Autonomous AI systems that perceive their environment and take action through tools
Core Idea: LLM Agents are systems that extend beyond basic language model capabilities by perceiving their environment through sensors (like text input) and acting upon it through actuators (like tools), using planning, memory, and reasoning to accomplish complex tasks autonomously.
Key Elements
Components
- Environment - The world the agent interacts with
- Sensors - Used to observe the environment (primarily text input)
- Actuators/Tools - Functions, APIs, and services used to interact with the environment
- Effectors - The LLM "brain" that decides how to go from observations to actions
- Memory - Systems that allow the agent to retain information over time
- Planning - Capability to break down tasks and reason through solution paths
Memory Systems
- Short-Term Memory:
- Uses context window to track immediate conversation
- May employ summarization techniques for longer interactions
- Long-Term Memory:
- Vector databases for retrieving relevant past information
- Different types include semantic memory (facts), episodic memory (experiences), procedural memory (skills), and working memory (current context)
Tool Systems
- Function Calling - Structured API access through JSON-like output formats
- Tool Selection - Autonomous determination of which tools to use when
- Toolformer - Training method for teaching models to use tools through API calls
- Model Context Protocol (MCP) - Standardized framework for tool integration
Planning & Reasoning
- Chain-of-Thought - Breaking down problems into logical steps
- ReAct Framework - Cycles between Thought, Action, and Observation
- Reflexion - Self-reflection on past actions to improve future performance
- SELF-REFINE - Iterative refinement through self-feedback
Autonomy Levels
- Ranges from fixed workflows with structured tool access to fully autonomous systems that determine their own action plans
Advantages Over Basic LLMs
- Compensates for LLM limitations (math calculations, current data access, etc.)
- Interacts with external systems and retrieves information
- Maintains memory of conversations and past actions
- Plans and executes multi-step tasks autonomously
Multi-Agent Systems
- Specialized Agents - Different agents handle different aspects of complex tasks
- Agent Orchestration - Coordination between multiple specialized agents
- Frameworks - AutoGen, MetaGPT, CAMEL, and other systems for multi-agent collaboration
- Generative Agents - Systems that simulate believable human behavior through memory, planning, and reflection
Connections
- Related Concepts: Multi-Agent Systems (collaborative agent frameworks), ReAct (reason and act cycles), AI Agent Memory Systems (how agents retain information)
- Broader Context: Large Language Models (foundation technology), Artificial Intelligence (parent field)
- Applications: AI Assistants, Autonomous Systems, Simulated Environments, No-Code AI Agent Development (platforms like n8n for building agents without coding), AI Agent Learning Resources (materials for learning agent development)
- Components: Toolformer, Model Context Protocol, Reflexion
References
- Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach
- Park, J. S., et al. (2023). Generative agents: Interactive simulacra of human behavior
- Yao, S., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models
- Anthropic. (2024). Introducing the Model Context Protocol
#LLMAgents #AIAgents #AutonomousSystems #ToolUse #AgentMemory #Planning
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