The ability to reconstruct every step an AI agent took during a session, including model calls, tool use, sandbox execution, and outputs. It is essential for access review, incident investigation, and proving that an agent stayed within approved boundaries.
Expanded Definition
Per-run traceability is the operational record that lets security teams reconstruct a single agent session from start to finish, including prompts, model invocations, tool calls, sandbox activity, and resulting outputs. In NHI governance, it sits between observability and evidence preservation: enough detail to explain what happened, but also enough structure to support review, policy enforcement, and post-incident analysis.
The term is still evolving across vendors. Some platforms treat it as a logging feature, while others frame it as a full audit trail with immutable records and correlated identifiers. For NHI and agentic AI use cases, the stronger interpretation is more useful because agent actions often span multiple services and short-lived credentials. That makes per-run traceability closely related to NIST SP 800-53 Rev 5 Security and Privacy Controls requirements around audit logging and accountability, even when the implementation details differ.
The most common misapplication is treating platform logs as per-run traceability when the logs cannot reliably correlate the agent, the tool invocation, and the final side effect.
Examples and Use Cases
Implementing per-run traceability rigorously often introduces storage, privacy, and performance constraints, requiring organisations to weigh forensic completeness against data minimisation and operational cost.
- A customer-support agent uses a ticketing tool, retrieves account details, and drafts a response. The run record must show each tool call and the exact data returned.
- An internal coding agent opens a repository, proposes a patch, and triggers tests in a sandbox. Traceability captures the commands, artifacts, and outputs needed for review.
- A procurement workflow agent requests pricing from an external API and then updates an approval record. The session trail shows who authorised the tool, what data left the boundary, and what changed.
- A security analyst investigates suspicious automation and compares the recorded run to policy. The trail should answer whether the agent exceeded its allowed scope or handled sensitive secrets improperly.
NHIMG research on the Ultimate Guide to NHIs shows that only 5.7% of organisations have full visibility into their service accounts, which is a strong indicator that run-level evidence is often missing even before agentic systems are introduced. For implementation patterns, the same trace data is often discussed alongside NIST SP 800-53 Rev 5 Security and Privacy Controls because the control objective is to make actions reviewable.
Why It Matters in NHI Security
Per-run traceability matters because NHI failures rarely look like a single bad login. They look like a chain of agent decisions: a token used in the wrong context, a tool invoked outside policy, or a sandbox result copied into an unsafe destination. Without a session-level record, access review becomes guesswork and incident response depends on incomplete logs that cannot prove whether the agent respected its guardrails.
This is especially important for service accounts, API keys, and delegated agent permissions, where excessive privilege can turn one poor action into broad exposure. NHIMG research in the Ultimate Guide to NHIs reports that 97% of NHIs carry excessive privileges and 80% of identity breaches involved compromised non-human identities, which means traceability is not a convenience feature but a core evidence layer for control validation.
Organisations typically encounter the need for per-run traceability only after a disputed agent action, at which point the inability to reconstruct the run makes containment, attribution, and remediation operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-08 | Per-run traceability supports detecting, reviewing, and investigating NHI session activity. |
| OWASP Agentic AI Top 10 | A-06 | Agentic systems need session traces to explain autonomous decisions and tool execution. |
| NIST CSF 2.0 | DE.AE | Traceability improves anomaly detection and supports event analysis across agent actions. |
| NIST AI RMF | AI risk management depends on traceability to evaluate and document model behavior. | |
| NIST Zero Trust (SP 800-207) | Zero Trust requires continuously verifiable actions, which per-run traces help provide. |
Record each agent run with correlated actions, outputs, and tool use so sessions can be audited end to end.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 18, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
