Agent types at a glance
The term "AI agent" covers a wide spectrum — from a simple rule-follower to a network of cooperating AI models. Choosing the right type for a task determines how well it performs and how much it costs to run.
We will cover four types that you will encounter most often in practice:
1. Simple reflex agents
A reflex agent maps inputs directly to outputs using a fixed set of rules. There is no reasoning, no memory, and no planning — just "if this, then that."
How it works: Input arrives → match against a rule → execute the associated action. No loop, no history.
Example: A customer support bot that responds with a fixed FAQ answer whenever it detects keywords like "refund" or "cancel".
Strengths
- Fast and cheap to run
- Predictable and easy to debug
Weaknesses
- Breaks on inputs it was not programmed for
- No ability to adapt or learn
2. ReAct agents
ReAct (Reason + Act) agents are the most widely used agent type today. The LLM alternates between writing its reasoning ("I should search for X") and calling a tool. The result feeds back into the next reasoning step.
How it works: Goal arrives → reason about the next step → call a tool → observe the result → reason again → repeat until done.
Example: A research agent that searches the web, reads pages, synthesises findings, and writes a summary — all in one run.
Strengths
- Handles open-ended, multi-step tasks
- Adapts based on what it finds
- Transparent — you can read the reasoning trace
Weaknesses
- Can go off-track on vague goals
- Costs more tokens per run
Start here. If you are building your first agent, use the ReAct pattern. It is well-supported in every framework and works for the majority of tasks.
3. Autonomous / long-running agents
These agents run for extended periods — hours, days, or continuously — without human interaction. They have persistent memory, can spawn sub-tasks, and often run on a schedule.
How it works: A long-horizon goal is given. The agent breaks it into a queue of sub-tasks, works through them, stores results, and picks up where it left off if interrupted.
Examples: AutoGPT, Devin (the AI software engineer), agents that monitor a codebase and open GitHub issues when tests fail.
Strengths
- Can tackle very large, open-ended goals
- Runs without supervision
Weaknesses
- Expensive — many tokens, many tool calls
- Hard to debug and control
- Errors can compound over many steps
4. Multi-agent systems
A multi-agent system is a team of specialised agents working together. One orchestrator agent breaks down the goal and delegates sub-tasks to worker agents, each with its own tools and expertise.
How it works: Orchestrator receives goal → assigns tasks to agents (e.g. Researcher, Coder, Writer) → agents complete their part → orchestrator combines results.
Examples: CrewAI, AutoGen, Microsoft Magentic-One — teams of agents where one plans and others execute.
Strengths
- Parallelises work across agents
- Each agent stays focused on one role
- Scales to complex, multi-domain tasks
Weaknesses
- More complex to build and orchestrate
- Coordination bugs are hard to trace
Which type should you use?
| Task type | Recommended |
|---|---|
| Fixed input, fixed output (e.g. FAQ bot) | Simple reflex |
| Open-ended research, coding, writing | ReAct agent |
| Long-running background task | Autonomous agent |
| Complex task requiring multiple skills | Multi-agent system |
Hybrid patterns in practice
In real production systems, agent types rarely stay pure. A customer support agent might start as a ReAct agent for most queries but escalate to a multi-agent system when the task requires both a database lookup agent and a policy-checking agent working together. Similarly, an autonomous agent often delegates individual sub-tasks using ReAct loops internally.
The key insight is that these categories describe patterns, not rigid boundaries. Most production agents combine elements from multiple types. Start with the simplest type that handles your core task, then layer in additional patterns only where the task demands it. Premature complexity is one of the most common reasons agent projects fail — see the limitations and best practices guide for the full list of pitfalls.
Types of AI Agents FAQ
What are the four main types of AI agents?
The four main types are simple reflex agents that follow fixed rules, ReAct agents that reason and act in a loop, autonomous agents that run for extended periods without supervision, and multi-agent systems where specialized agents collaborate on complex tasks.
What is a ReAct agent?
A ReAct (Reason + Act) agent alternates between writing its reasoning and calling tools. The LLM thinks about what to do next, calls a tool, observes the result, and reasons again — repeating until the task is complete. It is the most widely used agent pattern today.
When should I use a multi-agent system?
Use a multi-agent system when a task requires multiple specialized skills that are best handled by separate agents working in parallel. An orchestrator agent delegates sub-tasks to worker agents, each with its own tools and expertise, then combines the results.
What is the difference between a reflex agent and a ReAct agent?
A reflex agent maps inputs directly to outputs using fixed rules with no reasoning or memory. A ReAct agent uses an LLM to reason about each step, call tools, and adapt based on results. Reflex agents are fast and cheap but brittle; ReAct agents handle open-ended tasks but cost more tokens.
Which type of AI agent should a beginner start with?
Start with the ReAct pattern. It is well-supported in every major agent framework, handles the majority of real-world tasks, and produces a transparent reasoning trace that makes debugging easier.
Related tutorials
Understand the core components every agent needs, or jump straight into comparing agent frameworks like LangChain and CrewAI. For multi-agent architectures in depth, see the LangChain multi-agent systems guide.
Quick summary
- Simple reflex agents: fast, rule-based, no reasoning — for predictable tasks
- ReAct agents: most common — reason + act in a loop, good for open-ended tasks
- Autonomous agents: long-running, unsupervised — powerful but expensive
- Multi-agent systems: teams of specialised agents for complex, multi-domain work