Two terms dominate current AI talk — "reasoning models" and "agents" — and they're often blurred together. They're related but distinct, and untangling them clarifies a lot about what modern AI systems actually do and how to use them.
Reasoning models: think before answering
A reasoning model spends extra effort thinking before it responds — working through steps internally, exploring, checking — rather than blurting the first answer. The improvement is in the quality of the response to a single query. It's still fundamentally answering a question; it just does so more carefully, using more inference (test-time compute) to reason. Think: a smarter one-shot answer.
A reasoning model thinks harder about the answer. An agent takes actions to accomplish a goal. Thinking vs. doing.
Agents: act in the world
An agent goes further: given a goal, it acts — calls tools, runs code, searches, reads files, takes multiple steps in a loop, observes results, and adjusts. It's not just producing a better answer; it's doing things to accomplish a task. An agent operates over time and interacts with an environment.
How they combine
The two compose naturally and powerfully: the best agents use reasoning models as their "brain." An agent decides what to do (often using a reasoning model to plan and reflect), takes an action, sees the result, and reasons again about the next step. Reasoning improves each decision; the agent loop turns those decisions into accomplished work. Long-horizon and multi-agent systems layer both.
Why the distinction matters
Confusing them leads to mismatched tools. If you need a better answer to a self-contained question, a reasoning model may be all you need — no agent machinery, tools, or loop required (which add complexity and failure modes). If you need the system to do something involving external actions and multiple steps, you need an agent. Many "we need an agent" problems are really "we need a reasoning model"; some "just prompt it better" problems truly need an agent.
Why it matters
Reasoning is about the quality of thought; agency is about taking action toward goals. Modern frontier systems increasingly combine both — reasoning models as the mind, agent loops as the hands. Keeping the concepts separate helps you pick the right level of complexity for each task, and understand what a given AI system is really doing under the hood.
