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Course Outline
Introduction to Devstral and Coding Agents
- A high-level overview of Devstral’s architecture
- The role of agentic AI concepts in software engineering
- Practical use cases for coding agents
Establishing the Development Environment
- Installation and configuration of Devstral
- Seamless integration with Python and Git workflows
- IDE support via Visual Studio Code
Architecting Coding Agents
- Defining specific agent roles and capabilities
- Designing workflows for code navigation and refactoring
- Implementing error handling and rollback strategies
Tool and API Integration
- Linking agents to essential developer tools
- Integrating APIs for external service connectivity
- Employing automation patterns with coding agents
Agentic Workflows in Action
- Exploring code and generating documentation
- Supporting automated refactoring and testing
- Facilitating collaborative coding with agents
Security and Best Practices
- Creating safe execution environments
- Managing access controls and permissions
- Monitoring and logging agent actions effectively
Scaling and Maintaining Coding Agents
- Deploying agents across various teams and projects
- Maintaining and iterating on agent workflows
- Achieving continuous improvement through feedback loops
Summary and Next Steps
Requirements
- A robust command of Python
- Practical experience with software development workflows
- Knowledge of APIs and code integration processes
Audience
- ML engineers
- Developer-tooling teams
- SREs focused on enhancing developer experience
14 Hours
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
