Lowe’s is fighting to prevent AI agent overload

Q&A // March 24, 2026 Lowe’s is fighting to prevent AI agent overload By Mitchell Parton Ivy Liu Practically every retailer is racing to adopt artificial intelligence throughout their businesses. But ensuring that these AI agents produce quality, consistent results is an additional challenge.

Lowe’s is one of the retailers actively implementing AI to improve the customer shopping experience and to assist employees in asking difficult questions.

In March of last year, Lowe’s released Mylow, a virtual assistant for customers that can answer questions about home ownership, provide information about building home improvement projects and search for products. Last May, the company announced Mylow Companion, an employee-facing AI tool for customer service and employee onboarding.

Associates can use that assistant to access product details, project advice and inventory information that customers may ask them for. Modern Retail spoke with Chandhu Nair, svp of data, AI and innovation at Lowe’s, about how the company makes decisions on whether to build and invest in new AI features.

Nair has been working on Lowe’s AI initiatives for several years and is particularly interested in the issue of AI sprawl — what he defines as when AI agents are narrowly focused, poorly coordinated, difficult to maintain and built in silos across an organization.

He also spoke about the rules and guardrails the retailer puts in place to ensure quality responses and security. This interview has been edited for clarity and length. How are you addressing what you call AI “sprawl” and building governance around AI?

“The evolution that you’re seeing in the industry is from these conversational chat assistants using generative AI to many more agents. You have a quick way to build an agent that can take multiple tool sets and perform a very specific task. An example of an agent that we have is an invoice reconciliation agent.

That can go back and look at different documents on a procurement order and invoice, and if there are discrepancies, it can understand and correct it. It helps automate a lot of the mundane processes that no one wants to do.

Now, the challenge with technology like that is that it’s easy to get carried away and build a whole set of agents, and that’s the sprawl that can happen with it. What we had to do was create, essentially, one taxonomy of where we apply these agents.

Within Lowe’s, we’ve created this AI transformation office, which has a governance process that looks at which areas have the right use cases to apply these agents. When you deploy these agents, there is a “human-in-the-loop” framework to all these. We usually start with a human in the loop.

For certain low-risk tasks, [humans are] always observing and only intervene when there is something that needs to be done. Certain tasks carry no risk. Everything that we do goes through our Lowe’s-built AI foundry, which is a Lowe’s-based AI platform. It has observability, it has explainability to it, so I can track how these agents are performing, etc.”

What do you mean by “sprawl”? What can go wrong when you have too many AI agents? “The inherent technology — which has now obviously gotten a lot of attention with agentic AI — the idea is you can use the LLM to specifically go after a particular task. You give it to them in simple English, and it can use different tools.

It can use an API, it can use an Excel sheet to solve that particular task you gave it. That’s the whole idea of agentic AI. The sprawl is because it’s so easy to do, because I can now prompt it and say, ‘Hey, use tools 1, 2, 3,’ and do it. The challenge is to make it work in a very consistent way.

If you and I were to do that same task in Excel, we would have that context of what needs to be done. Inherently, the models have to learn the context of how that needs to work. Humans also have certain control parameters that we have built into our memory and context in terms of what we would do or we would not do.

The agents have to be trained to make sure it’s consistent and it works within those boundaries and frameworks. It’s fairly easy to build an agent. You could have multiple engineers build out agents, and suddenly you would have a list of agents without a purpose and not working consistently every time.

It may work 20% of the time or 80% of the time; the challenge is to make sure it works 100% of the time.” What kind of rules do you have around building new AI tools? “We certainly have guardrails at every layer of the technology stack, and then, we also have guardrails on the process itself. I’ll start with the process side. We use four parameters.

One is, ‘What is the true ROI for the business case?’ It has to be meaningful for the company. The second is the capital investment that is needed. Those are traditional;