What Is an AI Agent?

Update November 2, 2026 — Models. Mistral Large 3 (mistral-large-2512) and Mistral Medium 3.5 were deprecated on October 31, 2026, and will be retired on April 30, 2027. Their designated successor, Mistral Large 4 (mistral-large-4), has been in public preview since October 6. The examples in this course use the alias mistral-large-latest, which currently points to Mistral Large 3 according to its official specification sheet (verified November 3, 2026). Similarly, mistral-medium-latest points to Mistral Medium 3.5. These aliases will remain valid until April 30, 2027. For new projects, test mistral-large-4, their designated successor.

AI Agents: Beyond the Simple Chatbot

AI agents represent a major evolution in how we use large language models. A standard chatbot receives a question, returns an answer, and the interaction ends: nothing carries over to the next call, and the model has no way to fetch information it doesn’t already know. An agent, by contrast, is an autonomous system capable of planning, executing actions, and pursuing a complex goal over multiple steps.

With the Mistral Agents API, you get a complete framework for building these intelligent systems directly via the API. You don’t need to deploy extra infrastructure or write your own orchestration loop.

What Exactly Is an Agent?

An agent is an LLM-powered system that starts with high-level instructions and figures out how to fulfill them. It first plans a sequence of actions: ask it to compare three hosting plans, and it decides to examine each one separately before drafting a summary. It uses tools—web search, code execution, image generation—because text alone can’t verify a current price or validate a calculation. It maintains state across multiple turns, so your user’s third question naturally inherits context from the first two without you needing to resend the history. Finally, it collaborates with other agents when a task exceeds its specialty: the writer isn’t necessarily the calculator.

The fundamental difference from a simple chat.complete() call comes down to one word: the agent loops. It analyzes the situation, decides on an action, observes the result, and repeats until it reaches its goal.

The Perception-Action Loop

This cycle isn’t just a teaching abstraction: it’s exactly what happens between the moment you send a request and the moment you receive a response. Concretely, for the question “Which cloud plans suit my application?”, the agent perceives the request, reasons that it lacks pricing data, acts by launching a search, observes the pages it finds—and loops again because it still needs a performance comparison. The key takeaway is the exit condition: the model, not you, decides when the loop is finished.

Capabilities of the Mistral Agents API

The Agents & Conversations API covers this entire cycle. It relies on multimodal models that handle both text and images, specifically mistral-medium-latest and mistral-large-latest. It maintains persistent state: conversation history lives on the server, so you don’t have to implement your own message storage. It provides built-in tools ready to use—web search, code interpreter, image generation, document library—without requiring you to host any executors. It supports handoffs, meaning it can transfer control from one agent to another that’s more specialized. And it lets you choose the execution mode: single model, single agent, or multi-agent orchestration, depending on the actual complexity of the task.

Supported Models

As of today (April 2026), two models are compatible with the Agents API. mistral-medium-latest offers a good balance of performance and cost, making it the default choice for most production agents. mistral-large-latest targets maximum performance on complex tasks: multi-step reasoning, multi-agent orchestration, and long analyses. Start with the first, and switch to the second only when output quality measurably justifies it.

What You Will Learn

This course follows the natural progression of a project. You’ll start by creating and configuring agents via the Python API, then learn to manage persistent conversations with state. Next, you’ll leverage the four built-in tools—web search, code interpreter, image generation, and document library—before orchestrating multi-agent workflows with handoffs. The course concludes with securing your agents using guardrails, a step teams often postpone but that is essential for any production deployment.

Key Takeaways

• An AI agent is an autonomous system that plans, acts, and observes in a loop
• The Mistral Agents API handles server-side orchestration—you don’t have to implement the loop yourself
• Agents can use tools and collaborate with each other via handoffs
• Two models are currently supported: mistral-medium-latest and mistral-large-latest