This repository is a collection of concepts, notes, research papers, articles, design patterns, tools, and examples useful for building AI agents.
🚜👷🚧🏗️ Note: This repo is under active development. Things will move around a lot.
AI Agents are software programs capable of:
- Interacting with their environment
- Collecting data
- Planning
- Making decisions based on data
- Performing predetermined goals
- Autonomously
- GenAI architectural patterns
- Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs
- 3 Patterns in Agent Design
- Design Patterns with Autogen
- Simple reflex agents respond directly to stimuli from the environment without considering the history of the world.
- Model-based reflex agents use a model of the world to handle partially observable environments.
- Goal-based agents act to achieve specific goals.
- Utility-based agents aim to maximize their performance measure through a utility function.
- Learning agents have the ability to improve their performance over time based on experience.
- Combines reasoning and action to improve decision-making processes in AI agents.
- Uses task planning to break down complex goals into manageable tasks for the agent to execute.
- Coordinates multiple agents to work together towards common goals, enhancing their collective performance.
- Attention is All You Need
- GPT-3: Language Models are Few-Shot Learners
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- State of GPT by Microsoft
- Busy Executive's Guide on LLMs
- Agent Personas and Goals: Developing and defining different agent personas and their objectives to tailor their behavior.
- Decision Making and Planning
- Automated Planning and Scheduling
- Learning and Adaptation
- Concepts and techniques for enabling AI agents to learn and adapt to new environments and challenges.
- Cooperative Multi-Agent Reinforcement Learning
- One-Shot Learning with Memory-Augmented Neural Networks
- Zero-Shot Learning: A Comprehensive Evaluation of the Good, the Bad and the Ugly
- Multi-agent Systems and Coordination
- Interactions
- Best practices and techniques for designing interactions between AI agents and humans.
- Techniques for processing and managing inputs and outputs for AI agents.
- Reflection (Reflexion)
- Hallucinations
- Grounding
- Deployments, scalability and Performance
- LangChain with Reasoning Engine
- Security and Privacy
- Startup School: Gen AI - Building AI Agents