Agent Harness
The scaffolding around an AI agent — prompt construction, tool dispatch, retry logic, trace emission, state management — that turns a model into a workflow participant.
Production AI is not a prompt. It is a system of context, tools, permissions, traces, evals, and feedback loops.
What it is
An agent harness is the code that wraps a language model and gives it the structure to be an agent: it assembles the prompt, presents available tools, parses the model's tool calls, dispatches them to real implementations, handles errors and retries, manages conversation state, and emits traces. Without a harness, you have a model that can generate text. With a harness, you have an agent that can do work.
Why it matters
The harness is where most of the actual engineering of an AI agent lives. Two teams using the same model with different harnesses produce dramatically different agents. The harness decides what the agent can see, what it can do, how it recovers from failure, and what's observable about its behavior.
How it works
Common substrates include LangGraph, the OpenAI Agents SDK, Mastra, Inngest, and custom in-house harnesses. The harness is where MCP tool calls are dispatched, where memory is loaded and stored, where retry policies live, and where trace spans are emitted. We treat the harness as the contract — versioned, tested, and gated by the same eval set as the prompts and models it serves.
Related resources
The execution engine that turns an AI agent from a chat-window demo into a long-running, event-driven, restartable process you can trust with real operations.
The engine that runs an AI agent workflow as a durable, observable, restartable process instead of a one-shot script — what separates an agent demo from an agent deployment.
A governed catalog of every tool an AI agent can call — your APIs, your databases, your internal systems — with typed schemas, permission scopes, audit trails, and the standard protocol (MCP) that turns 'we exposed it to the LLM' into 'we know exactly who called what when'.
The mechanism by which a language model invokes external functions — APIs, databases, code execution, retrieval — and reads the results back to continue its work.