Agent Harnesses

An agent harness is the infrastructure layer that turns a raw AI model into a production-ready agent. It handles the plumbing so you can focus on what the agent should do, not how it all wires together.

What a harness provides

Think of a model like a powerful engine. A harness is the rest of the car — the chassis, steering wheel, fuel system, and dashboard. Without it, the engine is impressive but useless.

A good harness handles:

• Tool management — giving the agent access to web search, code execution, APIs, databases
• Memory — persisting information across sessions so the agent remembers context
• Conversation routing — deciding which model or sub-agent handles which part of a task
• Scheduling — running agents on a timer or in response to events
• Observability — logging what the agent did, why, and what it produced
• Security — controlling what the agent can access and what it can't touch

The spectrum of harnesses

Harnesses range from simple chat wrappers to full production platforms:

Minimal (direct API)

You call the model API directly. You manage everything yourself. Maximum control, maximum work.

• Good for: One-off scripts, prototypes, learning

Framework (LangChain, CrewAI, AutoGen)

A set of abstractions for building agents — tool connectors, memory modules, agent loops. You assemble the pieces.

• Good for: Developers building custom agent workflows

Platform harness (NullClaw, NanoClaw, OpenClaw)

A complete runtime — deploy it, configure it, and your agent is live. Handles channels (Telegram, Slack), memory, scheduling, tools out of the box.

• Good for: Deploying production agents without building infrastructure

Cloud platform (Scout, OpenAI Assistants)

Fully managed — you define the agent behavior via UI or API, the platform runs it for you.

• Good for: Non-technical teams deploying agents at scale

Choosing a harness

Next: Memory & Context →