content.
Sampling and aggregation have costs of their own. OpenTelemetry cautions about direct compute cost, engineering effort to maintain sampling methods, and the opportunity cost of missing important evidence. Include those costs in the baseline.
Remove unnecessary data at the source and collector
Separate operational metadata from content
An agent trace can usually identify a request, model or model family, tool name, start and end times, status, latency, retry count, deployment, and correlation identifiers without retaining the complete prompt, tool arguments, or output forever. Whether content is necessary depends on your own data classification and obligations; the available guidance does not establish agent-specific privacy or legal rules.
Drop, truncate, hash, or redact content only after confirming that the transformed value still supports the required investigation. Never place raw prompts or tool arguments in metric labels. They create large payloads and effectively unbounded cardinality.
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Filter low-value events
Debug logs, successful repetitive heartbeats, duplicate retries, and verbose intermediate events are common volume sources. Filter them before export when their loss does not break an alert or investigation. Keep a small, deliberate set of fields that explains why an operation succeeded or failed.
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Apply controls early, but preserve needed context
The OpenTelemetry Kubernetes blueprint recommends processing at the level where data can be filtered or aggregated early enough to avoid unnecessary network transfer and downstream compute. SDK or node-level processing can prevent a large payload from leaving the workload; a gateway can enforce consistent rules across services. Early filtering is cheaper, but it cannot use context that has not yet been produced. A completed-trace decision, for example, must occur after the relevant spans exist.
Use central guardrails to make filtering, transformation, redaction, and volume limits consistent. Local exceptions should be explicit, reviewed, and observable so one service cannot silently bypass organization-wide data-handling rules.
Reduce metrics with temporal and spatial reaggregation
Metrics often provide the best cost reduction without losing broad operational visibility. The OpenTelemetry Metrics data model describes two transformations:
| Transformation | What changes | What you gain | What you give up |
|---|---|---|---|
| Temporal reaggregation | Combine high-frequency measurements into longer intervals | Fewer samples, less ingestion and storage, lower processing load | Fine-grained spikes may disappear; alerts become less precise in time |
| Spatial reaggregation | Remove unwanted attributes and combine the remaining measurements | Fewer series and smaller points; lower cardinality | You can no longer break down results by removed dimensions |
Choose dimensions that support decisions
Useful dimensions for an agent service may include service, environment, model family, outcome class, and tool name. Per-request identifiers, full URLs with unique parameters, user text, tool arguments, and generated content generally create excessive cardinality or payload size. This dimension list is a practical starting point, not a universal schema; validate it against the questions operators actually ask.
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Bound cardinality deliberately
Set allowlists for dimensions, reject unexpectedly long label values, and monitor the number of active series. The CloudWatch OpenTelemetry metrics guidance notes that an OTLP point’s serialized size includes the metric name, labels, resource attributes, timestamp, and value; it recommends shorter label values and dropping high-cardinality labels that are not queried. A label that is rarely used but repeated on every point can cost more than its apparent information value.
Keep high resolution where it changes an action
Retain short intervals for latency, saturation, or error signals that drive rapid alerting. Reaggregate longer-lived capacity, usage, and trend metrics when minute-by-minute detail does not change a decision. Preserve raw exemplars or traces separately when a metric spike must be investigated, rather than retaining every metric point at maximum resolution indefinitely.
Choose head or tail sampling for traces
OpenTelemetry defines sampling as reducing the number of trace samples collected and sent to a backend. The specification’s wording is: “Sampling is a mechanism to control the noise and overhead introduced by OpenTelemetry by reducing the number of samples of traces collected and sent to the backend.” Sampling can occur at different collection stages.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute| Approach | Decision timing | Strengths | Limitations and requirements |
|---|---|---|---|
| Head sampling | At the start of a trace, before all spans are known | Simple, efficient, and inexpensive; a deterministic trace-ID decision can keep complete traces at a consistent rate; protects the pipeline early | Cannot know whether a trace will fail or become slow, so it may discard the most valuable evidence |
| Tail sampling | After all or most spans for a trace are available | Can retain errors, high-latency traces, selected attributes, or deployment conditions | Stateful; requires memory, compute, buffering, coordination, and operational monitoring |
Use head sampling when predictability matters
Head sampling is appropriate when traffic is extremely high, a statistically consistent baseline is sufficient, or the pipeline needs immediate protection. Deterministic trace-ID sampling helps all services make a consistent keep-or-drop decision for a trace. It still cannot guarantee that every error trace is retained because the decision precedes the completed trace.
Use tail sampling when diagnostic value is uneven
Tail policies can retain traces with an error status, latency above a threshold, a selected tool or deployment, or a specific outcome. They are more selective, but the collector must hold spans until it can decide. Size memory and queues for bursts, watch decision latency and dropped spans, and test failure behavior. Large systems often need tail sampling to balance volume and usefulness; very high-volume systems may combine a small initial head sample with later tail decisions to protect the pipeline.
Do not assume a percentage is optimal
A fixed rate is a policy input, not a proven answer. Compare the backend savings with collector resources, engineering complexity, and the number of incidents for which retained traces are insufficient. During a deployment or incident, temporarily increasing retention for the affected service can be more useful than permanently storing every normal request, provided the change is controlled and recorded.
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Decide where filtering and aggregation belong
| Placement | Avoids | Benefits | Trade-offs |
|---|---|---|---|
| SDK or workload | Payload creation, network transfer, and downstream processing | Lowest volume and fastest protection of sensitive data | Rules are distributed; changing them may require redeployments; context may be incomplete |
| Node or gateway collector | Cross-network transfer and some backend processing | Centralized implementation with useful service or cluster context | Collector CPU, memory, and queue capacity become part of the design |
| Backend | Little or none of the ingestion cost | Convenient rule changes and access to complete context | Data has already consumed network, collector, and ingestion resources; sensitive data may already be stored |
Use layered controls: remove clearly unnecessary content at the source, enforce organization-wide limits in collectors, and reserve backend queries for analysis rather than routine filtering. Keep counters for dropped events, redactions, aggregation changes, and sampling decisions so operators can tell whether a blank dashboard means “no activity” or “data was discarded.”
Build a retention policy by signal and sensitivity
Retention is not the same decision as collection. You may collect a trace for troubleshooting, retain only its operational metadata in the hot backend, and move an unsampled or less-filtered copy to a lower-cost store only when policy and law permit it. Conversely, a metric can be retained for trends while raw prompts are deleted quickly.
| Tier | Typical purpose | Keep in mind |
|---|---|---|
| Hot, queryable telemetry | Active alerting, on-call diagnosis, and current release analysis | Fast queries and convenient correlation cost more; apply strict content and cardinality limits |
| Lower-cost or unsampled evidence | Rare investigations, audits, or post-incident analysis | Access controls, encryption, exportability, retrieval time, and whether unsampled data is allowed |
| Aggregated history | Capacity planning, reliability trends, and service-level reporting | Longer retention is useful only if aggregation preserves the required dimensions and time resolution |
For each signal, document the purpose, owner, data classification, retention duration, deletion mechanism, access roles, legal or contractual basis, and restore or export procedure. Review prompts, tool arguments, outputs, identifiers, and security-related fields separately; they may have different sensitivity and obligations even when they share a trace.
No generally correct retention duration has been established for agent telemetry. A defensible period is the shortest one that supports the investigations and obligations you have identified, with an explicit exception process for an active incident or legal hold. Reassess it when the backend, workload, geography, contract, or data classification changes.
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The OpenTelemetry telemetry-policy proposal illustrates policies for dropping debug logs, sampling traces, controlling metric rates, filtering attributes, and redacting attributes. It specifies a fail-open runtime requirement: “Implementations MUST be fail-open:” and says a parse or evaluation failure must not cause telemetry loss. This is a proposal on the project’s main branch, not a guarantee that every SDK or Collector implements those features. Confirm the version and support level of the components you deploy before depending on them.
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A staged implementation workflow
- Inventory. Separate logs, metrics, and traces; measure rates, bytes, attributes, series, collector resources, backend charges, and peak bursts.
- Locate the billable unit. Determine whether the backend charges for bytes, events, series, indexed fields, query scans, storage, or a combination. Use the actual invoice and service documentation.
- Define required evidence. List alerts, dashboards, support investigations, security reviews, and compliance cases that must remain answerable.
- Classify data. Mark prompts, arguments, outputs, identifiers, and operational metadata by sensitivity. Remove or redact data without a documented use.
- Bound metrics. Allowlist dimensions, remove content and request IDs from labels, and apply temporal or spatial reaggregation where resolution is not actionable.
- Set trace priorities. Start with deterministic head sampling for baseline protection, or add tail policies when errors and slow traces are disproportionately valuable. Size stateful components and queues.
- Place processing early. Filter and transform before network transfer where context permits; use central collectors for consistent guardrails and exceptions.
- Choose retention tiers. Keep a hot, useful operational subset and a lower-cost tier only when access, security, export, and legal requirements are satisfied.
- Validate failure paths. Generate errors, latency spikes, retries, policy failures, collector restarts, and traffic bursts. Confirm that alerts still fire and that an investigator can correlate the retained evidence.
- Review continuously. Compare cost, dropped-data counters, cardinality, query latency, and investigation outcomes after releases and workload changes. Adjust rules instead of assuming the original policy remains optimal.
Common failure modes and corrections
“Keep everything and delete it later”
Backend deletion does not recover network transfer, collector processing, ingestion, indexing, or exposure of sensitive content. Filter and redact unnecessary data before it leaves the workload.
“Use one low sampling rate everywhere”
A uniform rate can discard rare failures while retaining large numbers of unhelpful successes. Preserve errors, high latency, and explicitly important deployments with tail decisions or targeted temporary overrides.
“Add every useful field as a metric label”
Labels such as request IDs and raw content multiply series and serialized bytes. Keep those values in bounded trace or log attributes when justified, and use a small metric dimension set for aggregation.
“Tail sampling is free selectivity”
Tail decisions require state, buffering, memory, compute, coordination, and monitoring. A saturated tail-sampling layer can drop evidence before the backend ever sees it. Load-test it at peak and failure traffic.
“A policy file guarantees enforcement”
Policy proposals are not universal implementations. Check component versions, observe policy errors, and verify behavior with test telemetry. If fail-open behavior is used, monitor the resulting volume so a policy outage does not become an unnoticed cost or privacy incident.
“A provider’s retention term is a universal recommendation”
Included storage, such as the documented 15-month period for the specified AWS metrics offering, describes a product term, not the correct retention for your agents. Match duration and tier to your evidence requirements, sensitivity, and obligations.
What a good control system looks like
A sustainable design keeps a small, queryable operational signal: bounded metrics for service and outcome health, traces that preserve correlations and unusual failures, and logs that explain decisions without duplicating large content. It removes low-value data before transfer, aggregates where fine resolution does not change an action, and uses sampling rules that reflect diagnostic value. Retention then preserves each signal only as long as its documented purpose requires. Cost dashboards, dropped-data counters, policy-version records, and periodic incident-reconstruction tests make those trade-offs visible rather than accidental.
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