Introduction to Mistral Workflows

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 still points to Mistral Large 3 according to its official specification (verified November 3, 2026), just as 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.

Orchestrating Complex AI Tasks

A serious AI application never fits inside a single API call. Consider a service that generates a customer report every night: it pulls data from three systems, sends it to a model for processing, emails the result, waits for a manager’s approval, and then triggers a billing process. This chain spans hours, sometimes days, crossing networks, queues, and third-party services—none of which are 100% reliable.

The day the machine running this pipeline reboots between sending the email and processing the bill, you face two outcomes. Either you restart everything from scratch and the customer receives their report a second time, or you have manually written a system of checkpoints, locks, and state tables—hundreds of lines of code that have nothing to do with your core business. The Mistral AI Workflows platform, launched in Public Preview on April 27, 2026, exists to spare you this choice. It provides a fundamental guarantee: your workflow will never lose its progress.

What Is a Durable Workflow?

A durable workflow is a program where every step is persisted before the next one begins. Take the nightly report again: the moment the email goes out, the platform has already logged in its journal that this step is complete and recorded the result. If the process stops a second later, it resumes at the billing step, not at the beginning—without data loss or duplicate actions.

This mechanism is not experimental. The Mistral Workflows platform is powered by Temporal, an orchestration engine proven in production at companies like Netflix, Uber, and Stripe. You inherit an execution model that has already withstood large-scale failures, wrapped in a Mistral layer that exposes it via modern Python.

Steps in a Typical Workflow

Before diving into the details of the following lessons, here is the complete journey you will take, from defining code to monitoring in production.

Key Platform Capabilities

Execution is reliable and fault-tolerant. Workflows never lose progress because every step is persisted before moving to the next. Whether it’s a server crash, a network outage, or a transient error from a provider, the workflow automatically resumes exactly where it left off, without any intervention from you.

The platform is also designed for AI-native scenarios. An agent chaining tool calls, a sequence of models, or a long-running conversation with an assistant: these patterns involve long waits and frequent resumptions, which is exactly the target use case. Workflows can last from a few seconds to several weeks, enabling scenarios impossible with a simple HTTP request, such as waiting three days for human validation.

The architecture is also fully observable end-to-end. Every action—activity completion, signal, error—is recorded as a real-time event. You can stream these events to external systems, allowing you, for example, to show the end user a progress bar that reflects the actual state of processing rather than a decorative animation. Finally, the Python SDK uses familiar decorators and async/await patterns: if you know Python, you already know how to write workflows.

Concrete Use Cases

Four families of applications come up regularly. A document processing pipeline chains extraction, analysis, summarization, and classification, with every document passing through the same steps so that a failure in the middle of a batch doesn’t force a full restart. A customer support agent retrieves context, generates a response, escalates to a human if necessary, and follows up on post-resolution tasks days after the ticket was closed. A content moderation workflow has multiple models analyze the same item, applies a majority vote, and escalates to a human moderator in case of disagreement. Finally, report generation collects data from multiple sources, aggregates it, produces the text, formats it, and sends the document.

Public Preview: What This Means

The Workflows feature has been in Public Preview since its launch on April 27, 2026 (official announcement), and this status has practical consequences. Before you begin, check the current status in the Workflows documentation. APIs and features may change without notice: pin your dependency versions and plan to review release notes before every update. The platform is production-ready but requires caution, meaning you should keep a fallback plan for critical processes. User feedback directly influences the roadmap, so your input carries real weight. Some technical limitations still exist, and we detail them in the following lessons rather than glossing over them.

Key Takeaways

• Mistral Workflows let you orchestrate complex AI tasks with a durability guarantee
• The platform is powered by Temporal, an orchestration engine proven in production
• The architecture relies on three components: activities (real work), workflows (orchestration), workers (execution)
• Every step is persisted—an interrupted workflow resumes automatically
• The feature is in Public Preview (April 2026): APIs are subject to change