> ## Documentation Index
> Fetch the complete documentation index at: https://open-index.io/llms.txt
> Use this file to discover all available pages before exploring further.

# How to ensure provenance in your AI agents

> Why users can't trust context they can't trace, and how per-fact attribution and citable retrieval build a proof-of-work agents can defend.

Users don't trust an agent because it sounds confident. They trust it when they can
**see why it did what it did** — the specific piece of context behind the action.
Provenance is that trail. Without it, every answer is "trust me," and adoption stalls
exactly where the stakes are highest: legal, healthcare, finance, infrastructure.

## The problem provenance solves

As you collate more documents, finding *which* reference the agent used gets harder:

* The agent read across several documents; which sentence actually drove the action?
* Was that fact current, or an old copy from a stale file?
* Who asserted it, and how confident should we be?

When you can't answer those on demand, you can't put a **proof-of-work** in front of
the user — and without proof-of-work, they can't trust the output. That single gap
caps how much you can hand off to the agent.

## Attribution belongs on the fact, not the document

The fix is to attach provenance to each **fact**, so it travels with the fact into
every answer:

<Steps>
  <Step title="Address every fact">
    Each entity has a stable id. An answer can cite `service:checkout` precisely —
    not "somewhere in the payments folder." → [entities](/concepts)
  </Step>

  <Step title="Record who asserted it, and how confidently">
    Attach the source and confidence when a fact is written — a connector, an import,
    an agent, a human. In [Open Index](/agents/mcp), writes carry a provenance block
    (`asserted_by`, `confidence`), and bulk imports attribute a whole batch at once
    (`--asserted-by import:jira`). → [populating entities](/guides/populating-entities)
  </Step>

  <Step title="Make retrieval citable">
    Because the agent retrieves discrete, addressed records — not a blob of merged
    text — every retrieved fact can be shown to the user with its id and source. The
    proof-of-work assembles itself from what was actually fetched. →
    [Context search accuracy](/guide/context-search-accuracy)
  </Step>
</Steps>

## Provenance is also an audit tool for *you*

The same trail that builds user trust lets you debug the agent. Local fetch analytics
record what context each client retrieved, how often, which queries returned nothing,
and where latency or failures cluster. When an agent acts wrong, you can see the
context it pulled — instead of reconstructing what it "must have" used. →
[MCP analytics](/agents/mcp)

<Note>
  Zero-result searches are a gift: they show precisely what your users asked for that
  your knowledge base couldn't answer — i.e. what to model next.
</Note>

## Checklist

* [ ] Every fact is individually addressable and citable.
* [ ] Writes carry source + confidence (who asserted this, how sure).
* [ ] The agent can produce a proof-of-work listing the exact records it used.
* [ ] You can audit, after the fact, what context an action was based on.

<Card title="Next: Human steering" icon="hand" href="/guide/human-steering">
  Reducing the autonomy tax now that context is trustworthy.
</Card>