INFO
Autonomous systems that perceive, reason, and act to achieve specific goals using rules, learning models, or adaptive strategies.
Purpose
- Enable intelligent decision-making in dynamic environments
- Automate tasks across domains like robotics, finance, and virtual assistance
- Learn and optimize behavior through interaction and feedback
How It Works
- Operate via predefined logic, machine learning, or reinforcement learning
- Types of agents include:
- Reactive Agents: Respond to stimuli without memory
- Deliberative Agents: Use planning and reasoning
- Hybrid Agents: Combine multiple strategies for complex tasks
- Adapt to changing conditions and improve performance over time
Applications
- Robotics
- Virtual assistants
- Autonomous vehicles
- Financial trading
- Scientific discovery and simulation
Case Study
- AlphaGo by DeepMind
- Uses deep reinforcement learning to master the board game Go
- Combines neural networks with self-play to refine strategies
- Demonstrated superhuman performance in strategic reasoning
Emerging Paradigm
- Agentic Reasoning emphasizes autonomous workflows and decision-making
- Builds on LLMs and multi-modal systems to create cost-effective, powerful agents
- Explored by Andrew Ng in BUILD 2024 as a key shift in AI deployment strategies