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Course Outline
Foundations of Devstral and Coding Agents
- Architectural breakdown of the Devstral framework
- The role of Agentic AI in modern software engineering
- Practical applications and use cases for coding agents
Configuring the Development Environment
- Installation and initial setup of Devstral
- Aligning Devstral with Python and Git workflows
- Optimizing integration with Visual Studio Code
Architecting Coding Agents
- Defining specific agent responsibilities and feature sets
- Designing workflows for code traversal and refactoring
- Implementing robust error handling and rollback mechanisms
Integrating Tools and APIs
- Establishing connections between agents and developer utilities
- Facilitating API integration for third-party services
- Leveraging automation patterns with coding agents
Applying Agentic Workflows
- Automating code exploration and generating technical documentation
- Enhancing refactoring and testing processes with AI assistance
- Facilitating collaborative coding sessions with agents
Security Standards and Best Practices
- Establishing secure execution boundaries
- Managing access controls and permission scopes
- Implementing monitoring and logging for agent activities
Scaling and Agent Lifecycle Management
- Distributing agents across distributed teams and projects
- Maintaining consistency and updating agent workflows
- Driving continuous improvement through feedback loops
Conclusion and Future Directions
Requirements
- Proficiency in Python programming.
- Practical experience in software development lifecycles.
- Working knowledge of API structures and code integration patterns.
Target Audience
- Machine Learning engineers.
- Teams dedicated to developer tooling and experience.
- SREs focused on enhancing the 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
