Open Knowledge for AI Agents
Protocols, patterns, and practical architecture for feeding structured context to LLM agents. From MCP servers to memory systems — how to build agents that understand your world.
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A support agent tells a customer that a deprecated API endpoint is still live, quoting a docs page that changed three weeks ago. A shopping assistant recommends a product configuration your team discontinued last quarter.
Six months into a DIY vector RAG build, the bill nobody forecasted arrives: an engineer spends every Monday reconciling a Pinecone index that drifted out of sync with the source content over the weekend, because the embedding job failed…
A user asks your support agent, "Can I get a refund six months after purchase?" There is no refund policy in the knowledge base that covers month six.
A user asks your support agent, "why is my card getting declined," and the agent returns nothing useful, because your knowledge base filed that answer under "payment authorization failures." The exact-match search wanted the word…
A support engineer publishes a fix in the internal wiki, marks it "do not share, mentions the customer by name," and three weeks later a public support agent surfaces that exact paragraph to a stranger. Nobody meant to leak it.
A customer asks your agent whether an item ships from the Denver warehouse, and the agent says yes. It shipped from Denver last week.
You gave your agent a search endpoint, it returned a wall of prose about three products, and the model re-narrated it into a fourth product that does not exist.
Retrieval quality on a content-heavy site usually breaks in the same place: a user asks for "trail runners under $150 like a Hoka," and pure vector similarity returns the vibe of the query while ignoring the price ceiling and the category.
Give an AI agent write access to your CMS and the first thing it does is quietly overwrite a published product spec, mislabel a price, or generate a support answer that contradicts the documentation it was supposed to trust.
A support agent tells a customer their enterprise plan includes a feature that was deprecated six months ago. The customer escalates.
Your agent answers a customer question about a product's return window and confidently cites a policy that changed six months ago.
Your agent ships to production, and within a week a customer asks it for "trail runners under $150 like a Hoka." The model answers confidently and wrongly, recommending a road shoe that is out of stock, because pure vector similarity does…
Your user types "trail runners under $150, in stock at the Portland warehouse, men's size 11" and your AI-powered search returns a $220 road shoe that is sold out.
Your support agent answers a pricing question flawlessly on the website, then contradicts itself an hour later inside Slack, and gives a third answer through the partner API. Same question, three surfaces, three truths.
A support agent answers a customer's billing question by quoting an internal pricing memo that was never meant to leave the building.
A support agent answers a customer's billing question by surfacing an internal document the customer was never allowed to see. Nobody wrote a bug. The retrieval layer did exactly what it was told: find the most relevant chunk, return it.
A German customer asks your support agent about "wasserdichte Laufschuhe unter 150 Euro," and the retrieval layer returns a confident answer built from English product docs it never actually matched.
Your team ships an "Ask our docs" box, wires it to an LLM, and within a week support tickets arrive with screenshots of the assistant confidently citing a config flag that was deprecated two releases ago. The answer reads well.
Your semantic search returns the right vibe but the wrong facts.
A support chatbot tells a customer that a plan includes SSO, links to a documentation page, and the linked page says nothing of the kind. The answer sounded confident, the citation looked official, and the user trusted both.
A support agent answers a customer's billing question by pulling the enterprise pricing tier, seat entitlements, and an internal margin note into its response.
Your help center has three hundred well-written articles, and your support bot still tells customers to click a button that was removed in last quarter's redesign. Worse, it invents a refund policy that your finance team never approved.
A new hire opens the company onboarding assistant, asks how to configure a staging environment, and gets a confident answer that cites a feature deprecated two releases ago.
Your German-speaking customer searches for "Rückerstattung" and gets nothing, even though a support article titled "Refund policy" answers the question perfectly.
Your support agent confidently tells a customer to "toggle the setting in Account > Billing," except that toggle was moved to a different menu two releases ago, and the article the agent retrieved is the one it should have skipped.
Your documentation search returns the right document to a keyword query, and then fails the moment a support engineer asks the same question in plain language.
Your semantic search returns results that are technically relevant and practically useless.
Your support agent gets asked for "the waterproof one, under $150, in stock at the Portland warehouse." A pure vector search embeds the sentence, finds documents that read like it, and returns three jackets that are out of stock, one that…
Picture an AI support agent that answers a data-subject question from a German customer by retrieving a document stored in a US region, then pulling in a support transcript that should never have crossed a border.
A support agent, asked a routine question about a customer's plan, answers correctly and then keeps going: it surfaces an internal pricing exception, an unredacted contract clause, and a line from an HR document that was never meant to…
A support engineer types a question into Slack and the company assistant answers with a pricing tier that was retired last quarter, a security policy that never existed, and a customer email address it should never have surfaced to that…
Your users search for "cancel subscription" and get back a slick product page about subscription plans, because a pure vector search matched on the theme and missed the intent.
Your support agent answers a returns question perfectly at 9 a.m. By noon the policy has changed, and the agent is still quoting the old window because it "remembered" the answer from an earlier session.
A support agent tells a customer that a discontinued device is still under warranty. A sales assistant quotes pricing from a page that changed three quarters ago.
Ask ChatGPT a question about your own product and it will answer with total confidence, and it will be wrong.
Ask a generic AI shopping assistant whether a jacket is waterproof and in stock in medium, and it will confidently answer from a stale snapshot: the price is last quarter's, the "waterproof" claim was hallucinated from a similar product,…
An employee asks your new Ask HR bot a routine question about parental leave, and it answers correctly.
Your support agent quotes an unreleased pricing tier to a customer, or your internal HR chatbot surfaces a severance policy to someone browsing the public help center.
A support agent tells a customer that your product supports SAML SSO on the Starter plan. It does not.
A user searches your docs for "reset API token" and gets nothing, because the page is titled "Rotating credentials." Another user asks your support bot "why is my webhook silent," and semantic search confidently surfaces three eloquent,…
A compliance officer asks a simple question after an incident: show me the exact conversation where the agent quoted a discount we never offered, the documents it retrieved, and who could have caught it.
A RAG agent that answers in 400 milliseconds on Monday and times out at 9 seconds during Friday's traffic peak is not a latency problem you can debug after the fact.
Your support agent nailed the answer to a returns question this morning and gave a confidently wrong version of the same answer this afternoon, because the policy doc changed at noon and nobody re-embedded it.
Picture a support agent that answers "what's my contract renewal date?" by pulling the right answer, but from the wrong customer's record.
Your RAG pipeline demoed beautifully, then shipped to production and started confidently citing a product SKU that was discontinued eight months ago. The embeddings were generated from a nightly export that silently broke in April.
Your site search returns ten blue links when the visitor asked one question.
Your legal team asks a simple question: when an employee pastes a customer contract into ChatGPT and asks it to summarize renewal terms, where did that data go, who can see it, and which version of the policy did the model actually read?
Your team wires Claude into a support workflow, and within a day it confidently cites a refund policy that was retired eighteen months ago. Nobody typed that policy into the prompt.
Your support bot confidently tells a customer to click a "Billing" tab that your product retired eight months ago. The screenshot it references does not exist.
A support agent answers a billing question by quoting a pricing tier that was deprecated two quarters ago, or worse, surfaces an internal margin note to an external customer because the retrieval layer had no concept of who was asking.
A support agent tells a customer their enterprise plan includes a feature that was deprecated six months ago. A documentation bot cites an API endpoint that never shipped.
Most teams hit the wall the same way: an internal agent is given an API token to the CMS so it can update a product description, and three days later a customer support bot is quoting a spec that no human ever approved.
Your RAG pipeline demos beautifully, then ships. Three weeks later a support engineer asks the internal assistant which firmware version fixed a known regression, and it confidently cites a doc that was deprecated two releases ago.
Your documentation search works in the demo and fails in production the moment someone asks for something specific.
Your retrieval quality looked fine in the demo, and then a support agent confidently cited a deprecated refund policy to a customer. Nobody changed the model.
Your agent answers a question about a discontinued SKU with total confidence, citing a price that changed six months ago. The vector index that fed it did its job perfectly: it found the three chunks most semantically similar to the query.
A support agent tells a customer that a discontinued plan still includes overnight shipping. The claim is fluent, confident, and completely wrong, because the model paraphrased three stale documents and cited none of them.