What are AI Agents?
An AI Agent is a system that uses a Large Language Model (LLM) as its “brain” to perform tasks autonomously. Unlike a traditional chatbot that just answers questions, an agent can plan steps, use tools (like a web browser or calculator), and interact with external environments to achieve a goal.
The Agentic Loop
Most agents operate on a cycle often called the Reasoning-Act (ReAct) loop:
- Thought: The agent analyzes the user’s goal and decides what to do next.
- Action: The agent selects a tool to use (e.g., search Google, write a file).
- Observation: The agent looks at the result of the action (e.g., the search results).
- Repeat: The agent continues this cycle until the goal is met.
Core Capabilities
| Capability | Purpose | Example |
|---|---|---|
| Tool Use | Interacting with the world | Function Calling |
| Planning | Breaking down complex tasks | Chain of Thought (CoT) |
| Short-term Memory | Context of the current task | KV Cache |
| Long-term Memory | Accessing historical data | RAG / Vector DB |
Types of AI Agents
1. Autonomous Agents
These are set-it-and-forget-it systems. You give them a vague goal (e.g., “Research and write a report on X”), and they figure out the steps themselves.
- Examples: AutoGPT, BabyAGI.
2. Multi-Agent Systems
Multiple Specialized agents working together, often in a “Manager-Employee” hierarchy.
- Examples: CrewAI, Microsoft AutoGen. One agent writes code, another critiques it, and a third executes it.
3. Personal Assistants
Agents trained to handle specific user workflows, like scheduling meetings or managing emails.
Popular Agent Frameworks
- CrewAI: Orchestrates role-playing agents (Research, Writer, etc.) to collaborate.
- AutoGen: Focuses on conversational agents that can talk to each other to solve problems.
- LangChain / LangGraph: Provides the Lego blocks for building custom agentic workflows.
- OpenAI Assistants API: A hosted way to build agents with built-in memory and tool access.
Current Challenges
- Infinite Loops: Agents can sometimes get stuck in a repetitive cycle of errors.
- Cost: Each step in the loop consumes tokens, making complex tasks expensive.
- Reliability: Small errors in early steps can snowball, leading to completely wrong conclusions.
Related Concepts
- Function Calling - How agents use external tools.
- Chain of Thought - The logic behind agent planning.
- Structured Output - Ensuring agents return the right data format.
- RAG (Retrieval-Augmented Generation) - Giving agents a long-term memory.