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
Comparative Analysis of Reasoning Architectures
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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 arXiv preprint arXiv:2210.03629 DOI: 10.48550/arXiv.2210.03629 - Introduces the ReAct framework, which intertwines reasoning (Thought) and acting (Action) steps, enabling LLMs to perform dynamic reasoning to create, maintain, and adjust action plans, and interact with external environments.
- 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) 36 DOI: 10.48550/arXiv.2305.10601 - Proposes the Tree of Thoughts framework, which generalizes Chain-of-Thought prompting by allowing LLMs to explore multiple reasoning paths and evaluate intermediate thoughts, leading to more robust problem-solving in complex tasks.
- Graph of Thoughts: Towards Large Language Model Based Cognitive Architectures, Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, Torsten Hoefler, 2024 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38 (Association for the Advancement of Artificial Intelligence (AAAI)) DOI: 10.1609/aaai.v38i16.29720 - Extends the Tree of Thoughts concept to a more general graph structure, enabling LLMs to manage and manipulate complex thought processes more flexibly, supporting advanced reasoning and planning.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, 2020 Advances in Neural Information Processing Systems (NeurIPS) 33, Vol. 33 DOI: 10.48550/arXiv.2005.11401 - Introduces Retrieval-Augmented Generation (RAG), a fundamental method for enhancing LLM factual accuracy and reducing hallucinations by conditioning generation on retrieved documents, highly relevant to the 'Self-Ask' approach's goal of factual grounding.