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Vector RAG often fails when AI agents need exact technical answers. Chunking splits related rules apart and burns tokens on noisy retrieval. Context MCP fixes this by exposing a structured knowledge base directly to your agent. Breakdown in the reply 👇

Production AI governance isn't just prompt guardrails. If unapproved doc edits can silently overwrite your retrieval layer, your agent isn't safe for enterprise workflows. Keep your knowledge base verified and auditable. Guide in the reply 👇

Vector similarity search often returns half-broken chunks when AI agents need exact rules. With Sanity Knowledge Bases and Context MCP, agents read the structured outline first and fetch complete Markdown entries by path. Breakdown in the reply 👇

Context MCP: Give AI Agents Precise Knowledge Base Access. Connect AI agents to a knowledge base with Context MCP. Load the outline, choose exact entry paths, and read full Markdown entries, without relying on similarity search to find the right chunk.

Sending sensitive patient data to a cloud LLM API just to parse a voice request is a massive HIPAA compliance headache. With local Speech-to-Text and a 12B Gemma model, you can execute tool calls 100% offline on-device. Breakdown in the reply 👇

Instead of letting an agent guess which vector chunk matters, start with two things in the config: a sharp dataset purpose and a focused GROQ projection. That produces a clean outline for direct entry-path reads via Context MCP. See it in the reply 👇

Offline On-Device AI Agents with Gemma & Local Tool Calls (feat. @howdevelop) How do you run an agentic healthcare workflow without sending private patient data to the cloud? Run local Speech-to-Text + a 12B Gemma model on-device to execute pharmacy tool calls 100% offline.