Harnesses Bring Coordination and Guardrails to Enterprise AI Agents

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The term "AI harness" has been appearing more frequently as enterprises move from experimenting with individual AI agents to building workflows that involve multiple agents and models. Vendors are also starting to package the concept into products for enterprise customers, but the terminology is still unsettled. What counts as a harness, where it fit in an agentic workflow when does an enterprise actually need one are still all unsettled questions.

In this Q&A, Gareth de Bruyn, CEO, founder and chief architect at Debcor Engineering, which specializes in native Ai integrations with SAP, discusses the emerging role of AI harnesses in enterprise workflows. He explains how they coordinate multiple agents, manage interactions with enterprise systems and provide controls around AI-generated actions.

What exactly is an AI harness and where does it fit in an agentic workflow?

De Bruyn: The harness is what sits between the agentic code and the models themselves. It handles routing, access control, context management, evaluation and audit. Those controls need to be in place before the agent interacts with enterprise systems.

When you truly see the value of agents is when you have many of them working together. A harness is what makes it possible to coordinate multiple agents and manage their interactions.

How does a harness govern the interaction between AI agents and enterprise systems?

De Bruyn: A harness coordinates how an agent performs its job and interacts with enterprise systems. "Transacts" might be a better way to describe that interaction.

An agent might access an enterprise system to gather information it needs to make a decision. But when it takes an action -- such as creating an order, updating a customer record or moving goods -- the harness applies the necessary guardrails and controls.

How would you explain the role of a harness to someone who hasn't worked with one before?

De Bruyn: Think of it as air traffic control.