04_ai_agents

AI Agents

Introduction

AI Agents are autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. They represent a significant advancement in AI, enabling systems to interact with their environment and learn from experiences.

Topics Covered

1. Algorithms

• Reinforcement Learning
• Planning and Search Algorithms
• Natural Language Processing (NLP)

2. Architectures

• Layered Architecture
• Blackboard Architecture
• Subsumption Architecture
• Agent Types:
  • Reactive Agents
  • Deliberative Agents
  • Hybrid Agents
  • Utility-Based Agents

3. Memory Module

• Short-term Memory
• Long-term Memory

4. Training Techniques

• Reinforcement Learning
• Imitation Learning
• Curriculum Learning
• Multi-Agent Learning

5. Challenges

• Scalability
• Uncertainty Management
• Integration of Sophisticated Learning Algorithms

6. Tools and Libraries

• LangChain
• LangGraph
• PydanticAI
• CrewAI
• BentoML
• TorchServe

Learning Resources

Documentation and Guides

• LangChain Documentation
• LangGraph Documentation
• CrewAI Documentation
• BentoML Documentation
• TorchServe Documentation

Articles and Tutorials

• What Are AI Agents?
• AI Agent Architecture: Breaking Down the Framework
• Types of Agent Architectures
• How Do You Train an AI Agent?
• AI Agents in Action: Advanced Training Strategies