Types of AI Agents: Five Shapes That Cover Almost Everything
Academic taxonomies of agents — reflex, model-based, goal-based, utility-based — are useful for exams and nearly useless when you are choosing what to build on Monday. What matters in practice is the shape of the control flow, because that determines your cost, your latency and how hard the thing is to debug.
Five shapes cover almost every agent we have shipped or reviewed. They form a rough ladder of complexity, and the most common expensive mistake is starting two rungs higher than the task requires.
The five shapes, from cheapest to hardest#
| Shape | Control flow | Good for | Main risk |
|---|---|---|---|
| Tool-augmented responder | One model call, maybe one tool | Lookups, enrichment, classification | Barely an agent; fine |
| Single-loop agent | Model loops over a small tool set | Support tasks, research, triage | Wandering on long tasks |
| Planner–executor | Plan once, execute steps, re-plan on failure | Multi-step ops, migrations | Stale plans after step three |
| Router with specialists | One router picks a narrow sub-agent | Broad domains with distinct skills | Routing errors compound |
| Collaborating agents | Several agents exchange results | Genuinely parallel research or review | Cost, latency, untraceable failures |
Start one rung lower than feels right#
The single-loop agent solves far more real problems than its reputation suggests, and it has one enormous advantage: a linear trace that a human can read top to bottom. Every rung above it buys capability by spending traceability. Before you move up, be able to name the specific case that the simpler shape failed on, with a trace to prove it. Teams that skip this step end up with a five-agent system whose bugs nobody can localise, doing a job a loop with four tools was already doing at a fifth of the cost.
How to tell which rung you actually need#
- If the task is one lookup and one decision, you need a tool-augmented responder.
- If the tools are few and the order varies, you need a single loop.
- If a human doing this job would write a checklist first, you need planner–executor.
- If the job splits cleanly into distinct expertises with different tools, you need a router.
- If two subtasks genuinely have no dependency and both are slow, collaboration may pay for itself.
The specialist trap#
Routers look tidy on a diagram and behave badly at the edges. The router sees only the request, not what the specialists would have found, so it must guess — and a wrong guess sends the request to a specialist that cannot say anything useful. Two mitigations help: let a specialist return `not mine` and route again once, and keep the number of specialists small enough that the router prompt can describe each in one clear sentence. If you cannot describe the boundary in a sentence, neither can the router.
Measure routing accuracy separately from task accuracy. A 90% accurate router in front of 95% accurate specialists gives you 85% end to end, and the diagnosis is invisible if you only track the total.
Shape and cost, honestly#
Cost grows faster than the diagram suggests. A single-loop agent on a typical support task costs a handful of model calls. A planner–executor adds one planning call and often a re-plan. A router adds a call before anything useful happens. Collaborating agents multiply: three specialists each running their own loop is three loops, and if a coordinator reviews their output that is a fourth. None of that is a reason to avoid the higher shapes; it is a reason to reach them deliberately, with a number in front of you.
Frequently asked questions
Are multi-agent systems better than a single agent?
Only when the subtasks are genuinely independent and each needs different tools or a different model. Otherwise you have paid for extra latency, extra tokens and a failure surface that is much harder to trace, in exchange for a diagram that looks impressive in a slide deck.
What is the most common shape in production?
The single-loop agent with three to six tools and a human gate on irreversible actions. It is unglamorous, it fits most real tasks, and its linear trace means an engineer can diagnose a bad run without special tooling.
How do I know when to move up a rung?
When you have a trace of a real failure that the simpler shape structurally cannot fix — not a case it got wrong once, but a class of case it cannot represent. Write that trace down; it is also the test case that proves the new shape helped.
types of ai agentsagent architecturesmulti agent vs single agentplanner executoragent routing
