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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesTo audit delegated actions across AI agents, give each workflow a durable run identifier, record explicit parent-to-delegate relationships, and capture each agent’s identity, authority, actions, decisions, and results. Propagate that context through tools, services, queues, and callbacks, then preserve the resulting evidence in storage the agents cannot alter.
Distributed traces help explain execution order and timing; they are not, by themselves, a complete or tamper-resistant audit trail. A reliable investigation needs both connected execution records and durable evidence that can answer who initiated an action, who delegated it, what authorization applied, what happened, and what followed.
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What an audit trail across agents needs to prove
A delegated workflow is a chain of responsibility, not just a sequence of spans. A useful record links the initiating user or workload to the root task, each delegating agent, each delegate, the authority under which work was performed, and the final outcome. Without those links, separate logs may show that an agent called a tool without establishing why it did so or who authorized the work.
Tracing and audit records serve different purposes
A distributed trace helps an operator see the order, timing, status, and relationships of operations. Audit evidence must also support later attribution, retention, and integrity review. A trace viewer can be part of the solution, but it does not guarantee that every participant emitted records, that asynchronous work stayed correlated, or that stored records were protected from alteration.
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The September 7, 2026 informational Internet-Draft Audit Architecture for Agent-Driven Systems frames auditability as linking intent, delegation, authorization, and execution. It is a draft, not a final standard, and is due to expire March 11, 2027; its text may change.
Define a shared event record
Use a common event envelope across agents and the services they call. Field names can differ by platform, but records need enough shared identifiers and context to correlate local logs without relying on a single vendor’s trace format.
| Field or record group | What to capture | Why it matters |
|---|---|---|
| Workflow identity | A durable run or correlation ID, root task ID, parent event or delegation ID, and event timestamp. | Connects events across agents and makes retries or child tasks distinguishable from unrelated work. |
| Actor and delegation | Initiating user or workload, acting agent identity and version, delegating actor, receiving delegate, and the relevant service or tool identity. | Shows who initiated work and which participant took each step. |
| Authority and decision | Authorization or permission reference, approval or policy decision, and any changes to authority during the run. | Allows an investigator to assess whether the action occurred under the expected authority, not merely which process issued it. |
| Action and result | Action type, destination, relevant arguments or artifact references, status, result, and error details where applicable. | Distinguishes an attempted call from a completed action and records what the participant observed. |
| Knowledge provenance | References identifying retrieval sources or memory artifacts that materially informed the action. | Helps explain where relevant context came from without treating a model response as its own evidence. |
| Configuration and artifacts | Relevant tool or model configuration version and links to input, output, or decision artifacts. | Supports later review of the environment and evidence associated with the event. |
Microsoft Learn’s guidance recommends capturing identities, run and parent identifiers, timestamps, tools, permissions, results, and retrieval provenance, and points to OpenTelemetry GenAI semantic conventions. Treat those conventions as an interoperability aid, not a substitute for deciding what your own investigations require. In particular, decide how much prompt, message, and argument content to retain based on sensitivity and forensic need.
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Emit records at the participants that make or receive decisions; a single top-level agent log cannot reliably describe what happened inside every downstream system. OWASP’s Agentic Observability Standard (AOS) proposal groups relevant events into execution, decision, protocol, composition, and system categories, including agent-to-agent and MCP-related activity. Its specifications and extensions are proposals, not established requirements.
Record task start, delegation, and agent activity
At workflow start, record the trigger, initiating identity, root task reference, and run ID. For every handoff, record the delegator and delegate, the delegated task or a protected reference to it, the intended scope, and the authority context. Record agent activations and, where your investigative requirements justify it, meaningful model steps or decision artifacts. Do not assume that the model’s final answer alone explains the decisions made during a run.
Capture tools, retrieval, approvals, and blocked actions
For a tool or service call, capture the caller, destination, relevant arguments or artifact references, authorization context, response status, and returned result or error. For retrieval or memory operations, record source provenance sufficient to identify the material used. Record approvals, denials, policy blocks, and human modifications as events in their own right; otherwise an investigator may see an intended action without learning that it was stopped or changed.
Include failures and system changes
Record timeouts, retries, duplicate deliveries, agent crashes, and other failures with enough context to distinguish them from successful actions. Keep relevant changes to tools, permissions, or model configuration attributable and time-bounded so an investigator can establish which configuration applied to an event.
Propagate context across handoffs and asynchronous work
Carry the run ID and parent/delegation relationship through agent protocols, API calls, queues, event buses, callbacks, and retries. Preserve explicit delegation edges even if each platform emits its own local trace. A platform may provide useful trace spans for its own agent lifecycle, but local visibility does not establish continuity across a mixed system.
- Set the root context at entry. Assign or designate a stable run identifier when the initiating task enters the workflow, and attach the initiating identity and root task reference.
- Create a child relationship at each delegation. Give each delegated operation its own event or span identifier and record the parent and both actors. Keep the delegation relationship even when the receiving service cannot accept the original trace context.
- Carry context in asynchronous messages. Propagate identifiers through queue messages, callbacks, and event payloads using the mechanisms supported by each component. If native propagation is unavailable, add an explicit correlation reference and log the bridge at both ends.
- Preserve relationships during retries. Record a retry as a new attempt linked to the original operation rather than silently overwriting or conflating its history.
- Verify continuity on real workflows. Follow a successful run and a run with errors or retries from entry through the final downstream effect. AWS guidance notes that asynchronous correlation can be partial; its documentation on collaborators describes OpenTelemetry spans across a collaboration lifecycle in that product context.
Protect records and limit sensitive payloads
Store audit evidence outside the authority of the agent being audited. Apply least-privilege access, protect records with appropriate encryption, and consider immutable or tamper-evident retention where the risk warrants it. Retain an export path for independent investigation rather than making the agent or its operational dashboard the only place the evidence exists. Microsoft Learn recommends access controls, encryption, and privacy-aware retention; AWS architecture guidance recommends protected records outside the agent’s scope.
Do not capture unrestricted prompts, tool arguments, or responses simply because a tracing SDK can collect them. Messages and tool payloads may contain personal, financial, confidential, or otherwise regulated information. Define which content is necessary to reconstruct decisions, redact or minimize it where feasible, restrict access to detailed records, and set retention, deletion, and data-residency rules to meet applicable organizational and legal requirements. Microsoft Learn recommends data contracts that balance forensic needs with privacy, minimization, residency, and retention obligations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test whether an investigator can reconstruct the run
Validate the system with both technical tests and an investigation exercise. Start from a final action or a denied request, then work backward through the stored evidence. Confirm that the records answer the following questions without relying on an agent’s unsupported summary:
- Who or what initiated the task, and which root task does it belong to?
- Which agent delegated each part of the work, to whom, and under what authority?
- Which tools, services, or retrieval sources were involved, and what were their recorded results?
- Was the action approved, denied, blocked, or modified, and by whom or which policy?
- Did a retry, crash, asynchronous handoff, or downstream effect alter the outcome?
- Can the team detect missing context, duplicate events, clock differences, or attempted changes to stored evidence?
Run these checks against errors and policy blocks as well as successful workflows. An end-to-end trace that works only on the happy path does not establish that the audit trail will explain a real incident.
Evaluate tooling by coverage, not by the trace screen
For a single-platform workflow, platform tracing may provide a useful starting point. For a mixed-vendor architecture, assess the whole chain and the durability of its records before treating any one feature as an audit solution. OpenAI and AWS documentation describe platform-level tracing capabilities; those examples do not establish complete coverage across other vendors or services.
- Propagation: Can context cross agents, protocols, services, queues, and callbacks, with explicit links where it cannot?
- Event coverage: Are tool calls and results, decisions, approvals, denials, retrieval, failures, and configuration changes represented?
- Identity and authority: Can a reviewer identify the actors and the permissions or authorization context in force?
- Asynchronous continuity: Are retries and delayed work correlated to the original task without hiding distinct attempts?
- Evidence protection: Are access, integrity, retention, and export controls independent of the agents being reviewed?
- Privacy and interoperability: Can you minimize sensitive content while retaining enough context for reconstruction, and export records for cross-system review?
OpenTelemetry provides a useful basis for trace propagation and GenAI telemetry conventions. OWASP AOS is a proposed agent-specific event model, while the September 2026 IETF draft discusses common audit context across record classes and protocol and administrative boundaries. Treat each according to its status: proposals can guide design, but they do not prove that an implementation is complete or compliant.
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