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To audit a delegated AI workflow, carry a durable run identifier and explicit parent–child delegation links through every agent, tool, service, queue, and callback. Record who initiated and delegated each action, what authority applied, what happened—including approvals, denials, failures, and results—and preserve those records in storage the agents cannot alter. Distributed traces help reconstruct execution flow; they are not, by themselves, a complete or tamper-resistant audit trail.
What an audit of delegated AI actions needs to show
An investigation should be able to follow responsibility and execution from the original request to its downstream effects. A chain of spans alone may show which services ran, but not necessarily why an action was permitted, which identity delegated it, or whether a guardrail blocked an attempt.
For each meaningful handoff, preserve the initiating task, the delegating actor, the receiving agent or service, the authority context, and the action outcome. Keep explicit delegation relationships even when platforms also generate local traces. This reflects the framing in the September 7, 2026 IETF informational Internet-Draft draft-kuehlewind-audit-architecture-01, which links intent, delegation, authorization, and execution. It is a draft, due to expire March 11, 2027, not a final standard.
What to record at each step
Use a common event envelope across agents and the services they call. The following fields are a practical baseline; capture sensitive content only when justified by the investigation need and your data-handling rules.
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| Field | What it establishes |
|---|---|
| Event time and event type | When the event occurred and whether it was an activation, delegation, tool call, retrieval, approval, denial, modification, failure, or other relevant action. |
| Run ID and parent or delegation ID | Which workflow the event belongs to and which task, agent, or event directly led to it. |
| Actor and agent identity | The user or workload that initiated the work, the agent or service acting, and—where available—the agent or configuration version. |
| Authority reference | The permission, policy decision, credential context, or approval under which the action was attempted or performed. Record changes in authority as events. |
| Target and action | The tool, service, resource, or data source involved and the operation requested. |
| Outcome | Success, error, denial, modification, or pending status; include the relevant result or a protected reference to it. |
| Provenance and artifact references | Which retrieval sources informed a decision and where the related input or output artifact can be securely examined. |
Microsoft Learn recommends capturing identity, tool and request details, results, retrieval provenance, and related telemetry, including OpenTelemetry GenAI semantic conventions. The appropriate level of prompt, message, and argument detail depends on sensitivity and forensic needs; a tracing SDK’s ability to capture a payload is not a reason to retain it unrestricted.
Which events to include
Instrument boundaries where responsibility, data, or authority changes. OWASP’s Agentic Observability Standard (AOS) proposal groups relevant events into execution, decision, protocol, composition, and system categories. Its materials discuss A2A and MCP protocol events; its OTel and OCSF extensions are presented as working drafts, not established universal requirements.
- Execution: agent activations, relevant model steps, tool or service calls, returned status, and failures.
- Decision: approvals, denials, policy blocks, human review, and modifications to a proposed action.
- Communication and delegation: agent-to-agent messages, A2A or MCP interactions, and the parent–child relationship created at each handoff.
- Data access: memory and retrieval operations, including the sources that influenced an action. A log of writes alone cannot establish what information an agent read.
- System changes: changes to tools, permissions, model configuration, or other settings that could affect later decisions.
Record blocked attempts and denied actions as well as successful ones. Otherwise, an investigator may see the final result without evidence that a guardrail intervened—or may mistake an absence of logged activity for proof that no attempt occurred.
How to carry context across agents and asynchronous work
- Create a root context. At workflow start, assign a stable run-level correlation ID. Record the initiating user or workload, trigger type, root task, and relevant authorization reference.
- Create a delegation edge at every handoff. When one participant delegates work, record its identity, the receiving participant, the parent task or event, the delegated task reference, and the authority context. Do not rely on matching timestamps or similar task text to infer parentage later.
- Pass context through each boundary. Propagate trace and audit context through agent protocols such as A2A or MCP, API calls, queues, event buses, and callbacks. Keep the delegation relationship even if a platform also emits its own local span or trace.
- Bridge systems that cannot propagate context natively. Add an explicit mapping between the upstream run or parent identifier and the downstream job or trace identifier, and log the bridge event. Preserve enough metadata to find both sides without copying sensitive payloads unnecessarily.
- Check retries, delayed work, and errors. Ensure retries and asynchronous completions retain the original relationship while remaining distinguishable as separate attempts or events. Verify that failure paths are logged, not just the happy path.
AWS documentation describes OpenTelemetry spans across a collaborator lifecycle in one product context and warns that asynchronous correlation can otherwise be partial. That is useful implementation guidance, not evidence that every platform or mixed-vendor system automatically preserves end-to-end context.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsHow tracing differs from durable audit evidence
| Capability | Distributed tracing | Audit records |
|---|---|---|
| Main use | Inspect execution sequence, timing, status, and service relationships. | Support later attribution, authorization review, retention, and investigation. |
| Typical view | Spans and links that help operators follow a request across components. | Events and artifacts that establish who acted, under what authority, and with what outcome. |
| Key risk if used alone | Context may break at asynchronous or cross-platform boundaries; a trace may omit decisions or data access. | Records can still be incomplete or alterable unless coverage, access, and integrity are deliberately managed. |
| What to do | Use trace context to investigate flow and latency. | Retain independent, controlled evidence and test whether it supports reconstruction. |
OpenTelemetry trace capabilities can support observability, but neither a trace viewer nor a platform’s local event log proves that a mixed-vendor workflow is completely covered. Keep audit records outside the authority of the agent being audited, restrict modification and access, and make retention and integrity controls explicit. Microsoft Learn advises access controls, encryption, and privacy-aware retention; AWS architecture guidance recommends protected records outside the agent’s scope. Depending on risk, consider immutable or tamper-evident storage and export records for independent review. The IETF draft discusses optional attestation and independent third-party logging as possible assurances, not universal requirements.
Protect privacy while retaining useful evidence
Detailed prompts, messages, tool arguments, and results can contain personal, financial, or confidential information. Define what must be retained to reconstruct decisions and effects, and avoid capturing unrestricted secrets or personal data simply because instrumentation makes it easy.
- Prefer event metadata and references to protected artifacts when full payload retention is not necessary.
- Redact or minimize sensitive fields where feasible, while preserving the evidence needed to explain the action.
- Limit access to detailed records and encrypt stored data.
- Set retention, deletion, and data-residency rules to meet organizational, legal, and regulatory requirements.
- Document which records are captured, who may access them, and how long they remain available.
Microsoft Learn recommends governing capture and retention through data contracts that balance forensic needs with privacy, data residency, minimization, retention, and legal obligations. OWASP AOS also identifies sensitive message and tool data as an observability concern.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to test whether an investigator can reconstruct a workflow
Test a real end-to-end workflow and a deliberately difficult set of cases before treating the logging design as adequate. The question is not merely whether a trace appears, but whether an independent reviewer can connect the evidence and establish what happened.
- Can the reviewer identify the initiator, every delegating and delegated actor, and the final downstream effect?
- Can they determine which authorization or approval applied when the action ran?
- Can they identify the tool or service, the requested action, its status, and the relevant result?
- Can they see retrieval sources that influenced the action, plus denied or guardrail-blocked attempts?
- Do context links survive asynchronous queues, callbacks, retries, agent crashes, and partial failures?
- Can duplicate events, clock differences, or missing context be recognized rather than silently mistaken for a complete history?
- Can unauthorized modification of stored evidence be detected, and can records be exported for independent investigation?
Use the findings to adjust instrumentation, context propagation, storage controls, and data contracts. Re-run the checks after material changes to agents, tools, permissions, or workflow architecture.
How to compare implementation options
For a platform or observability stack, assess the whole path rather than choosing on trace visualization alone. Compare support for context propagation across agents and protocols; event coverage for tool calls, decisions, approvals, denials, retrieval, and failures; asynchronous continuity; identity and authorization detail; integrity and storage controls; privacy, retention, and residency controls; and exportability or interoperability.
OWASP AOS is a proposal for agent-specific conventions. OpenAI and AWS documentation illustrate platform-level tracing capabilities, while the IETF draft proposes a cross-boundary audit architecture. Those sources describe different scopes; none, by itself, establishes complete coverage for a particular mixed-vendor deployment. Validate the actual workflow and records against the reconstruction checks above.
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