Agent provenance
Also called: AI agent provenance, agent execution provenance
Evidence that connects an agent action to the identities, sources, models, policies, tools, approvals and release context that produced it.
What agent provenance means.
Agent provenance is the traceable origin and transformation history behind an AI agent's action. It links the initiating human or system, acting agent, source material, model and version, policy decision, approval, tool request, downstream receipt and deployed release. Provenance is broader than a timestamped log entry because it preserves the relationships needed to answer who acted, on whose authority, using which inputs and components, and what system accepted the effect.
Why it matters.
An agent can combine retrieved data, model output, memory and tool responses before it changes a real system. Without provenance, a reviewer sees the final action but cannot determine whether a stale source, changed model, poisoned memory, invalid delegation or different release shaped it. Provenance makes investigation, correction, evidence review and controlled reuse possible.
How it works in practice.
Identity and delegation stay attached
The record connects the initiating principal, acting workload identity, tenant, delegated scope, expiration and any handoff between agents or services.
Inputs retain source context
Retrieved records, documents and memory carry source identifiers, content digests, retrieval time, trust class and handling constraints instead of becoming anonymous prompt text.
Models, policies and tools are versioned
The execution records the model endpoint and version, policy bundle, tool contract, configuration and release so later review uses the components that actually ran.
Effects connect to receipts
A proposed action is linked to authorization, approval, request identity, target response and reconciliation state, which distinguishes intent from an accepted or completed side effect.
What people get wrong.
That provenance means storing every prompt and hidden reasoning trace forever. Useful provenance identifies origin, authority, transformation and effect while minimizing sensitive payloads. Content digests, stable references, typed events and authorized excerpts can preserve accountability without duplicating every source into the audit system.
Common questions.
How is provenance different from an audit log?
An audit log is a sequence of events. Provenance is the relationship graph across identities, inputs, components, decisions and effects. A strong audit record can carry provenance, but a timestamp and message alone usually cannot.
Does provenance prove an output is correct?
No. It shows the basis and path of an action so quality can be assessed. Incorrect sources or models can still produce a well-documented wrong answer.
Should source content be copied into the record?
Only when necessary and authorized. Prefer stable identifiers, content digests, versions and controlled excerpts when they provide enough evidence without creating another sensitive-data store.
What happens when a model endpoint changes?
Record the route change and endpoint identity, apply the approved selection policy, retain evaluation evidence and preserve one run identity across the transition.
How does provenance help incident response?
It identifies affected runs, inputs, releases, models, policies and target effects, so investigators can bound impact and determine which records or actions need correction.
Can provenance cross an air gap?
Only through the authorized transfer and release process. Inside a disconnected environment, the identity, source, component and receipt records must remain resolvable without external services.
Standards referenced.
Trunnion is engineered to align with these control families. Alignment by design is not a claim of current certification or authorization.
Read next.
- Replayable audit recordAn audit record complete enough to reconstruct an agent run step by step, including the reasoning behind each action.
- Hash-chained audit trailAn append-only log where each entry includes the cryptographic hash of the previous one, so any later alteration is detectable.
- Agent governanceThe control layer that decides what an AI agent may do, who approves consequential actions, and how every step is proven afterward.
- LLM-agnostic model routingDirecting each task to an appropriate language model at runtime, so a provider can be changed without rewriting workflows.
See agent provenance running on your workflow.
Governance, approval, and provable audit built into the control plane, from cloud to air-gap. Request a demo against a workflow you actually run.