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An LLM is the model that reads input and produces a response or tool request. An agent is that model working toward a goal through a process of planning, acting, observing results, and adjusting. A harness is the surrounding software and operating context that supplies instructions, tools, state, and constraints.
The caveman version
Imagine a job that needs doing. The LLM is the brain: it interprets what it is told and decides what to say or request next. The agent is the worker pursuing the job: it can take steps, inspect what happened, and choose what to do next. The harness is the rules, tool belt, work area, and workflow that shape what the worker can do and how the job proceeds.
That is a memory aid, not a literal description. These pieces are software, and different systems may divide responsibilities differently.
What is an LLM, and how is it different from an agent?
An LLM is a model
A large language model (LLM) processes input and generates output. It might answer a question in one response, or it might request that a tool perform an action. Generating a response, even a detailed one, does not by itself make the model an agent.
An agent is a goal-directed process using a model
An agent uses a model in a task-directed process that can decide what steps to take and make use of tools. Anthropic defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task”—in contrast with a system that follows a fixed script. Anthropic’s explanation of agents describes this distinction.
A typical agent loop looks like this: the model considers the task, requests or selects an action, receives the result, and continues or finishes. The process may repeat several times. The agent is not just the model: its behavior also depends on its instructions, available tools, accessible data, and execution environment.
What is an agent harness?
A harness is the software layer and operating context around the model-and-agent process. It provides or coordinates instructions, context, tool calls, session state, and constraints. The term is not used with exactly the same scope everywhere.
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- Anthropic describes a harness in one context as “the instructions, and the guardrails, that the model operates under.”
- In a discussion of agent scaffolding, Anthropic defines an agent harness as “the system that enables a model to act as an agent: it processes inputs, orchestrates tool calls, and returns results.”
- Microsoft’s VS Code documentation uses a runtime-focused definition: “the software layer that runs an agent session.”
Those descriptions overlap, but emphasize different boundaries: the rules around a model, the orchestration that enables agent behavior, or the software running a session. So “harness” is useful architecture terminology, not a single universally fixed component name.
How the model, agent, harness, and tools fit together
- The harness prepares the request. It supplies the model with instructions and relevant context, subject to the system’s permissions and configuration.
- The model responds or requests an action. It may answer directly, or ask for a tool to be used.
- The harness routes or executes the request. It coordinates the call according to the available tools and rules.
- A tool acts and returns a result. A tool is a capability or service—such as a function or external service—not the harness itself.
- The harness returns the result to the model. It may update session context or state so the model can continue.
- The model continues or finishes. It can interpret the result, request another action, or provide a final response.
The environment determines what files, sites, services, and data the process can reach. Anthropic warns that even a well-trained model can be exposed to risks by a poorly configured harness, an overly permissive tool, or an exposed environment. That is why permissions and boundaries matter alongside model capability.
Is an AI agent just an LLM with tools?
Not necessarily. A model that can call a tool once may still be operating in a fixed, one-step workflow. Agent behavior is more specifically about pursuing a goal through a process in which the model can direct its next steps, use tools, observe results, and adapt. The distinction is about how the system operates, not just whether a tool is present.
Likewise, the harness is not a synonym for tools. Tools perform actions; the harness provides or coordinates access to them and governs how the larger session runs.
Choosing an implementation: what to compare
The right architecture depends on who owns execution and how much control or integration work an application needs. OpenAI’s documentation names three starting points, with different levels of runtime responsibility:
| Starting point | How the documentation positions it |
|---|---|
| Agents API | A managed agent and runtime path. |
| Agents SDK | An SDK path where the application controls deployment, storage, approvals, and runtime integration. |
| Responses API | A lower-level path for direct model responses or building an agent from scratch. |
These are documented options, not a ranking. Before choosing an approach, compare the responsibilities that matter to your application:
- Runtime ownership: Does a vendor manage the runtime, or does your application run it in its own infrastructure?
- Loop and orchestration: Does a runtime or SDK provide the agent loop, or must your application build it?
- State: Is session state saved by a service, stored by your application, or manually passed between calls?
- Tool execution: Are tools hosted, handled by application code, or executed in the developer’s environment?
- Controls: What permissions, approval steps, and sandbox boundaries apply before an action runs?
For current implementation details, consult the relevant vendor documentation: OpenAI Agents documentation, Anthropic’s agent guidance, and Microsoft’s VS Code guide to agent harnesses.
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