Agentic LLM Systems and Memory-Augmented Architectures
Prerequisites: LLM fundamentals & Python
Level:
What You'll Learn
Agent Architectures
Analyze and implement advanced agent architectures including ReAct, Self-Ask, and Tree of Thoughts.
Memory Augmentation
Design and integrate various memory systems (short-term, long-term, vector databases) into LLM applications.
Reasoning and Planning
Implement complex reasoning and planning algorithms for autonomous task decomposition and execution.
Tool Integration
Develop mechanisms for LLM agents to effectively select and utilize external tools and APIs.
Multi-Agent Systems
Construct and manage systems involving multiple interacting LLM agents for collaborative or competitive tasks.
System Evaluation
Apply rigorous methods for evaluating the performance, reliability, and robustness of agentic systems.