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Duration 14 hours
Course Outline
Introduction to Devstral and the Landscape of Coding Agents
- Overview of Devstral’s architectural foundations
- Understanding agentic AI concepts within software engineering
- Exploring key use cases for coding agents
Preparing the Development Environment
- Installation and configuration of Devstral
- Integrating with Python and Git workflows
- Leveraging IDE support through Visual Studio Code
Architecting Coding Agents
- Defining specific agent roles and functional capabilities
- Designing workflows for code navigation and refactoring tasks
- Implementing robust error handling and rollback strategies
Integrating Tools and APIs
- Linking agents to essential developer tools
- Connecting to external services via API integration
- Applying automation patterns using coding agents
Practical Application of Agentic Workflows
- Performing code exploration and generating documentation
- Assisting with automated refactoring and testing processes
- Engaging in collaborative coding sessions with agents
Security Considerations and Best Practices
- Establishing safe execution environments
- Managing access controls and permission sets
- Implementing monitoring and logging for agent actions
Scaling and Maintenance of Coding Agents
- Deploying agents across multiple teams and projects
- Maintaining and updating established agent workflows
- Driving continuous improvement through feedback loops
Conclusion and Future Directions
Requirements
- Solid proficiency in Python
- Practical experience with software development workflows
- Understanding of API mechanics and code integration
Target Audience
- ML engineers
- Teams focused on developer tooling
- SREs dedicated to enhancing developer experience
Testimonials (1)
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