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Sometimes, but only if the system was built to record it. An agent can return a fluent answer or report a finished task while leaving no usable account of what triggered the run, which model and agent acted, what tools it touched, or what evidence backed its claims. Reconstructing a run means linking those pieces into one record. Current guidance from OpenAI, Microsoft, OWASP, and NIST covers parts of that record. None of it guarantees that the record is complete, correct, or tamper-resistant.
The questions a reconstruction has to answer
When someone asks what an agent did, the honest answer usually needs to cover seven things. Each one maps to a different kind of record, and a log that answers only one or two of them leaves the rest of the investigation guesswork.
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- Trigger: what started the run, whether a user request, a scheduled job, an event, or another agent.
- Actor: which agent and which model, and where captured, the software or configuration version in effect.
- Actions: which tools were called, with what arguments, and what came back, including errors.
- Data: which documents, records, or memory entries were read or written.
- Delegation: whether another agent was involved, and what messages passed between them.
- Authorization: which approval, permission, or policy check applied to a consequential action, and what the outcome was.
- Support: what evidence sits behind any factual claim the agent made to a person or system.
Linked context, not isolated model logs
A prompt-and-response log tells you what the model was asked and what it said. It does not tell you why a tool was called three steps later or which user request that call served. OpenAI’s tracing documentation describes traces as built from steps within turns and sessions, with spans for agents, generations (model calls), and tools. That structure is what lets an investigator move from an outcome back to the request that started it. The guidance is about a hierarchy of linked spans, so the first design question is whether your system emits those relationships at all, not merely whether it writes logs.
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Evidence for claims, not only activity
Recording that a tool ran establishes activity. Establishing that a claim was true requires something else. NIST’s evaluation-probe project, created May 1, 2026 and updated May 5, 2026 and still ongoing, works on this problem. It describes probes that can run during a workflow or after it, compare generated output against trusted source material, and return a rationale for whether the source supports a claim. The project frames three dimensions for judging a claim:
- Faithfulness: whether the source supports the claim.
- Completeness: whether the text captures the source’s full message.
- Sufficiency: whether the evidence carries the burden of the claim.
The project also describes mapping agent decisions to supporting evidence in a structured audit trail. Because the work is still in progress, treat it as a direction for design rather than an established practice. Its project page puts the goal this way: “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.’” That sentence is the project’s own description and is not attributed to a named individual.
Control decisions for consequential actions
For actions that move money, change access, send external messages, or alter records, OWASP’s AI Agent Security Cheat Sheet recommends capturing structured metadata alongside the action. The fields it describes are:
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- risk score, where the system computes one;
- authorization result;
- approval identifier;
- execution result;
- policy version in force at the time.
The policy-version field is the one most often missing. Without it, a later reviewer can see that an action was allowed but cannot tell which rule allowed it, or whether the rule had changed since. A denied or modified action should be recorded with the same fields, because a blocked attempt is often the most informative event in an investigation.
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What a complete trace should contain
The checklist below combines the event categories in OWASP’s Agent Observability Standard (AOS) event specification, the high-risk action metadata above, and the span structure in OpenAI’s tracing documentation. It is an editorial synthesis, not a claim that any one standard requires every field.
| Element | What to record | Primary guidance |
|---|---|---|
| Trigger | User request, scheduled job, or autonomous trigger, with identifier and timestamp | AOS event specification |
| Agent identity | Agent name, model, and software or configuration version where captured | OpenAI tracing (agent spans) |
| Model calls | Inputs and outputs, subject to data policy | OpenAI tracing (generation spans) |
| Tool calls | Tool name, arguments, execution result, error, and timestamp | OpenAI tracing (tool spans) |
| Retrieval and memory | Reads and writes, with the source referenced | AOS event specification |
| Delegation | Parent and child run relationships and inter-agent messages | AOS event specification |
| High-risk actions | Action classification, authorization result, approval identifier, execution result, policy version | OWASP AI Agent Security Cheat Sheet |
| Evidence | References to the sources supporting important factual claims | NIST evaluation-probe project |
| Health and performance | Errors, latency, and resource events relevant to the run | AOS event specification |
Keep the identifiers consistent across all rows. A trace is only as useful as its ability to join a tool call to the approval that preceded it and to the run that produced it.
What a trace does not prove
A trace is evidence that something was recorded. It is not proof that the record is right. Four separate questions are involved, and each needs its own check:
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- Is it linked to the right run and actor? Check that identifiers join correctly across agents and systems, especially after a handoff or a retry.
- Does the cited evidence support the claim? This requires a claim-checking step, not just a link to a source.
- Are integrity and coverage established independently? Platform documentation describes how to inspect traces. It does not establish that logs are complete, immutable, or legally sufficient, so those properties need separate verification.
Treating the first question as the answer to the fourth is the most common mistake. A dashboard full of spans can still be missing the tool calls that failed silently.
The privacy and retention trade-off
More detail helps an investigation and also widens exposure. Prompts, responses, tool arguments, retrieved content, and user information can all end up in telemetry. Microsoft’s Agent Framework observability documentation describes a setting that can log prompts, responses, function-call arguments, and results, and it cautions that enabling sensitive-data logging can expose that material. The AOS specification identifies comparable risks in message, memory, retrieval, and agent-to-agent events, including sensitive information exposure and oversharing.
In practice, that means deciding in advance what each event type stores in full, what gets redacted, who can read the traces, and how long they are kept. The published specifications do not set a universal retention period. The right period depends on operational needs and on the legal requirements that apply to the data and the jurisdiction. Limit access by role, and treat the trace store as a sensitive system in its own right.
Recorded rationale is not proof of reasoning
Some systems store the rationale an agent emits alongside an action. That record is useful: it shows what was captured at the time. It does not establish that the stored text faithfully describes the hidden computation that produced the action. Describe a stored rationale as a recorded explanation, not as a window into the model’s internal reasoning. The stronger and better-supported claim is that structured records improve reconstruction and make evidence checking possible.
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The standards landscape
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 a program announcement, so it does not by itself create a required format for agent logs.
OWASP’s Agent Observability Standard project frames agents as needing to be instrumentable, traceable, and inspectable. It describes building on existing standards, including OpenTelemetry, OCSF, CycloneDX, SWID, and SPDX. The project page lists roadmap milestones, and the event specification enumerates event types. Both are drafts that may change, and neither shows that the approach has been widely adopted.
Comparing implementations
When you evaluate an agent platform or tracing tool, compare it on the properties that matter for reconstruction rather than on the number of dashboards it offers:
- event coverage across model calls, tools, retrieval, memory, triggers, and delegation;
- evidence linkage and claim checking;
- correlation of runs across agents and systems;
- privacy controls, redaction, access control, and retention configuration;
- export and compatibility with OpenTelemetry or other existing telemetry;
- policy and approval metadata, and anomaly monitoring;
- the integrity guarantees the vendor actually documents.
Microsoft documents OpenTelemetry integration for Agent Framework, and OWASP’s AOS describes extensions to existing standards. Those support interoperability as a criterion. They do not establish that every product implements the same schema, so ask a vendor to show an exported trace for a consequential run rather than relying on the feature list.
Where to start
- Pick one consequential workflow, such as one that sends external messages or changes records.
- Define the trigger, agent, and policy identifiers, and make sure every tool call carries them.
- Record the authorization result, approval identifier, and policy version for each high-risk action, including denied ones.
- Connect parent and child runs for any delegated work, and store inter-agent messages.
- Decide, per event type, what is stored in full, what is redacted, who can read it, and how long it is kept.
- Run a test task, then check whether an outside reviewer can rebuild the run from the trace alone.
The last step is the one that tells you whether the trace answers the question. If the reviewer needs to ask the engineer who built the agent what happened, the record is not yet complete.
Primary references: NIST evaluation probes, OpenAI tracing, OWASP AI Agent Security Cheat Sheet, Microsoft Agent Framework observability, AOS event specification, OWASP AOS project, and NIST announcement of the AI Agent Standards Initiative. Platform documentation was accessed October 7, 2026, and defaults and draft specifications may change after that date.
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