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An AI agent’s run can be explained after the fact only if its record links the trigger that started it, the agent and model that acted, every tool call and its result, any delegated agent, the evidence behind each consequential claim, and the authorization that allowed each high-risk action. A plausible final answer does not show any of that. Standard application logs usually do not either, because they record that something happened without tying it to the run, the actor, or the decision that permitted it.
This article explains what an audit-ready record of an agent run should contain, where the current guidance from OpenAI, Microsoft, NIST, and OWASP agrees and where it is still in draft, and what a trace cannot prove on its own.
The six questions a reconstruction must answer
When someone asks what an agent did, the answer has to be assembled from several kinds of record. The table below maps each question to the fields that answer it and to the documentation that supports recording them. Where a field is not required by a named standard, the table says so.
| Question an investigator asks | Fields to capture | Documented basis |
|---|---|---|
| What started the run? | User request, scheduled job, event, or other trigger identifier; time of trigger | OWASP AOS event categories; OpenAI trace structure (turns and sessions) |
| Which agent and model acted? | Agent identity; model name; agent or software version where captured | OWASP AOS event categories; Microsoft Agent Framework observability |
| What did the model receive and return? | Generation inputs and outputs, as data policy permits | OpenAI generation spans; Microsoft observability (sensitive-data logging is off by default in the documentation reviewed here; see privacy section) |
| Which tools ran, with what arguments, and what came back? | Tool request, arguments, execution result, error, timestamp | OpenAI tool spans; Microsoft function-call logging option |
| Was another agent involved? | Parent and child run identifiers; delegation records; inter-agent messages | OWASP AOS agent-to-agent event types; OpenAI agent spans |
| Was the action allowed, and by which rule? | Action classification; authorization result; approval identifier; policy version; allow, deny, or modify outcome | OWASP AI Agent Security Cheat Sheet recommendations for high-risk actions |
| What evidence supports the claims made? | References to source documents supporting important factual statements | NIST evaluation-probe project (work in progress) |
Start with linked context, not isolated model logs
A single model response tells you what the model said at one moment. It cannot tell you why a refund was issued three steps later, or which retrieval results shaped it. The record has to join those steps into one run.
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OpenAI’s tracing documentation describes traces as a structure of steps within turns and sessions, with spans for agents, generations, and tools. That structure is the closest thing in the current vendor documentation to a run-level view: the parent trace holds the sequence, and each span records one piece of it. The documentation covers how to inspect that structure in its own tracing features. It does not state that the trace is a complete or tamper-evident audit log, so treat it as a reconstruction aid rather than a legal record.
Source: OpenAI tracing guide.
Record the evidence behind claims, not only activity
Knowing that an agent called a search tool does not tell you whether the answer it gave is supported by what the search returned. Activity logs establish the sequence of events; evidence records establish whether the conclusions follow.
NIST’s evaluation-probe project, created May 1, 2026 and last updated May 5, 2026, is working on this. Its described probes can run during a workflow or after it. They compare generated outputs with trusted source material and return a rationale for whether the source supports a claim. The project describes three dimensions it uses to judge that support:
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- Completeness: whether the text captures the source’s full message.
- Sufficiency: whether the evidence carries the burden of the claim.
The project also maps agent decisions to supporting evidence in a structured audit trail. Because the work is ongoing, it should be read as a direction for practice rather than an established method that every auditor can apply today. The project page also states the goal in its own words: “The goal is to move beyond ‘the AI said so’ to better understand ‘here is what the AI found, where it found it, and how the evidence supports the conclusions.’” The statement is the project’s description, not a quotation from a named individual.
Source: NIST evaluation probes project.
Capture authorization and policy decisions for consequential actions
For actions that move money, change access, send messages outside the organization, or alter production data, the most important question after the fact is not only what happened but whether it was permitted. OWASP’s AI Agent Security Cheat Sheet recommends structured metadata for high-risk actions. The fields it lists are:
- action classification;
- risk score, where the organization uses one;
- authorization result;
- approval identifier;
- execution result;
- policy version.
The policy version field matters more than it first appears. A decision that was allowed under the rules in force on the day may look wrong against rules changed later. Without the version, an investigator cannot tell which rule applied. Note that the risk score is recommended only “where applicable,” so it should not be treated as a universal requirement.
Source: OWASP AI Agent Security Cheat Sheet.
Distinguish a recorded rationale from proof of internal reasoning
Some agent systems store a rationale the model emitted alongside an action. That stored text is useful, but it establishes only what was captured. It does not establish a faithful account of the model’s internal computation. Two runs with the same recorded rationale could have followed different internal paths, and a rationale can be written after the fact to justify a decision.
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What a record can do is improve reconstruction and evidence checking: it can show what inputs were present, what the agent produced, and whether the stated support appears in the cited sources. Treat the rationale as one field among others, and check it against the evidence references rather than accepting it as an explanation.
Privacy and retention: more detail aids review and increases exposure
Detailed traces are the most useful for incident review and the most sensitive. Prompts, responses, tool arguments, retrieved content, and user information may all appear in telemetry.
Microsoft’s Agent Framework observability documentation describes a setting that can log prompts, responses, function-call arguments, and results, and warns that enabling sensitive-data logging can expose that material. The OWASP AOS event specification identifies similar risks, including sensitive information exposure and oversharing, across message, memory, retrieval, and agent-to-agent events. Those are the places where personal data and confidential content are most likely to appear.
In practice, that means:
- Log the event and its identifiers by default, and capture content only where a defined need exists.
- Redact or hash personal identifiers before they reach shared telemetry.
- Restrict who can read full-content traces, separately from who can read summaries.
- Set retention from your operational and legal requirements. The documentation reviewed for this article does not establish a universal retention period, and none should be assumed.
Sources: Microsoft Agent Framework observability; OWASP AOS event specification.
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NIST announced its AI Agent Standards Initiative on February 17, 2026, with a focus on industry-led standards, open-source protocol development, and research on agent security and identity. The announcement names confidence and interoperability as constraints on wider adoption. It is an initiative, not a finished standard, so it does not yet define required audit fields.
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OWASP’s Agent Observability Standard project describes agents as needing to be instrumentable, traceable, and inspectable. It states that it builds on OpenTelemetry, OCSF, CycloneDX, SWID, and SPDX. The project page includes roadmap milestones, and the event specification enumerates event types. Both are drafts from the standpoint of adoption: they do not show that products implement the same schema, and they should not be presented as a universally implemented requirement.
Sources: NIST announcement on the AI Agent Standards Initiative; OWASP Agent Observability Standard project.
For buyers comparing tools, the useful axes are event coverage across model calls, tools, retrieval, memory, triggers, and delegation; evidence linkage; the ability to correlate runs across agents and systems; privacy controls and retention settings; export to OpenTelemetry or other existing telemetry; policy and approval metadata; and which integrity guarantees the vendor documents. Microsoft documents OpenTelemetry integration for Agent Framework, which supports interoperability, but that does not establish that all products share one schema.
How to reconstruct a run after the fact
- Identify the trigger. Find the user request, scheduled job, or event that started the run, and note its timestamp. If no trigger identifier exists, record that gap; it weakens every later step.
- Pull the parent trace. Retrieve the top-level run and confirm that its turns and sessions are complete, with no missing child spans.
- Order the generations and tool calls. For each model call, check the inputs and outputs that were captured, then match each tool request to its execution result and error status.
- Follow delegation. For any child agent or inter-agent message, pull the child run and confirm the parent-child link.
- Check authorization for each high-risk action. Confirm the classification, authorization result, approval identifier, and the policy version in force at that time.
- Test the evidence. For each consequential claim, locate the cited source and check whether it supports the statement, using the faithfulness, completeness, and sufficiency questions above.
- Record what you could not establish. State any missing events, unlinked actors, or redacted content in the findings, rather than filling the gap with inference.
What a trace cannot prove
A trace shows that an event was recorded. Four things need separate checks:
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- The record is linked to the correct run and actor.
- The cited evidence supports the claim.
- The record is complete, meaning no events were dropped or sampled out.
- The record’s integrity has been independently established.
The product documentation reviewed for this article supports trace inspection features. It does not support a general claim that platform logs are complete, immutable, or sufficient as legal evidence. Ask the vendor specifically how events are dropped, sampled, or retained, and whether storage is write-protected, before relying on a trace in a dispute.
Practical trace checklist
For any consequential run, aim to link each of the following:
- the initiating task, user request, or autonomous trigger;
- agent identity and, where captured, agent, model, and software version;
- model generation inputs and outputs, as data policy permits;
- each tool request, its arguments, execution result, error, and timestamp;
- retrieval and memory reads and writes where relevant;
- parent-child or delegated-agent relationships and inter-agent messages;
- approval, authorization, policy version, and the allow, deny, or modify outcome for high-risk actions;
- evidence references supporting important factual claims;
- relevant error, health, and performance events.
This list combines event categories from the OWASP AOS specification, the high-risk action metadata in the OWASP cheat sheet, and the trace spans in OpenAI’s tracing guide. It is an editorial synthesis. No single standard currently requires every field, so the list is a starting point for your own policy rather than a compliance checklist.
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