Most AI agent projects that stall do so because of knowledge, not models. The agent cannot find the right policy, sees a document it should not, or answers from a version that expired last quarter.
People tolerate a stale page and skim around it. Agents treat whatever they retrieve as truth and then act on it. That is why a knowledge base for agents has to be run as production infrastructure, with owners, SLAs and tests, not as a folder of embeddings.
No single retrieval method wins. Start with the baseline and add layers only where your evaluation set shows it failing.
Run keyword and vector search together, fuse the results and rerank the top candidates. It catches both paraphrases and exact terms like part numbers and error codes. This is the baseline for almost every production system.
Builds an entity graph from the corpus and pre-summarizes communities of related entities. It answers whole-corpus questions such as "what are the main risks across our vendor contracts" where standard RAG gives thin answers. Expect higher ingest cost and graph upkeep.
The agent decides when and what to retrieve, decomposes questions, calls several indexes or tools and retrieves again when evidence is thin. Best for multi-step, multi-source tasks, but it uses more tokens and is harder to test. Cap loop depth.
Tie every source to an agent task, an owner, a sensitivity class and a permission model before you index anything.
Use layout-aware parsing, structure-aware or contextual chunking, rich metadata and one stable, versioned embedding model.
Start with hybrid search plus reranking, add GraphRAG or agentic RAG where tests justify it, and keep agent memory separate.
Track recall and faithfulness in CI, log every agent turn, map controls to NIST and SOC 2, and report cost per resolved task.
A governed retrieval layer over company content. It ingests documents, tickets, wikis and records, splits them into indexed chunks with metadata and permissions, and returns only the relevant, authorized and current passages an agent needs. It also keeps logs for audit.
RAG retrieves relevant chunks and passes them to a model. GraphRAG builds an entity graph and community summaries to answer whole-corpus questions. Agentic RAG lets the agent decide when and what to retrieve across several sources and steps.
Store each source's allowed users and groups on every chunk and filter at query time using the caller's identity from your identity provider. Retrieve on behalf of the user, isolate tenants and regulated data, sync revocations on a stated schedule and include leakage tests in evaluation.
Chunk along the document's structure first, with headings, sections and tables kept intact. Use parent-child chunking for long technical documents and contextual chunking where chunks lose meaning on their own. Test on your own queries.
Measure retrieval and generation separately. Track recall@k and ranking for retrieved chunks, then faithfulness and relevance for answers, on a fixed test set rerun in CI on every change. Calibrate any LLM judge against human-labeled examples.
Memory stores what the agent learned while working, such as user preferences and outcomes of past tasks. The knowledge base stores what the company knows. Keep them separate, and promote learnings into shared content only through human review.
Buy enterprise search when content sits in common SaaS tools and staff need fast, permission-aware answers. Build on a cloud knowledge base service or vector database when agents need custom retrieval or tight control over cost and data residency. Most enterprises blend both.
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