To search an AI agent’s production loop reliably, emit every iteration as a schema-versioned JSON event, attach OpenTelemetry context, collect and enrich the records, then expose a controlled search API over a backend such as Elasticsearch, Loki, or a managed logs service. JSON syntax by itself is not enough: dependable filtering and aggregation require stable field names, types, and meanings.
What production agent-loop logging must accomplish
An agent run is a chain of model requests, tool calls, retries, state updates, and terminal outcomes. A useful log system lets an operator answer questions such as:
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- Which request, customer, service, and deployment produced this run?
- What happened in each loop iteration, and in what order?
- Which tool call consumed time or failed?
- Did a retry follow a timeout, validation error, or model refusal?
- Can the same event be found through a trace, a run ID, a time range, or an error type?
The design should support both a human investigating one run and an automated endpoint searching thousands of runs. That requires four properties:
- Stable schema: field names, data types, and semantics are defined and versioned.
- Correlation: trace, span, run, and resource fields connect events across services.
- Controlled storage: sensitive and high-volume data is bounded, redacted, indexed selectively, and retained intentionally.
- Queryable interface: operators and applications use documented query parameters, authorization, pagination, and predictable response envelopes.
OpenTelemetry provides an interoperability baseline for log records and context. Your organization still needs a contract that defines the agent-specific fields and their allowed values.
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JSON is only structured when the schema is stable
A line such as {"message":"tool failed"} is valid JSON, but it is not a useful event contract if one service later writes msg, another writes error_message, and a third puts the value inside an arbitrary nested object. Search, validation, dashboards, and aggregations become dependent on parser exceptions.
Define a versioned schema before instrumenting every loop. Record whether each field is required, its type, permitted values, privacy classification, and indexing policy. Keep event names stable; add a new version when semantics change rather than silently reusing a field for a different purpose.
Recommended event shape
The following example is deliberately bounded. It records enough context to investigate a run without placing an entire prompt, tool payload, or model response into every event.
{
"schema_version": "1.0",
"timestamp": "2026-10-20T14:05:31.482Z",
"observed_timestamp": "2026-10-20T14:05:31.509Z",
"severity_text": "INFO",
"severity_number": 9,
"service.name": "agent-worker",
"deployment.environment": "production",
"event.name": "agent.tool.completed",
"agent.run_id": "run_7f3c",
"agent.loop_iteration": 4,
"trace_id": "4f2c...",
"span_id": "91ab...",
"tool.name": "inventory_lookup",
"tool.input_hash": "sha256:...",
"tool.duration_ms": 183,
"status": "ok",
"body": {
"result_count": 12
}
}Use one convention for names. Dot-delimited names such as service.name and agent.run_id align naturally with OpenTelemetry resource and attribute concepts. If your backend has restrictions on dots, map them consistently at ingestion rather than producing multiple spellings.
Fields and their purposes
| Field | Purpose and guidance |
|---|---|
schema_version |
Identifies the contract used to validate and parse the event. Change it when semantics or types change. |
timestamp |
Time the event occurred according to the emitting process. |
observed_timestamp |
Time the collector or backend observed the record; the difference helps identify buffering and ingestion delay. |
severity_text and severity_number |
Human-readable and numeric severity values. Keep the mapping consistent across languages. |
service.name and deployment.environment |
Identify the producing service and environment, such as production or staging. |
event.name |
Stable event type, for example agent.loop.started, agent.model.completed, or agent.tool.failed. |
agent.run_id |
Application-level identifier for one complete agent run. |
agent.loop_iteration |
Monotonic iteration number within a run. Define whether counting starts at zero or one. |
trace_id and span_id |
Connect the event to distributed tracing and the specific operation that emitted it. |
tool.name, tool.input_hash, tool.duration_ms |
Identify a tool without storing its sensitive input, prove repeated inputs when needed, and measure elapsed time. |
status and error.type |
Use a controlled status vocabulary and a stable error classification for filtering. |
body or attributes |
Store bounded event-specific details. Reject or truncate oversized values according to policy. |
Do not put secrets, access tokens, raw personal data, or unbounded prompts in the body. Store a hash, redacted summary, or reference to an access-controlled system when an investigation genuinely needs the original content.
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How OpenTelemetry maps to agent events
OpenTelemetry LogRecords can carry a timestamp, observed timestamp, context, severity number and text, body, attributes, event name, and optional exception data. The Logs API is intended for logging-library authors to build appenders, so an agent framework can emit records directly or bridge its existing logger into the API.
Emit at meaningful boundaries
Instrument boundaries rather than every internal variable assignment. Typical events include run start and end, model request and completion, tool request and completion, retry scheduling, guardrail decisions, and exceptions. For expensive record construction, use the logging implementation’s enabled check so a disabled severity does not require serializing a large body.
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Keep event names and field types identical across model providers and tools. A model completion should not use a string duration in one adapter and a number in another. If a provider has unique metadata, put it under a namespaced attribute rather than changing the shared fields.
Correlate the whole run
Trace ID and span ID connect a log event to the distributed trace. The run ID follows the business-level agent execution even when the run crosses several traces or asynchronous workers. Resource context identifies the service, host, container, region, and deployment that produced the record. Together, these identifiers let an operator move from an API request to a model call, then to a tool call and a downstream service without relying on message text.
Propagate context across queues and background jobs. If a retry is intentionally detached from the original trace, preserve the run ID and record a parent-run or retry relationship in attributes so the sequence remains discoverable.
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Collect, transform, and export the records
- Emit. Create a LogRecord in the agent loop or in a logging bridge. Validate required fields locally and cap body size before transmission.
- Receive. Send records through OTLP or another supported input to an OpenTelemetry Collector or equivalent agent. File tailing can work for legacy processes, but direct structured transport avoids fragile multiline parsing.
- Parse and normalize. Decode JSON, convert timestamps and numeric fields, map legacy names, and reject malformed records into a dead-letter path instead of silently dropping them.
- Enrich. Add resource metadata, deployment information, tenant identifiers, and collector timestamps. Apply redaction before data leaves the trusted boundary.
- Export. Route normalized events to the selected search backend. Keep routing rules and index or label policies under version control.
- Monitor the pipeline. Track ingestion lag, parse failures, rejected records, exporter errors, query latency, and retention consumption. These measurements are more useful than an assumed universal throughput or latency number because workload, schema, and backend configuration vary.
The Collector can receive, parse, transform, enrich, and export logs. A deployment should include a bounded queue and retry policy, but also a clear failure mode: decide which events may be dropped under pressure and which must be durably spooled or sent to a dead-letter destination.
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Choose the search layer deliberately
| Backend | Query surface | Strengths | Checks before standardizing |
|---|---|---|---|
| Elasticsearch with EQL | EQL and REST search controls over indexed documents | Rich field search, aggregations, and event-sequence analysis; suitable for self-managed or hosted deployments. | Mappings, shard and index lifecycle, EQL and DSL skills, and retention cost. |
| Grafana Loki | LogQL through the HTTP API, with OTLP ingestion documented at /otlp/v1/logs |
API-first, label-oriented querying and open-source deployment choices. | Label cardinality, LogQL expertise, object-storage design, and retention configuration. |
| Datadog Logs API | Managed log search API with archive capabilities | Managed observability, integrated retention controls, and hosted exploration. | Plan limits, pricing, data residency, retention terms, and archive retrieval behavior. |
EQL assumes each document in a data stream or index corresponds to an event. That model is useful for finding ordered patterns, such as a tool failure followed by a retry and then a run termination. LogQL is oriented around selecting streams by labels and filtering their contents, so label design is critical. A managed Logs API reduces infrastructure work but makes plan and vendor constraints part of your operational design.
Design a safe production search API
Do not expose a raw backend endpoint to every application or user. Put a small service in front of it with a stable contract. A practical request can accept:
run_idortrace_idfor exact correlation;fromandtotimestamps with an enforced maximum window;service,environment,event_name,severity,tool_name, anderror_typefilters;- a cursor or page token rather than an unbounded result size;
- a sort direction and an explicit limit within a server-side maximum.
Validate every parameter against an allowlist. Reject backend query syntax supplied by clients unless you have a separate, carefully sandboxed administrative interface. Enforce tenant authorization before constructing the backend query, and apply field-level redaction to responses as well as ingestion.
Use a stable response envelope
{
"data": [
{
"timestamp": "2026-10-20T14:05:31.482Z",
"event.name": "agent.tool.completed",
"agent.run_id": "run_7f3c",
"status": "ok"
}
],
"next_cursor": "eyJvZmZzZXQiOjEwMH0",
"partial": false,
"took_ms": 42
}Document whether results are ordered by event time or ingestion time, how equal timestamps are tie-broken, and what partial means when a shard, tenant, or archive is unavailable. Return a request ID for support and auditing. Saved queries can provide a safer interface for common views such as “all failed tools for this run” or “all retries in the last hour.”
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Retention, indexing, and cost controls
Index fields used for frequent filtering: run ID, trace ID, service, environment, event name, severity, tool name, status, and error type. Avoid indexing every arbitrary body key; dynamic mappings and high-cardinality fields can increase storage and query cost. In Loki, be especially conservative with labels: a run ID generally belongs in the log content or structured metadata rather than a label if it creates a stream for every run.
Set retention by operational value. Recent events may remain in a fast index, while older records move to cheaper storage or an archive subject to the same authorization rules. Define deletion and legal-hold behavior before production launch. Measure actual ingestion volume, index growth, query latency, and archive retrieval time from your workload; no universal benchmark establishes the right values for every agent system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Failure modes and recovery
Events fail schema validation
Return a validation error in development and route invalid production records to a dead-letter stream with the original payload, parser error, and collector timestamp. Alert on the rate, not just the existence, of failures. Add contract tests for every language adapter and event producer.
Logs arrive late or out of order
Retain both event and observed timestamps. Query using a time tolerance around the user’s requested window, then display the event-time ordering explicitly. Queue retries and clock synchronization reduce confusion but do not eliminate asynchronous arrival.
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Sensitive data appears in a body
Stop indexing the affected route, rotate exposed credentials, and remove or restrict access to stored copies according to your incident policy. Add deterministic redaction and payload-size checks at the emitter and Collector; relying only on a dashboard filter is insufficient.
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Require a narrower time range, an exact run or trace identifier, or a bounded result limit. Review mappings, shard or index lifecycle, Loki label design, and archive boundaries. Cache only authorized, non-sensitive saved-query results, and return a cursor instead of increasing the page size.
The backend is unavailable
Keep the agent’s business path independent from synchronous log search. Use bounded local buffering or an approved durable queue, expose ingestion health separately, and document which events may be sampled or discarded during an outage. Never let an unbounded logging queue exhaust the worker that is running the agent.
A rollout checklist
- Publish a schema with field names, types, required fields, event names, and a versioning rule.
- Instrument run, model, tool, retry, and exception boundaries.
- Propagate trace ID, span ID, run ID, and resource context through asynchronous work.
- Redact secrets and bound prompts, tool inputs, outputs, and exception text.
- Deploy a Collector or equivalent pipeline with validation, enrichment, retries, and dead-letter handling.
- Select Elasticsearch/EQL, Loki/LogQL, or a managed Logs API after reviewing query semantics, deployment, retention, and cost.
- Expose an authenticated search API with allowlisted filters, pagination, stable ordering, and a response envelope.
- Test malformed records, delayed delivery, duplicate events, partial backend failures, tenant isolation, and redaction.
- Dashboard ingestion lag, parse failures, query latency, storage growth, and retention usage.
- Measure the system under your own agent workload before setting service-level objectives.
Practical query patterns
Implement these as saved, parameterized queries rather than letting callers assemble arbitrary backend syntax:
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agent.run_id, ordered by event timestamp and a deterministic tie-breaker. - Trace drill-down: search
trace_idacross services to expose downstream effects of one agent request. - Tool reliability: filter by
event.nameandtool.name, then aggregatestatusanderror.type. - Retry investigation: find retry events and the preceding failure within the same run or trace.
- Pipeline health: compare
timestampwithobserved_timestampto identify ingestion delay.
This approach keeps the agent loop observable without making production logs a second, uncontrolled data warehouse. A versioned event contract, OpenTelemetry correlation, a resilient collection pipeline, and a narrowly governed search API are the pieces that turn JSON records into an operable system.
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