AI harnesses bring coordination and guardrails to enterprise AI agents

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 fits within an agentic workflow, and when an enterprise actually needs one remain unsettled.

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?

Gareth 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.

An agent might also need to interact with multiple enterprise systems to complete a task. The harness governs those interactions, helping determine what the agent can access, what actions it can take and how those actions are carried out.

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.

Take, for example, a sales order coming into a company. Someone might send it by email, by fax or as a perfectly formatted sales order. Based on all of this, we need someone acting as air traffic control. And that is really what the harness does.

It can say, "I recognize this. I can process your order, look up the data and create the order." Or it can say, "No, we need to run this through verification checks." That verification might require several AI calls, which can add cost and require additional auditing and tracking.

The harness makes sure everything is routed appropriately and that the right checks are applied. The result is a standardized order that can then go into the customer system.

At what point does an enterprise actually need a harness?

De Bruyn: If you just have one agent working by itself, I'd really question whether you need a harness.

For example, think of a simple OCR application, or optical character recognition. I created one where I could take a picture of someone's badge at a conference, have an agent read the information immediately, and save it in my CRM database. That doesn't need a harness, because it's a lightweight and straightforward task.

The value of a harness becomes clearer when multiple agents need to work together or when their actions require more oversight and coordination.

Are pre-made AI harnesses becoming a viable option for enterprises?

De Bruyn: There's a build-versus-buy decision happening with AI. Many enterprises are looking for pre-made AI solutions because they don't always have the expertise or confidence to build these systems themselves.

We're seeing the development of pre-made harnesses and other packaged AI options, but I don't think the market is mature yet. The terminology can also be misleading because a solution marketed as a single AI agent might actually involve many agents working together.

For example, at SAP's Sapphire conference, we heard about an accounts payable agent. But when you look at what that workflow actually needs to do, it could involve 10 to 15 different agents handling tasks such as processing invoices, checking information and routing decisions.

So, while it may be presented as one agent, there can be many agents and systems working behind the scenes. That's where I think marketing and reality haven't fully caught up.

We're going to see more pre-made solutions emerge, but enterprises need to understand what is inside those options, how the different agents work together and what role the harness plays in coordinating them.

What is the biggest lesson you've learned from building and deploying AI that enterprise leaders should keep in mind when thinking about AI harnesses?

De Bruyn: AI is not magic. There's a lot of pressure on enterprises to adopt AI, just as there was with technologies such as cloud and IoT. But leaders need to bring it back to the basics.

Start by asking: What are we trying to achieve? What outcome do we want, and how will we measure it?

Once you define those goals and KPIs, you can determine what the AI system needs to do and where a harness fits in. A harness is essentially a collection of components that brings together the models, agents and other systems needed to deliver that outcome.

You don't need to get into the technical details right away. Start with the business outcome, then break down the different pieces and determine what role the harness should play.

At its core, the harness helps manage risk and efficiency by coordinating those different pieces.

Editor's note: This interview has been edited for clarity and conciseness.

Kinza Yasar covers AI and emerging technology for TechTarget, with a focus on ethics, enterprise adoption, governance and business strategy. Before moving into journalism, she worked in IT and network support roles, giving her a systems-level perspective on how enterprise technologies are built, deployed and managed.