What is an agent harness? How agent harnesses work

An agent harness is the software layer that enables an AI model to complete multistep tasks by coordinating model calls, tool use, context, execution environments, state, permissions, and feedback loops. It separates a model’s proposed actions from the systems that authorize and perform them, helping teams limit access, validate inputs, preserve progress across interruptions, evaluate results, and stop work on completion, failure, cancellation, or resource limits. Effective harnesses provide relevant rather than excessive context, secure execution boundaries, durable task artifacts, approval controls, audit trails, and testing or other verification before results are accepted. They differ from frameworks, which provide reusable building blocks, and sandboxes, which isolate code execution but do not manage the full agent workflow; they may also use MCP to connect tools and resources. Organizations evaluating a harness should test its completion reliability, recovery behavior, authority controls, observability, and resource management on realistic tasks. Northflank positions its Harnesses and Sandboxes as managed or bring-your-own-cloud isolated workspaces for coding agents, supporting persistent files, configurable resources, repository integration, networking, and deployment workflows while leaving agent selection and access policies to the customer.

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