Build vs. Buy for AI Agents: A Practical Guide

We’re at a new frontier in enterprise AI, as agents are no longer just clever chatbots or assistants that answer questions. They’re evolving into systems that interact with your data, automate decisions, run parts of your workflows, or engage your business specialists at the right time. That shift is exciting, but it also raises a familiar question with a new twist: Do you build, or do you buy?

When generative AI assistants first arrived, the build versus buy debate was mostly about speed to value and integration. With agents, the stakes are higher. What matters isn’t just whether the agent works but where it can be trusted, what systems it connects to, and whether it creates real business value.

And with that comes a new risk: agent sprawl. Organizations start by experimenting with a few copilots or scripts, only to end up with dozens of disconnected tools, no central oversight, and growing technical debt. The challenge now isn’t getting an agent to work. It’s making sure agents work together, at scale, and in a way that the business can trust.

This article breaks down when to buy, when to build, and why most companies will end up somewhere in between.

Why AI Agents Are Different

AI agents aren’t apps you download and plug in. They need deep integration with your data, security layers, and business processes. Their usefulness depends entirely on what systems they connect to and what actions they’re authorized to take.

That’s where many companies hit the wall. A pre-packaged assistant might handle quick wins (say, surfacing Salesforce reports or smoothing ServiceNow workflows), but as soon as you need the agent to step outside of its parent system, you need more.

The difference isn’t theoretical. It’s practical. What the agent is expected to do, and where, determines how you approach it.

The Case for Buying

Buying in the agent world usually means leaning on pre-built assistants packaged with your core systems, like Salesforce, ServiceNow, or SAP.

Benefits:

• Speed: Agents are available out of the box, with little setup.
• Native integration: They’re tightly coupled with the workflows of the parent system.
• Lower upfront cost: No need to assemble dedicated teams or infrastructure.

Limitations:

• Confined scope: These agents rarely act outside their parent system.
• Standard performance: Your competitors have access to the same functionality.
• Limited extensibility: Harder to customize or match to your unique processes and decision-making approaches.

Buying works well when the job is narrow, quick, and system-specific. If you want answers faster inside a single app, buying is a fine option. But if your goal is enterprise-wide workflows or differentiated business value, it won’t be enough.

The Case for Building

Building gives you flexibility and ownership. It’s how enterprises create agents that drive competitive advantage.

Benefits:

• Control: Full say over data, security, and decision logic.
• Breadth: Agents can connect across multiple systems, not just one.
• Differentiation: Tailored capabilities that competitors can’t copy. Your agents match your uniqueness.

Drawbacks:

• Resource-intensive: Requires time, talent, and infrastructure.
• Risk of sprawl: Without governance, you end up with duplicate agents, inconsistent quality, and operational overhead.
• Maintenance burden: Keeping custom-built agents running isn’t free.

Building shines when creativity and control matter most. But it also comes with risk. Without strong governance, “build” quickly turns into dozens of siloed projects that lack the right level of IT oversight. That’s not innovation, that’s sprawl.

The Hybrid Reality

For most organizations, the reality isn’t build or buy. It’s both.

• You might buy a ServiceNow copilot to accelerate ticket handling.
• You might build a custom maintenance scheduling assistant that pulls data from equipment sensors, traditional regression models for uptime prediction, maintenance history records, the production scheduling system, technician availability and scheduling data, spare parts inventory database, and more.

This combination makes sense. Buying gets you quick answers and proven integrations. Building gives you tailored capabilities and cross-system reach. Together, they let you start small, learn quickly, and scale with confidence.

But hybrid only works if it’s governed. Otherwise, you’re right back to the patchwork problem. That’s why having a centralized orchestrating platform matters.

A modern AI platform can:

• Provide central creation: Give analysts no-code tools and developers full Python flexibility, while applying the same security and audit rules across both.
• Orchestrate across models and agents: Switch between LLM providers or connect multiple agents without rewriting from scratch.
• Build in governance and observability: Monitor performance, trace every tool call, and prevent drift or bias from slipping into production.
• Capture the uniqueness of your processes and business: Combine the specific expertise of your business specialists and power of your AI professionals to create the decision-making agents that can bring true transformative potential to your company through a composite approach.

That’s the foundation needed to avoid agent sprawl and make hybrid strategies actually scalable.

How to Decide

If you’re weighing build versus buy for agents, start with four simple questions:

1. What do we need the agent to do? Is it a narrow, single-system task or something spanning multiple processes?
2. Where does the action happen? Inside one application, or across data and systems?
3. What’s the priority? Immediate speed-to-value or long-term differentiation?
4. What level of control and governance do we need? Is it acceptable to rely on a vendor’s roadmap, or do we need oversight from day one?

Practical advice: Buy for contained use cases where speed matters most. But plan to build (or more often, to blend build and buy) if your goal is meaningful business transformation.

The Path Forward: A Platform Approach

The build versus buy debate for agents isn’t about picking a side. It’s about finding the right mix for your goals.

• Buying gets you quick answers and faster pilots.
• Building gets you creativity, control, and business impact.
• Hybrid gives you both, as long as it’s supported by the right foundation.

That foundation is a platform approach. At Dataiku, we’ve seen firsthand how quickly agent sprawl can derail even the most promising AI efforts. That’s why we’ve extended our platform to help organizations create, connect, and control AI agents at scale, with governance and flexibility built in.

Wherever you are on this journey, the key is to look beyond shiny tools and focus on what drives real business value. The organizations that succeed with AI agents won’t be the ones that only buy or only build. They’ll be the ones that combine both and make it work across their enterprise.