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Course Outline
Foundations of Devstral and Coding Agents
- Insights into Devstral's architectural framework
- The role of Agentic AI in software engineering
- Practical applications and use cases for coding agents
Configuring the Development Workspace
- Setup and configuration of Devstral
- Seamless integration with Python and Git workflows
- Enhancing IDE experience with Visual Studio Code
Architecting Coding Agents
- Specifying agent roles and functional capabilities
- Designing workflows for code traversal and refactoring
- Strategies for error management and state rollback
Connecting Tools and APIs
- Linking agents to essential developer utilities
- Integrating APIs to connect with external services
- Establishing automation patterns via coding agents
Implementing Agentic Workflows
- Performing code analysis and generating documentation
- Assisting with automated refactoring and testing
- Facilitating collaborative coding with agents
Ensuring Security and Best Practices
- Creating secure execution environments
- Managing access controls and user permissions
- Monitoring and logging agent activities
Scaling and Sustaining Coding Agents
- Distributing agents across multiple teams and projects
- Continuously maintaining and updating agent workflows
- Driving continuous improvement through feedback mechanisms
Recap and Future Directions
Requirements
- Comprehensive knowledge of Python
- Proficiency in software development processes
- Adequate familiarity with APIs and code integration
Target Audience
- Machine Learning engineers
- Teams focused on developer tooling
- SREs dedicated to improving 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
