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Course Outline

Introduction to the Mistral AI Ecosystem

  • Overview of Mistral models (Medium 3, Le Chat Enterprise, Devstral)
  • Positioning within the agentic AI landscape
  • Key features and distinct advantages

Principles of Agent Design

  • Characteristics that define an AI agent
  • Defining agent roles, memory structures, and tool usage
  • Distinguishing between enterprise and developer-centric agents

Practical Application with Mistral Medium 3

  • Model setup and configuration
  • Inference tuning and optimization techniques
  • Implementing multimodal and coding workflows

Development with Devstral

  • Code-first approaches to agent design
  • Integrating Devstral for enhanced code comprehension
  • Best practices for engineering assistance

Integrating Le Chat Enterprise

  • Deploying Le Chat for enterprise-grade agents
  • Incorporating RBAC, SSO, and compliance measures
  • Linking enterprise applications and data repositories

End-to-End Agent Workflows

  • Combining Mistral Medium 3, Devstral, and Le Chat
  • Constructing multi-tool workflows (connectors, APIs, data sources)
  • Applying grounding and RAG patterns

Deployment and Governance

  • Comparing self-hosting versus API deployment
  • Monitoring, logging, and observability strategies
  • Addressing cost, performance, and compliance factors

Summary and Next Steps

Requirements

  • Proficiency in Python programming
  • Practical experience with machine learning workflows
  • Familiarity with APIs and model integration

Audience

  • AI engineers
  • Solution architects
  • Applied machine learning teams
  • Product developers
 14 Hours

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