How to index documents into the document store with an AI agent

Only current, owned, approved documents should enter the store that agents answer from. The agent prepares each candidate: it checks the version, extracts owner, effective date and permissions, splits the text for retrieval, flags conflicts with documents already indexed and proposes what to retire. A named content owner approves entry and retirement. Drafts, superseded versions and unvetted uploads stay out.

What is the document store, and who owns it?

The document store is the indexed collection agents search when they answer questions or check rules: policies, procedures, product terms, rate sheets, approved templates. Whatever is in it becomes, to an agent, the firm's view. A superseded policy left in the store is not history; it is a live instruction the agent may follow.

The knowledge manager owns the store and its rules. Each document has a content owner, usually the author's function, who approves its entry and retirement. The agent prepares the work. Indexing is different from filing documents in the document management system, which how to let an AI agent file documents in your document management system covers; the store holds a curated subset that agents may rely on.

What does the agent prepare?

1. Agent, depth 1: finds candidates: new or updated documents in approved libraries of the document management system, published through the normal approval route.
2. Agent, depth 1: checks status and version: final, approved, current. Drafts and documents awaiting approval are excluded.
3. Agent, depth 2: prepares metadata: title, owner, version, effective date, review or expiry date, permission labels from the source, and provenance (an approved policy from the library is sealed; a document uploaded by a member of staff is declared until approved).
4. Agent, depth 2: splits the document into retrievable sections that keep headings and context, so a retrieved passage still says which policy and section it came from.
5. Agent, depth 1: flags conflicts: earlier versions still indexed, other documents that say something different on the same point, and references to documents not in the store.
6. Agent, depth 2: proposes retirements: superseded versions and documents past their review date.
7. Person, content owner: approves entry, checking the metadata and any conflict.
8. Person, content owner: approves retirements.
9. Agent, depth 3: indexes approved documents and removes retired ones, then runs a set of test questions to confirm the right passages come back.

What does a named person approve?

Entry. The content owner confirms the document is the version agents should follow, that the metadata is right, and that its permission labels match who may see it.

Conflicts. Where two documents disagree, only the owners can decide which is right, or whether one must be corrected.

Retirement. Removing a document changes what agents answer. The owner confirms nothing still depends on it.

Exceptions to the rules. Indexing anything outside approved libraries, or any document containing personal data, needs the knowledge manager's approval.

Which controls matter, and what evidence do they leave?

One current version. Indexing a new version retires the old one in the same step. Evidence: the index log showing both actions.

Permissions carried. Each section keeps the source document's permission labels, so agents only retrieve what their users may see. Evidence: labels on indexed sections.

Provenance on every section. Agents can tell a sealed policy from a declared upload. Evidence: provenance metadata.

Instructions inside documents are inert. A document containing text aimed at agents, such as "ignore other policies", is flagged at preparation. Evidence: flags raised and the owner's decision.

Retrieval tests. A fixed set of questions with known answers runs after each indexing batch. Evidence: test results.

Where does it usually go wrong?

Old versions linger. The new policy is indexed but the old one is not removed, and agents answer from whichever ranks higher.

Drafts leak in. A draft in a shared folder is picked up because it looks final. Index only from approved libraries, and check status.

Permissions are lost. Sections from a restricted document become searchable by every agent. Carry labels on every section.

Uploaded files become policy. A member of staff uploads a helpful summary, and agents start citing it as the rule.

Every document is treated alike. A rate sheet that changes weekly and a code of conduct reviewed every few years need different review dates and different checks. Set the review interval per document type, so fast-moving content is never left to age in the store.

Scanned documents are indexed badly. Text read from scans can contain errors that change meaning; extraction quality matters, as discussed in what is intelligent document processing?

What should you measure?

Documents indexed and retired per week, documents past their review date still in the store, conflicts found and resolved, retrieval test results, and agent answers later found to cite a superseded or draft document. The last should be none.

Where Pryme fits

In Pryme, the Pryme Context Protocol stamps every passage reaching an agent with its source and provenance class, so an approved policy is treated as sealed and a staff upload as declared. A related set-up step is connecting an AI agent to your document management system; the wider path is in deploying an AI agent: train, shadow, certify, widen.