Types of AI Agents, Grouped by What They Actually Do (2026)
Last updated 2026-11-06
Quick answer
Most published taxonomies of AI agents sort them by capability level, which is tidy on a slide and useless when you are choosing what to build. This one groups agents by observable behaviour: how they decide, whether they hold state, and how long they run. Each group maps to a different set of MCP servers, and getting the group wrong is the most common reason an agent project stalls.
Why behaviour beats capability tiers
Capability tiers — level 1 through 5, or 'assistant to autonomous' — describe a trajectory, not a design. Two systems at the same 'level' can need completely different infrastructure depending on whether they run for one turn or for a week.
Grouping by behaviour answers the questions you actually have: does this agent need working memory, does it need a sandbox, does it need other agents, and does it need to survive a restart.
1. Reactive agents — one turn, one action
These respond to a single input, call at most a tool or two, and return. A support assistant that looks up an order is reactive. So is a reviewer that reads a diff and comments.
Infrastructure needed: one tool server, no state store, no orchestration. Cheapest to build and the easiest to test, because the flow is fixed and you can assert on every path.
2. Plan-and-execute agents — decompose, then act
These break a task into steps first, then execute them. A coding agent that reads a spec, plans the file changes and applies them is plan-and-execute. Loki Mode, with its 39 MCP tools and spec-to-product flow, is this shape.
Infrastructure needed: a tool server plus somewhere to hold the plan, and a way to revise it. The characteristic failure is a plan that drifts from reality — the agent keeps executing steps that no longer apply because step two changed the world step four assumed.
3. Multi-agent systems — division of labour
Several agents with different roles coordinate on one task. This is where the infrastructure cost jumps, because agents need to pass messages, share state and avoid duplicating each other's work.
Agent Swarm is the local-first option: multi-agent API with state in SQLite, so message history and agent memory stay on your machine. The alternative is a hosted orchestration platform, which is faster to start and puts your agents' shared memory in someone else's infrastructure.
4. Long-running agents — survive the session
These keep working between conversations, which is the shape most production use cases actually need. An agent that forgets everything when you close the tab cannot chase a task over days.
Omnara's durable-agent platform is this category. The design questions are durability (what happens on restart), cost (steps accumulate), and retention (your agent's working data lives somewhere).
5. Embodied agents — act in a real environment
These operate a device, a browser or an app rather than an API. agent-device covers iOS, Android, desktop and TV; Ghost in the Droid hands an LLM a real phone with 62 MCP tools.
Highest capability, highest risk. They see whatever is on screen, including logged-in sessions and personal data. Run them on dedicated devices or emulator profiles, and assume anything visible is something the agent might act on.
Choosing a group
Pick the smallest group that completes the task. Most projects that stall have over-classified their problem: a reactive agent with two tools was sufficient, and a five-agent orchestration was built instead, bringing coordination failure modes the task never required.
If you are unsure between two groups, start with the cheaper one and instrument it. The signal that you actually need to move up is specific — the agent running out of context, or repeating steps, or needing to survive a restart — not a feeling that the architecture is too simple.
The picks, one by one
Plan-and-execute coding agent: reads a spec, plans, and applies changes through 39 MCP tools.
Best for: Delegating a complete implementation task rather than steering each edit.
Watch out: Review everything it writes; a drifted plan produces confident but wrong code.
Multi-agent API and MCP server with local SQLite state.
Best for: Multi-agent coordination that must keep its message history on your own machine.
Watch out: You own backups and state cleanup — nothing is synced anywhere.
Durable managed-agent platform for agents that keep running between conversations.
Best for: Long-running tasks that must survive the end of a chat session.
Watch out: Execution happens in the provider's infrastructure; check retention and per-step pricing.
Embodied agent control across iOS, Android, desktop and TV.
Best for: App QA where the agent must drive the real interface.
Watch out: It sees signed-in sessions — use a dedicated device.
Gives an agent trusted access to the Postman platform for API work.
Best for: Reactive and plan-and-execute agents that need to exercise APIs rather than guess their shape.
Watch out: Scope the token to the workspace the agent actually needs.
Frequently asked questions
What are the main types of AI agents?＋
Grouped by behaviour: reactive (one turn, one action), plan-and-execute (decompose then act), multi-agent (several coordinated roles), long-running (survives the session) and embodied (operates a device or browser). The grouping matters because each needs different infrastructure.
Which type should a beginner build first?＋
A reactive agent with one or two tools. It is testable because the flow is fixed, it is cheap because there is no state store, and it teaches you how the model actually picks tools — which is the failure mode that bites in every larger design.
Do multi-agent systems actually work better?＋
Not automatically. They add coordination failure modes — duplicated work, conflicting actions, state divergence. They pay off when the task genuinely splits into roles with different tools and different context needs. Otherwise a single agent with more tools is usually better.
What makes an agent long-running?＋
Durable state outside the conversation. The agent's progress, plan and working memory have to survive a restart or a closed tab. That requires a state store and a runtime that resumes, which is exactly what managed platforms like Omnara provide and local frameworks require you to build.
How do MCP servers fit into this taxonomy?＋
They supply the actions, not the type. The same server can serve a reactive agent or a long-running one. What changes across types is orchestration, state and sandboxing — which is why picking the type first, then the servers, avoids building infrastructure the task never needed.