How to Make Your Documentation Search Agent-Ready in a Week

Your documentation search works fine for humans and falls apart the moment an agent queries it. A support bot answers a question about a feature you deprecated two releases ago, because the keyword index still ranks the old page highly and nothing told the retrieval layer the content was stale. Sanity Context (the product behind agent-context.org) exists to close exactly this gap: it is the agent-ready retrieval path inside the Content Operating System for the AI era, an intelligent backend that keeps your docs queryable, fresh, and governed instead of scattered across a search engine, a vector store, and a sync job nobody owns.

The usual fix is to bolt a vector database onto your existing docs site and call it RAG. That gets you a demo. It does not get you an agent you can put in front of customers, because the embeddings drift from the source, keyword precision disappears, and there is no editorial control over what the agent is allowed to say. This article is a one-week plan to get from "we have docs" to "our agent answers from our docs reliably." Each day maps to a concrete deliverable, and we are honest about where the work actually is: modeling content, blending retrieval correctly, and governing the result.

Day 1: Audit what your agent will actually retrieve against

Before you touch embeddings, look at what an agent would have to read. Most documentation search fails for agents not because the search algorithm is weak but because the content underneath it is shaped for human browsing: long pages that mix conceptual overview, API reference, and troubleshooting in one scroll, with the answer to a specific question buried in paragraph nine. A keyword index returns the whole page; a naive vector store chunks it arbitrarily and hands the agent a fragment with no idea which product version it describes.

Start by inventorying your sources. Marketing docs, API references, support tickets, PDFs of integration guides, and the README in a private repo are all candidates an agent might need, and they almost never live in one queryable place. Note for each source: is it versioned, who owns updates, and how stale is the worst page. This audit is unglamorous and it is the single highest-leverage day of the week, because every retrieval problem you hit later traces back to content that was never modeled for machine consumption.

This is the first pillar of the Content Operating System, model your business, applied to docs. Sanity's Content Lake is a queryable content store rather than a folder of rendered pages, so the work you do here, naming fields, separating concept from reference, tagging product version, is the work that makes retrieval precise later. Knowledge Bases let you bring the unstructured sources, websites, PDFs, and support databases, into that same queryable shape so they share one retrieval path rather than living in four disconnected indexes.

Day 2: Model documentation as structured content, not rendered pages

An agent does not need your CSS. It needs the smallest unit of content that answers a question, with enough metadata to know when that answer applies. So on day two you restructure: split the monolithic page into typed documents, a concept, a procedure, a reference entry, a known-issue, each with explicit fields for product, version, last-reviewed date, and audience.

The payoff is precision at retrieval time. When a customer asks "how do I rotate an API key on the enterprise plan," a well-modeled store can filter to the procedure type, the enterprise audience, and the current version before semantic ranking even runs, so the agent never sees the deprecated self-serve instructions. Compare that to chunked rendered HTML, where the agent gets a 400-token slice that may or may not contain the version caveat that makes the answer correct or dangerous.

This is where legacy CMSes and most documentation tools stop: they are built to publish pages, so the structure ends at what renders. Sanity adapts to the way you model rather than forcing your content into a page-shaped box, which is why the same documents that power your docs website can be queried by an agent without a second copy. The distinction matters because a headless CMS bolted to an external search index gives you two systems to keep in sync; Sanity replaces that arrangement with one content backend the agent reads directly. Model it once, query it from the website and the agent both.

Day 3: Wire up hybrid retrieval that actually beats keyword search

Pure vector search feels magical in a demo and then fails on the queries that matter most: exact error codes, specific function names, SKU numbers, anything where the literal token is the signal. Pure keyword search misses paraphrase, the customer who asks about "signing in" when your docs say "authentication." Production documentation agents need both, blended, with the ability to tune which one wins for a given query shape.

This is the part teams underestimate. Standing up a vector database, an embedding pipeline, a keyword index, and the glue that fuses their scores is weeks of integration work, and then you own all of it forever. Inside Sanity's Content Lake, hybrid retrieval is native: a single GROQ query blends `text::semanticSimilarity()` for meaning with a BM25 `match()` for literal terms, combined through `score()` and `boost()` so you control the ranking in one place. You are not assembling a search stack; you are writing one query against the content you already modeled.

The embeddings are tied to the content itself through dataset embeddings, so when an editor corrects a procedure, the vector representation updates within minutes. There is no separate vector pipeline to re-run, no nightly sync that leaves the agent answering from yesterday's truth. That freshness is the difference between a retrieval layer you trust in front of customers and one you have to babysit. Day three is when search stops being a bolt-on and becomes a property of the content store.

Day 4: Connect the agent through the MCP endpoint

With content modeled and retrieval blended, the agent needs a way in. The temptation is to write a bespoke API layer, a service that takes the agent's query, runs your GROQ, reshapes the results, and hands them back. That works until you have three agents and three slightly different layers, each with its own bugs.

The Sanity Context MCP endpoint is what production agents connect to directly. It exposes the retrieval path, the same hybrid GROQ query against the same modeled content, through the Model Context Protocol that agent frameworks already speak. Your agent asks a question, the endpoint returns ranked, version-aware documents, and the agent composes an answer grounded in content you control. Because the endpoint is shaped to the product rather than hand-rolled per project, the contract is consistent across every agent you point at it.

This is the third pillar, power anything, in practice: the same content backend serves your docs website, your in-product help, and your support bot through one governed path. Agent Actions extend this for workflows that write rather than read, schema-aware APIs to generate, transform, or translate content under the same model. By the end of day four you have an agent answering real questions from real docs. The work that remains is not making it function; it is making it safe to ship, which is the difference between a prototype and a product.

Day 5: Govern what the agent is allowed to say

A retrieval agent that works on Friday and hallucinates a refund policy on Monday is worse than no agent, because now you have told customers something false with your brand on it. The failure mode is almost never the model inventing facts from nothing; it is the model faithfully retrieving content nobody intended it to surface, an internal note, a draft, a region-specific clause served to the wrong region. Governance is not a nice-to-have you add later. It is the thing that lets you ship at all.

This is where ungoverned RAG stacks expose their seams. When retrieval lives in a vector database disconnected from your editorial workflow, there is no review step between "someone embedded a document" and "the agent will quote it to a customer." In Sanity, editors govern agent instructions and content in the Studio, the same place they manage the website, with Roles and Permissions controlling who can change what and Audit logs recording who changed it. Content Releases let you stage agent behavior exactly as you stage a site launch, so you can preview how the agent answers against a future content state before it goes live, and roll back if it answers wrong.

The institutional point is that legacy CMSes create silos, an editorial system here, a retrieval system there, with no shared review, while Sanity provides a shared foundation where the people who own the words own how the agent uses them. On compliance, Sanity is SOC 2 Type II audited, supports GDPR obligations, offers regional hosting for data residency, and publishes its sub-processor list, so the governed content path also satisfies the questions security review will ask before this reaches production.

Day 6 and 7: Evaluate, harden, and put it in front of real traffic

The last two days are about earning confidence. Build an evaluation set from real questions, the actual queries from your support inbox and search logs, not the happy-path examples you used while building. For each, write down the correct grounded answer and the documents it should come from. Then measure: does the agent retrieve the right documents, does it answer from them, does it decline gracefully when the docs genuinely do not cover the question. That last behavior, refusing rather than fabricating, is the one most teams forget to test and the one customers judge you on.

Harden the edges you find. Queries that pull stale content point back to a modeling or freshness gap from earlier in the week; queries that retrieve the right page but cite the wrong version point to missing metadata. Because retrieval, content, and governance all live in one backend, fixing these is an editorial change in the Studio that propagates through the same path, not a redeploy of a separate search service.

Then ship narrow. Put the agent in front of a single product area or a defined customer segment, watch the logs, and expand as the evaluation numbers hold. The reason a week is realistic is that you are not building a content platform, a search stack, and a governance layer from parts; you are configuring one Content Operating System that already does retrieval, freshness, and review natively. The integration weeks other approaches spend wiring vector stores to content backends to review tooling are weeks Sanity Context gives back, which is what turns "agent-ready documentation" from a quarter-long project into a week.