The main types of AI agents
If you've read a few articles on this topic, you've probably seen two different lists that never quite line up. The classic textbook gives five types of AI agents, running from dead-simple reflex agents to learning agents. The 2026 LLM crowd talks about ReAct agents and multi-agent systems instead. Both are right; they're describing different eras of the same idea. This guide merges them into seven types of AI agents, ordered from simplest to most capable, each with a real example you'll recognize.
The point isn't trivia. Picking the right type for your problem saves you money and grief. A thermostat doesn't need a language model, and a research assistant can't run on if-then rules. Match the agent to the job. For the broader concept of what makes something an agent at all, see autonomous AI agents.
1. Simple reflex agents
A simple reflex agent maps the current perception straight to an action using condition-action rules. No memory, no model of the world, no planning. If it sees X, it does Y.
How it works:
if condition then action, evaluated on the present input only.Real example: a thermostat that turns on heating below 19C, or a spam filter that blocks any email containing a blocklisted phrase.
Strength: fast, cheap, totally predictable.
Weakness: blind to history and context. If the right action depends on what happened five minutes ago, a reflex agent can't help.
This is the floor of the taxonomy, and honestly plenty of "AI" features in shipping products are reflex agents with a nicer label.
2. Model-based reflex agents
A model-based reflex agent keeps an internal model of the world so it can act sensibly even when it can't observe everything at once. It tracks state over time and uses that state, plus the current input, to choose an action.
How it works: maintains an internal representation of "how the world is now," updated as new perceptions arrive.
Real example: a robot vacuum that builds a map of your rooms and remembers where it has already cleaned, even when its sensors only see a small patch at a time.
Strength: handles partial observability; less brittle than pure reflex.
Weakness: the model can drift from reality, and it still isn't planning toward a goal.
3. Goal-based agents
A goal-based agent has an explicit objective and searches for a sequence of actions that reaches it. Instead of "if X then Y," it asks "which actions get me to the goal state?"
How it works: uses search or planning to evaluate action sequences against a defined goal.
Real example: a GPS navigation system planning a route from A to B, or a puzzle solver working backward from the finished state.
Strength: flexible; the same agent handles new goals without new rules.
Weakness: knows whether a goal is met, but not whether one path is better than another beyond reaching it.
4. Utility-based agents
A utility-based agent goes past "did I reach the goal?" to "how good is this outcome?" It assigns a utility score to possible states and picks actions that maximize expected utility, which lets it weigh tradeoffs.
How it works: a utility function ranks outcomes; the agent chooses the highest expected value, handling uncertainty and competing objectives.
Real example: a ride-hailing dispatcher balancing driver wait time, fuel cost, and passenger ETA, or an investment bot trading off risk against return.
Strength: makes nuanced decisions when several outcomes all technically "work."
Weakness: you have to define the utility function well, and a bad one produces confidently bad decisions.
5. Learning agents
A learning agent improves its behavior from experience rather than staying fixed. It has a component that critiques outcomes and feeds lessons back so future decisions get better.
How it works: a performance element acts, a critic evaluates results, and a learning element updates the agent's policy over time.
Real example: a recommendation engine that adapts to what you click, or a reinforcement-learning agent that gets better at a game across thousands of rounds.
Strength: adapts to change and to individual users without hand-written rules.
Weakness: needs data and feedback loops, and it can learn the wrong lesson from biased or sparse signals.
6. Tool-using LLM agents (the ReAct pattern)
This is what most people in 2026 mean by "an AI agent." A tool-using agent wraps a large language model in a loop where it reasons about a goal, calls a tool, reads the result, and reasons again. The common name is ReAct, short for reason plus act.
How it works: the model interleaves thinking and tool calls (search, code execution, API requests) until the task is done.
Real example: a coding assistant that reads your repo, runs tests, and edits files, or a research agent that searches the web and cites sources.
Strength: general-purpose; one agent handles wildly different tasks through natural language and tools.
Weakness: can hallucinate, costs tokens per loop, and needs guardrails on anything irreversible.
If you're deciding what to build, a single ReAct agent is the right starting point for most modern use cases. The what is an AI agent framework guide covers the libraries that implement this loop for you.
7. Multi-agent systems
A multi-agent system splits a job across several specialized agents that coordinate, each often a ReAct agent with its own role, tools, and context.
How it works: agents communicate and hand off work; a planner or orchestrator routes sub-tasks and combines results.
Real example: a content pipeline where a researcher agent gathers facts, a writer agent drafts, and an editor agent reviews before publishing.
Strength: tackles tasks too big or too varied for one agent's context window.
Weakness: coordination overhead. Agents can deadlock, duplicate work, or compound each other's errors.
When one agent isn't enough, this is the pattern, but reach for it deliberately. We cover exactly when in agentic AI vs AI agents.
The seven types at a glance
| # | Type | Core idea | Real example |
|---|---|---|---|
| 1 | Simple reflex | Condition-action rules | Thermostat, blocklist spam filter |
| 2 | Model-based reflex | Internal world model | Mapping robot vacuum |
| 3 | Goal-based | Search toward a goal | GPS route planner |
| 4 | Utility-based | Maximize expected value | Ride dispatch, trading bot |
| 5 | Learning | Improve from feedback | Recommendation engine |
| 6 | Tool-using LLM (ReAct) | Reason plus call tools in a loop | Coding and research agents |
| 7 | Multi-agent system | Coordinated specialists | Research, write, edit pipeline |
The first five come from the classic AI textbook taxonomy; the last two are where the LLM era extended it. Most 2026 products live in rows six and seven, built on top of the ideas in rows one through five.
How to choose the right type of agent
The taxonomy is only useful if it changes what you build. Here's a practical way to walk from a problem to the right type without over-engineering.
Is the decision a fixed rule? If the correct action is always the same response to the same input, you want a simple reflex agent. Don't put a language model behind a rule a regex can enforce. It's slower, costlier, and less predictable.
Does it need to remember recent state? If the right action depends on what happened moments ago (a session, a partial view of the world), step up to a model-based reflex agent that tracks state.
Is there a clear goal to reach? If success is a defined end state and you need a path to it, a goal-based agent that plans and searches fits.
Are there tradeoffs between good outcomes? When several outcomes all "work" but some are better, you need the scoring of a utility-based agent.
Should it get better over time? If the environment or the users change and you have feedback data, a learning agent adapts where a static one would stale.
Is the task open-ended and language-shaped? For anything you'd describe in a sentence and solve with tools (search, code, APIs), the tool-using ReAct agent is the default in 2026, and it's where most new projects should start.
Is it genuinely too big for one agent? Only then reach for a multi-agent system, and expect to pay for the coordination in complexity.
A useful bias: pick the simplest type that can do the job, then move up only when it demonstrably can't. Teams lose weeks building a multi-agent system for something a single ReAct agent with three good tools would have handled. Complexity is easy to add later and painful to remove.
How Sokko fits
Types six and seven, the tool-using and multi-agent kinds, are the ones that need real infrastructure, because they run continuously, hold state, and touch the network. Sokko is managed, always-on cloud hosting built for exactly that. We run the servers so you don't. Each agent gets its own private, isolated space, walled off from everyone else's, and your API keys are stored securely and never exposed in your code. Anything your agent writes is saved automatically and survives restarts, so a learning or tool-using agent keeps what it wrote. For multi-agent systems, every agent stays isolated with its own CPU and memory limits, and there's an optional org-wide shared-memory add-on when agents genuinely need common context. Pick from four pre-wired runtimes (OpenClaw, Hermes, Paperclip, OpenSRE), bring your own model keys or use Sokko credits, and each boots usually in under a minute.
Whichever type of agent you're building, Sokko starts from about $12/month per agent with $100 in trial credits, so you can run one ReAct agent today and grow into a coordinated system later.
Key takeaways
The seven types of AI agents span simple reflex, model-based reflex, goal-based, utility-based, learning, tool-using ReAct, and multi-agent systems.
The first five are the classic taxonomy; ReAct and multi-agent systems are the LLM-era additions.
Match the type to the job: rules for simple tasks, a ReAct agent for most modern ones, multiple agents only when one can't cope.
The heavier types need real hosting, with isolation, secrets, and persistent state.
For further grounding, IBM's types of AI agents overview and the intelligent agent reference both align with this taxonomy.