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Course Outline
Review of AutoGen Core Concepts for Government
- Agent and group definitions
- Function calling and role chaining
- Limitations of built-in agents and where customization is needed
Building Custom Agents with Python for Government
- Defining agent behavior using user_proxy and AssistantAgent subclasses
- Injecting role-specific logic and decision-making capabilities
- Creating reusable agent modules and mixins for enhanced modularity
Advanced Tool Integration and Routing for Government
- Tool registration, binding, and invocation processes
- Conditionally routing inputs to specific tools based on requirements
- Managing multi-step toolchains and composite actions for complex tasks
Planning and Context Management for Government
- Designing task decomposers and intermediate planners to enhance efficiency
- Maintaining context across chained agents to ensure seamless operations
- Implementing scoped memory for long-running sessions to preserve state information
Error Handling and Recovery Mechanisms for Government
- Detecting and managing failed or incomplete interactions to maintain reliability
- Agent-triggered retries and fallback logic to ensure robustness
- Logging, debugging, and response validation for thorough oversight
Multi-Agent Collaboration with Custom Roles for Government
- Coordinating specialists within dynamic agent groups to optimize team performance
- Orchestrating reasoning loops and cooperative workflows to achieve collective goals
- Evaluating role separation versus role blending in task assignments to enhance flexibility and effectiveness
Real-World Deployment Strategies for Government
- Optimizing for performance and cost, including token use and caching techniques
- Embedding AutoGen workflows into web applications or data pipelines for seamless integration
- Ensuring security, observability, and user feedback integration to support continuous improvement
Summary and Next Steps for Government
Requirements
- Proficiency in Python programming for government applications
- Experience building with large language model (LLM) based applications
- Familiarity with function calling and multi-agent system design
Audience
- Senior developers
- Platform engineers
- AI architects
14 Hours
Testimonials (1)
Trainer responding to questions on the fly.
