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Course Outline
Foundations of Devstral and Coding Agents
- Architectural overview of Devstral
- Concepts of Agentic AI in software engineering
- Key use cases for coding agents
Preparing the Development Environment
- Installation and configuration of Devstral
- Seamless integration with Python and Git workflows
- Support within Visual Studio Code
Architecting Coding Agents
- Defining specific agent roles and capabilities
- Designing workflows for code navigation and refactoring
- Strategies for error handling and rollback
Connecting Tools and APIs
- Linking agents to essential developer tools
- Integrating external services via APIs
- Implementing automation patterns with agents
Applying Agentic Workflows
- Exploring code and generating documentation
- Assisting with automated refactoring and testing
- Collaborative coding sessions with agents
Security Standards and Best Practices
- Creating safe execution environments
- Managing access controls and permissions
- Monitoring and logging agent activities
Scaling and Agent Maintenance
- Deploying agents across multiple teams and projects
- Maintaining and updating agent workflows
- Driving continuous improvement via feedback loops
Summary and Future Steps
Requirements
- Proficient knowledge of Python
- Practical experience with software development lifecycles
- Understanding of API interactions and code integration
Intended Audience
- Machine Learning Engineers
- Teams focused on developer tooling
- SREs specializing in 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