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Duration 21 hours
Course Outline
Foundations of Large Language Model (LLM) Agent Frameworks
- Core concepts of LLM agents and multi-agent system architectures
- Overview of the AutoGen framework and its operational ecosystem
- Functional agent roles: user proxy, assistant, function caller, and specialized variants
Installation and Environment Configuration for AutoGen
- Establishing the Python runtime environment and managing dependencies
- Basics of AutoGen configuration file management
- Integrating with LLM providers, including OpenAI, Azure, and local model instances
Agent Architecture and Role Definition
- Analysis of agent typologies and conversational interaction patterns
- Specification of agent objectives, instructional prompts, and behavioral guidelines
- Role-based task delegation strategies and control flow management
Function Invocation and External Tool Integration
- Registration of executable functions for agent utilization
- Execution of autonomous and collaborative function calls
- Integration of external APIs and Python scripting interfaces into agent workflows
Session Governance and Contextual Memory Management
- Implementation of session tracking and persistent memory structures
- Inter-agent messaging protocols and token management procedures
- Administration of conversation context and historical data retention
Implementation of Comprehensive Agent Workflows
- Construction of multi-stage collaborative processes, such as document analysis and code review
- Simulation of user-agent interactions and sequential decision-making chains
- Performance debugging, optimization, and refinement of agent systems
Operational Use Cases and Production Deployment
- Internal automation applications: research synthesis, reporting, and scripting
- External service integrations: chat interfaces and voice-enabled assistants
- Packaging strategies and deployment procedures for production-grade agent systems
Concluding Overview and Subsequent Development Paths
Requirements
- Proficiency in Python programming principles
- Working knowledge of large language models and prompt engineering techniques
- Practical experience with API interactions and automation workflow design
Target Audience
- AI Engineers
- Machine Learning Developers
- Automation Architects
Testimonials (1)
I liked that he constantly provided examples but also offered time for individual work on what he presented.
