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You've probably used an AI agent without realizing it. When you ask a chatbot to actually book a flight, reschedule a meeting, or pull up an order, an agent — a piece of AI software that can do things, not just chat — is doing the work behind the scenes.
Databricks, a company that builds platforms for data and AI, recently published a guide titled "How to scale agentic applications without creating AI sprawl." Here's what that means in plain language, and why it might quietly shape the tools you use at work.
What is an AI agent?
An AI agent is a small program powered by AI that can take actions on its own. Think of it as a tiny assistant that doesn't just answer questions — it actually goes and does things. It might check your calendar, send an email, or look up a shipment.
According to Databricks, building these agents is "getting easier." Newer AI models and "coding agents" (AI tools that help write software) mean even non-developers can piece one together. That's a big shift from a few years ago, when building one required a team of engineers.
What is "AI sprawl"?
Sprawl is a word borrowed from city planning. "Urban sprawl" means a city growing faster than its roads and services can keep up.
AI sprawl is the same idea. When every team in a company starts building their own AI helpers, with no shared plan, you end up with dozens — or hundreds — of agents doing overlapping work. Nobody knows which one is in charge. Some talk to each other; some don't. Costs add up. Problems hide in corners.
For a regular person, the closest parallel might be this: imagine your household installed five different smart-home apps, each with its own login, each controlling one light. Now multiply that across a company with 50 teams, each with their own AI helpers.
Why companies care
When AI sprawl gets out of hand, a few things tend to go wrong:
- Costs balloon, because nobody is tracking which agents are running or how much they cost.
- Security gets fuzzy — too many tools, not enough oversight.
- Agents contradict each other or quietly duplicate work.
- When something breaks, it's hard to find out why.
That's why Databricks' guide focuses on the management side, not just the building side. Easier to build means easier to lose control.
Wrap-up
Databricks' guide is less about a new product and more about a growing pain: AI is now easy to build, but hard to organize. If you work with AI tools in any capacity — even just using them — expect to hear the phrase "AI sprawl" more often in the months ahead. The next step you can take today: ask your team or your software provider whether they have a plan for managing all those AI helpers, or if they're simply piling up.