Knowledge Base Design for Enterprise AI Agents
A well-designed Knowledge Base gives agents accurate, retrievable information. A poorly designed one causes hallucination and incorrect answers that erode user trust.
What Is an Agent Knowledge Base?
An agent Knowledge Base is a curated collection of documents, policies, and reference information that the agent can query when it needs to answer questions or make decisions. It's the agent's reference library — the difference between an agent that gives accurate policy answers and one that hallucinates policy that doesn't exist.
What Belongs in a Knowledge Base
- Process documentation — step-by-step guides for the processes the agent supports
- Policy documents — the rules and thresholds the agent must enforce
- FAQs — common questions and accurate answers in the domain
- Reference data — lookup tables, approval thresholds, standard terms
What Does NOT Belong in a Knowledge Base
- Real-time data — use Skills (live API calls) for data that changes. Knowledge Bases are for stable reference content.
- Confidential data beyond the agent's access scope — the Knowledge Base is accessible to the agent's full context, so govern its contents accordingly
- Poorly maintained documents — outdated content is worse than no content. An agent citing an outdated policy erodes trust.
Chunking and Retrieval Design
Documents in the Knowledge Base are chunked for retrieval. Chunk size affects retrieval quality: chunks that are too small lose context, chunks that are too large retrieve too much noise. Use semantic chunking where possible — break documents at natural topic boundaries, not at fixed character counts.
Keeping Knowledge Bases Current
Every Knowledge Base needs an owner and a review cadence. Quarterly reviews for stable reference content. Immediate updates when policy changes. An agent citing an outdated travel policy is a trust problem, not just a content problem.