What are AI Agents?
An AI Agent is an autonomous or semi-autonomous system that perceives its environment, reasons about how to achieve a specific goal, and takes actions using available tools.
While a standard Large Language Model (LLM) is like a "brain in a box" that waits for a prompt to generate text, an AI Agent is like a "brain with hands." It doesn't just talk; it does.
1. The Core Architecture: The Agentic Loop
An agent operates in a continuous cycle often referred to as the Perception-Reasoning-Action loop.
- Perceive: The agent receives a goal (e.g., "Research the latest stock prices for NVIDIA and email a summary").
- Plan: It breaks the goal into sub-tasks (1. Search web, 2. Extract data, 3. Format email).
- Act: It uses a Tool (like a Web Search API).
- Observe: It looks at the tool output and decides if it needs to do more.
2. Key Components of an Agent
To function effectively, an agent requires four primary modules:
| Component | Responsibility | Analogous to... |
|---|---|---|
| Brain (LLM) | Reasoning, planning, and decision-making. | The Prefrontal Cortex |
| Planning | Breaking down complex goals into manageable steps. | Strategic Thinking |
| Memory | Short-term (Current context) and Long-term (Vector Databases). | Hippocampus |
| Tools/Action | Interfaces to interact with the world (APIs, Code Interpreters). | Hands and Senses |
3. Agents vs. Chatbots: What's the Difference?
| Feature | Standard Chatbot | AI Agent |
|---|---|---|
| Initiative | Reactive (Wait for prompt) | Proactive (Takes steps to reach goal) |
| Connectivity | Static knowledge | Dynamic (Can browse, use apps, run code) |
| Multi-step | Single turn | Multi-step reasoning and self-correction |
| Goal-oriented | Provide an answer | Accomplish a mission |
4. Logical Workflow: Agentic Reasoning
The following diagram illustrates how an agent handles a complex request using a ReAct (Reason + Act) pattern.
5. Types of AI Agents
- Simple Reflex Agents: Act only on the basis of the current perception (e.g., a simple chatbot).
- Goal-Based Agents: Take actions specifically to reach a defined destination (e.g., AutoGPT).
- Utility-Based Agents: Not only reach a goal but try to find the "best" or "most efficient" way to do it.
- Multi-Agent Systems (MAS): Multiple agents working together, often with specialized roles (e.g., one agent writes code, another tests it).
6. Popular Agent Frameworks
If you want to build agents today, these are the most common starting points:
- LangChain: The "Swiss Army Knife" for connecting LLMs to tools.
- AutoGPT / BabyAGI: Early experiments in fully autonomous goal-seeking.
- Microsoft AutoGen: A framework for multi-agent conversations.
- CrewAI: Orchestrating role-playing autonomous agents to work as a team.
References
- Original Paper: ReAct: Synergizing Reasoning and Acting in Language Models
- Lilian Weng Blog: LLM Powered Autonomous Agents
- DeepLearning.ai: AI Agents Specialization
Agents are powerful because they can use tools. But how exactly does an LLM know which tool to pick and how to use it?