Course Outline
Introduction to Devstral and Coding Agents
- Overview of Devstral architecture
- Agentic AI concepts in software engineering
- Use cases for coding agents
Setting Up the Development Environment
- Installing and configuring Devstral
- Integration with Python and Git workflows
- IDE support with Visual Studio Code
Designing Coding Agents
- Defining agent roles and capabilities
- Workflow design for code navigation and refactoring
- Error handling and rollback strategies
Tool and API Integration
- Connecting agents to developer tools
- API integration for external services
- Automation patterns with coding agents
Agentic Workflows in Practice
- Code exploration and documentation generation
- Automated refactoring and testing assistance
- Collaborative coding with agents
Security and Best Practices
- Safe execution environments
- Access controls and permissions
- Monitoring and logging agent actions
Scaling and Maintaining Coding Agents
- Deploying agents across teams and projects
- Maintaining and updating agent workflows
- Continuous improvement with feedback loops
Summary and Next Steps
Requirements
- Strong understanding of Python
- Experience with software development workflows
- Familiarity with APIs and code integration
Audience
- ML engineers
- Developer-tooling teams
- SREs working on developer experience
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2900 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny