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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 ecosystem
- Key features and competitive differentiators
Agent Design Principles
- Defining the core components of an AI agent
- Establishing agent roles, memory mechanisms, and tools
- Differentiating between enterprise and developer-centric agents
Hands-On with Mistral Medium 3
- Model setup and configuration
- Tuning and optimizing inference
- Handling multimodal and coding workflows
Building with Devstral
- Code-first agent design strategies
- Integrating Devstral for code comprehension
- Best practices for engineering assistants
Le Chat Enterprise Integration
- Deploying Le Chat for enterprise-grade agents
- Implementing RBAC, SSO, and compliance integration
- Connecting enterprise applications and data stores
End-to-End Agent Workflows
- Combining Mistral Medium 3, Devstral, and Le Chat
- Creating multi-tool workflows (connectors, APIs, data sources)
- Applying grounding and RAG patterns
Deployment and Governance
- Comparing self-hosting vs API deployment
- Monitoring, logging, and observability
- Considerations for cost, performance, and compliance
Summary and Next Steps
Requirements
- A solid understanding of Python programming
- Experience with machine learning workflows
- Familiarity with APIs and model integration
Audience
- AI engineers
- Solution architects
- Applied ML teams
- Product developers
14 Hours
