An AI agent activity log should capture enough structured information to reconstruct a run from who started it, through the agent’s decisions and actions, to the final outcome. Record identity, timestamps, trace relationships, inputs and source provenance, model and tool activity, permissions and approvals, results and failures, plus operational signals such as latency and token use. Keep the record useful without turning it into an unnecessary store of raw prompts and sensitive payloads.
What an AI agent activity log needs to show
A final answer alone does not explain how an agent reached it or what it changed. Log the meaningful lifecycle events: model generations, retrievals, tool calls, delegated work, authorization checks, human approvals, external actions, and resulting state changes. Include successful, failed, denied, retried, and cancelled events so operators can reconstruct both intended and prevented activity.
Use structured events linked to a run-level trace. A session can contain multiple turns; a trace can connect the steps in a turn; and parent-child relationships can show work handed to subagents or services. Microsoft recommends end-to-end traces and OpenTelemetry GenAI conventions for consistent telemetry. OpenAI’s tracing model is one concrete implementation example, not a mandatory schema: it distinguishes agent, generation, and tool spans, and can include duration, status, errors, and usage data. Microsoft observability guidance · OpenAI tracing documentation
Practical fields to capture
A useful starting point is one event per lifecycle step, with shared identifiers that make the events joinable across systems. Adapt names and formats to your telemetry stack; not every deployment should store raw content.
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| Field group | Capture | Why it matters |
|---|---|---|
| Identity and scope | Run, session, and trace IDs; initiating human or calling service; agent and subagent IDs; version and configuration identifiers; downstream principal or service identity where relevant | Attributes activity and records the authority under which the agent operated. |
| Time and sequence | UTC event time, duration, sequence or parent-child relationship, and correlation IDs carried across services | Reconstructs ordering, latency, delegation, retries, and multi-step workflows. |
| Inputs and context | User request or safely retained representation; prompt or configuration version; retrieved source IDs and provenance; relevant context or data labels | Helps explain what information influenced the run without requiring unnecessary copies of sensitive content. |
| Agent and model activity | Model identifier when available; generation input/output references; status; usage metadata; agent handoffs | Separates model activity from orchestration, tools, and delegated work. |
| Tools and external actions | Tool or server name; target resource; normalized arguments or safe hashes/summaries; permissions or scopes; result or output reference; downstream request ID | Shows what the agent attempted and what the external system returned or changed. |
| Policy and approval | Risk or action classification where used; policy and version; allow/deny result; approval ID and approver; action parameters bound to approval; approval time and expiry | Establishes whether an action was authorized and whether approval applied to the operation actually performed. |
| Outcome and failure | Success, failure, or blocked status; result or state-change reference; useful error category and context; retry or cancellation reason | Makes failed or prevented actions visible for troubleshooting and review. |
| Operational and security signals | Latency, token use, cost, request and tool-call counts, anomalous behavior, or alerts | Supports capacity, reliability, cost, and abuse monitoring. |
| Record protection | Classification and redaction state; access controls; storage-integrity controls; retention and deletion rules | Reduces exposure and helps keep the audit record dependable. |
Microsoft’s recommendations include request identity, timestamps and run identifiers, inputs and system responses, retrieval provenance, and tool names, arguments, permissions, and outputs. Singapore’s government addendum also covers models, data stores, memory, agents, tools, MCP interactions, communications, and external actions, with actions, inputs and outputs, state changes, errors, timestamps, durations, and task or session identifiers. These are useful coverage references, not a requirement to collect every possible payload. Singapore government guidance
Connect action, identity, and authority
For each consequential action, make it possible to answer: who or what initiated the run, which agent acted, what permissions applied, which policy decision was made, what approval was given, what target was involved, and what the system did. OWASP recommends binding high-impact approvals to the actor, tool, target resource, normalized parameters, timestamp, and expiry. Logging an approval ID by itself is weaker if the record cannot establish that the approval covered the parameters ultimately executed.
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For high-impact operations, separate proposing an action from executing it and independently validate scope, privilege, and approval. Record the policy version, decision, approval metadata, and execution result. OWASP also recommends failing closed if audit logging, policy lookup, or approval validation fails; a log cannot provide accountability if the agent can bypass the authorization and recording point. OWASP AI Agent Security Cheat Sheet
Retain useful evidence without copying everything
Complete raw prompts, tool payloads, and results can expose secrets, personal information, or confidential business data if the log store is accessed improperly. But recording too little context can make attribution and incident reconstruction unreliable. Define a data contract for event types, payload handling, access, retention, and deletion. Prefer structured fields and, where they are sufficient for investigation, references, summaries, redacted values, or hashes instead of full content.
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- Redact secrets and personal data where possible; OWASP’s monitoring example redacts tool parameters and results before emitting an event.
- Restrict log access to the smallest practical set of roles and encrypt the store according to policy and risk.
- Set retention and deletion rules for the actual use case, jurisdiction, and applicable obligations; general guidance does not establish one universal retention period.
- Protect the integrity of records so unauthorized alteration is harder to conceal.
Microsoft’s observability guidance emphasizes balancing forensic value with privacy, data residency, minimization, retention duties, and compliance. OWASP warns against exposing sensitive context through logs.
Use metrics and alerts to find abnormal activity
Operational telemetry complements the event trail. OWASP recommends tracking token use and cost per session or user, alerting on security-relevant events, and watching for abnormal tool-call frequency, repeated approval-bypass attempts, elevated privilege use, and increases in high-risk actions. Microsoft identifies token usage, latency, error rate, and tool-call or request volume as useful measures, with baselines and alerts for deviations.
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Do not treat example thresholds in guidance as universal production limits. Set alert levels against the workload’s normal behavior and risk model. Usage figures may also be delayed or unavailable: OpenAI’s tracing documentation notes that token usage can arrive after a turn or remain unknown.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the trail explain evidence, not just conclusions
When an agent’s answer depends on retrieved information, retain source identifiers and provenance so reviewers can see what evidence informed the result. NIST describes evaluation-probe work aimed at moving beyond “the AI said so” toward understanding what it found, where it found it, and how evidence supports its conclusions. That work supports traceable evidence as a useful design goal; it is not a universal mandate to retain full source documents. NIST: Building Evaluation Probes into Agentic AI
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Choose a tracing approach that fits your system
When evaluating an implementation, check whether it can capture the full agent-to-tool path, correlate traces across agents and services, record permissions and approvals as well as blocked actions, support redaction and retention controls, and export data into existing telemetry systems. OpenTelemetry GenAI conventions can aid consistency across tools. OpenAI documents exporting session traces as OTLP JSON when organization settings and access permissions permit; this is a product-specific capability rather than a general requirement. Microsoft observability guidance · OpenAI tracing documentation
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