How to Ground Agents With Your Own Knowledge
Why naive RAG fails in production and how token chunking, cosine thresholding, and verifiable source citations eliminate conversational hallucinations.
Retrieval-Augmented Generation (RAG) is the foundational architecture of accurate AI agents. Yet many teams struggle with low recall, irrelevant context retrieval, and lingering hallucinations.
Why Naive RAG Breaks Down
Most naive implementations simply take an entire PDF, split it arbitrarily every 1,000 characters, and send the top 3 vector matches to the LLM. This leads to three systemic failures: 1. Truncated context: Important caveats or table columns get split across arbitrary chunk boundaries. 2. Diluted relevance: Sending too much irrelevant text causes the model to overlook critical facts in the middle of the prompt. 3. Ghost citations: Without granular passage tracking, users cannot verify which document actually supported the answer.
The AgentFlow Grounding Standard
In AgentFlow, knowledge ingestion follows an optimized pipeline: - Smart structural chunking: Documents are split along semantic headers, paragraphs, and markdown tables with a 64-token overlap. - Vector embedding with text-embedding-3-small: Fast, cost-efficient, and highly accurate for multi-lingual semantic search. - Inline traceable citations: Every response returns citation markers linked directly to the exact chunk ID and source document page.
Building Your First AI Agent: From Blank Canvas to Production
A practical, step-by-step walkthrough of building an autonomous agent in AgentFlow. Learn how to connect vector knowledge bases, register action tools with strict parameter schemas, and test responses in a live sandbox before deploying across channels.
Giving AI Agents Tools They Can Actually Use
Moving beyond conversational assistants. How deterministic JSON schemas, human confirmation gates, and webhook bindings turn agents into workflow execution engines.