Agentic LLM Systems and Memory-Augmented Architectures
Chapter 1: Foundations of Agentic Behavior
Defining Agency in LLM Contexts
Components of an Autonomous LLM System
Challenges in Agentic System Design
State-of-the-Art Agent Frameworks Overview
Chapter 2: Advanced Agent Architectures and Reasoning
The ReAct Framework: Synergizing Reasoning and Acting
Implementing ReAct Agents
Self-Ask: Improving Factuality through Iterative Questioning
Tree of Thoughts (ToT) for Complex Problem Solving
Graph-Based Reasoning Structures
Comparative Analysis of Reasoning Architectures
Practice: Building a Custom ReAct Agent
Chapter 3: Designing Sophisticated Memory Systems
The Role of Memory in Agentic Systems
Short-Term Memory Mechanisms
Long-Term Memory: Vector Stores and Embeddings
Advanced Retrieval Strategies
Structured Memory Representations
Memory Consolidation and Summarization Techniques
Managing Memory Read/Write Operations
Hands-on Practical: Integrating Vector DB Memory
Chapter 4: Complex Planning and Tool Integration
Task Decomposition Strategies
Hierarchical Planning Approaches
Integrating External Tools and APIs
Tool Description and Selection Mechanisms
Handling API Errors and Tool Execution Failures
Self-Correction and Plan Refinement
Practice: Implementing a Multi-Step Planning Agent with Tools
Chapter 5: Multi-Agent Systems
Principles of Multi-Agent System (MAS) Design
Communication Protocols for LLM Agents
Collaborative Problem Solving Architectures
Agent Roles and Specialization
Coordination Mechanisms
Competitive and Negotiation Scenarios
Challenges in Scaling Multi-Agent Systems
Hands-on Practical: Building a Collaborative Agent Team
Chapter 6: Evaluation and Optimization of Agentic Systems
Defining Success Metrics for Agentic Tasks
Evaluating Reasoning and Planning Capabilities
Assessing Tool Use Reliability and Accuracy
Memory System Performance Evaluation
Benchmarking Agentic Systems (AgentBench, etc.)
Debugging Strategies for Complex Agent Behavior
Optimization Techniques for Agent Performance
Fine-tuning LLMs for Specific Agent Roles
Practice: Setting up an Evaluation Framework
Self-Correction and Plan Refinement
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- ReAct: Synergizing Reasoning and Acting in Language Models, Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao, 2023 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2210.03629 - Introduces a paradigm where LLMs interleave reasoning (Thought) and action (Act) steps, processing observations, which forms a basis for detecting deviations and reacting with new actions.
- Tree of Thoughts: Deliberate Problem Solving with Large Language Models, Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan, 2023 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2305.10601 - Describes a method for complex problem-solving that enables LLMs to explore multiple reasoning paths, backtrack, and re-evaluate, enhancing their ability to handle planning failures.