Production AI Agents with Mistral AI: OCR, RAG, MCP & Evals

About this course

Unofficial course, not affiliated with or endorsed by Mistral AI or Anthropic. Adapted from the Mistral AI Cookbook (MIT license), with new production material written for this course.

You can call an LLM API. Your demo works. Then real work starts. An invoice scan has the wrong total. An email names a shipment the model cannot find. A tracking note tells your agent to email the whole customer list to a stranger, and with no approval step it obeys in five runs out of five. Most tutorials stop before this point.

This course builds one agent, step by step, until it holds up in production. The agent is Dock, the operations assistant of a made-up freight company. Every section starts from a real failure of Dock, shows how Mistral's tools solve it, and measures the fix with numbers from real runs.

What you build

Structured output you can trust: JSON schemas, validators and retries that turn emails into clean data. Document AI: Mistral OCR on invoices and bills of lading, with checks that catch a wrong total even at 99.9% OCR confidence. Answers with sources: a handbook assistant that cites its sections and says "not found" when it should, and the smallest model that still passes. Agents and handoffs: triage, tracking and billing agents with the Agents API, a routing rule improved from 17 to 19 out of 20. Human approval and an MCP server: every email waits for a person, and the server refuses non-customers even when someone approves. Multi-agent designs: one agent against parallel workers, compared on quality, time, retries and tokens. Guardrails: moderation plus your own business rules, and a judge that quotes the sentence that broke them. Evals: a 30-case scorecard that catches the regression a cheaper model hides.

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