Agent vs Harness: What's the Difference?

Understand the distinction between AI agents and harnesses. Learn how agents handle decision-making while harnesses provide the infrastructure for reliable, long-running AI systems.

| 2 min read
AI LLM Agents Architecture Machine Learning

If you’ve been following the AI engineering space, you’ve probably seen the terms “agent” and “harness” thrown around. It took me a while to understand the distinction and I’m still trying to get my head around it, but I guess this is one of those things you actually need to build to fully understand. They’re often used together, sometimes interchangeably, and the boundaries can feel blurry.

The Core Distinction

Think of an agent as the brain that makes decisions and plans actions to achieve a goal. The harness is the body that provides the environment and tools for the agent to operate.

An agent might say, “I need to gather data from various sources, analyze it, and generate a report.” The harness provides the APIs, databases, and file systems that make those tasks possible.

Put simply: an agent is an AI model equipped with tools and the autonomy to take actions. A harness is the surrounding infrastructure that enables the agent to work reliably over time.

What the Harness Handles

The harness takes care of the operational concerns that would otherwise clutter the agent’s reasoning:

  • Tool provision - Web search, file access, code execution, API calls
  • Context management - Memory across sessions, context compaction, conversation history
  • Reliability - Saving progress, handling errors, recovery from failures
  • Workflow structure - Progress files, task lists, checkpoints
  • Guardrails - Safety constraints, output validation, steering mechanisms

The Car Analogy

If the agent is the engine, the harness is the entire car: the steering, brakes, navigation, fuel system, and everything else that makes the engine useful.

Harness = The infrastructure and framework surrounding the agent.

Agent = The model operating within that harness, doing the thinking and decision-making.

In Practice

Here’s how they interact:

The harness says: “Here are your tools. Here’s how to get started. Here’s the format for leaving notes in the scratchpad.”

The agent decides: “I’ll read the progress file, pick a feature to work on, write some code, test it, and commit.”

The harness doesn’t tell the agent what to do. It sets up the conditions for the agent to figure that out on its own. The agent doesn’t worry about how tools are implemented or how state is persisted. It just uses them.

References

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