AI Agents Can’t Save You: Build Smarter Foundations Instead

Globant’s Esteban Sancho offers commentary on why AI agents can’t save you and recommends building smarter foundations instead. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Everyone is selling AI agents. Few can explain what they actually do.

The term is everywhere — in decks, headlines, demos, etc. But ask five people to define it, and you’ll get five different answers. That’s not a trend. That’s a problem.

Before companies can unlock any value from agentic AI, they need something more basic: clarity, alignment, and a digital foundation that can actually support it.

An Agent Means Everything: That’s the Issue

Some vendors define agents as autonomous systems that act independently. Others call them enhanced workflows powered by large language models. Some are just glorified chatbots.

The result? Confusion. One company’s “agent” is another’s helpdesk script.

Executives expect adaptive systems. Teams deliver pre-trained prompts. The disconnect creates hype on the outside and frustration on the inside.

But the real blocker isn’t the definition. It’s the infrastructure.

You Can’t Deploy What You Can’t Support

Most companies aren’t ready for agentic AI. Not because the tools don’t exist but because their systems can’t support them.

Fragmented data. Outdated backends. Weak security protocols. You can’t hand AI decision-making power when it only sees half the picture. Or worse, when it sees sensitive data with no safeguards.

Agents don’t thrive on theory — they require a modern architecture underneath. That means secure APIs, integrated data flows, clean governance models, and a shared understanding of what you’re even trying to build.

Start With Clarity

Don’t wait for the industry to settle on a definition. Start with your own.

Is an agent in your company a self-directed system with autonomy? Or is it a tightly scoped automation embedded in a workflow?

Without that internal clarity, teams pull in different directions. One builds a script. Another expects a strategist. Alignment breaks before anything ships.

Define what an agent is and what it isn’t for your business, then map it to your real processes. Where in your order-to-cash cycle, customer onboarding, or compliance workflows could an agent remove friction or reduce manual effort?

The companies making progress aren’t chasing AI frameworks. They’re picking use cases that matter and anchoring their definitions in real outcomes.

Unblock Your Data

AI can’t act intelligently without full access to context. And most organizations today are running on fragmented, outdated, and siloed systems.

A McKinsey survey found that nearly half of companies have adopted some form of AI – but only 21% have scaled it across multiple business units. Why? Infrastructure and talent gaps.

Your agent is only as smart as the data it can access. If it’s operating on partial snapshots or stale records, it won’t perform. Worse, if it has access to unprotected systems, it becomes a risk.

These foundational gaps don’t just impact AI agents, they stall broader digital transformation efforts too.

Many organizations have invested heavily in analytics, automation, and customer experience tools, only to discover that none of it scales without reliable, secure data infrastructure underneath. Agentic AI just makes that weakness more visible and more urgent to solve.

Modernization isn’t optional. It’s the entry ticket. That means:

• Clean, connected systems

• Real-time data pipelines

• Secure, governed access

• Traceability and audit trails built in

If your data doesn’t flow, your agents won’t either.

Train the System. Train the People

AI agents won’t succeed in isolation. You need both technical refinement and cultural readiness.

Culture matters. Rolling out agents without preparing people is like introducing new machinery without training the operators.

You don’t need every team member to become an AI expert, but they do need to understand how agents fit into their workflows, where the guardrails are, and what to do when things go wrong. Empowered teams spot weak points, raise flags, and help the technology improve faster.

Start small. Run pilots. Don’t test for perfection, test for value. Can this agent reduce cycle time? Eliminate manual effort? Surface insights faster? Use the feedback to improve, then scale.

As you train your models, train your teams. AI literacy isn’t a side project. It’s a business capability. The more people understand how agents work and where they fit, the more likely they are to use them effectively and safely.

No Shortcuts; No Exceptions

Using AI agents without readiness is not innovation. It’s exposure.

It risks bad decisions, compliance failures, and reputational damage. One mistake, one breach, one system breakdown, can wipe out years of progress.

The promise of agents is real. But it only becomes reality when companies stop chasing buzzwords and start fixing what’s underneath.

The Smartest Wins Will Be Boring

The winners in this space won’t be the fastest. They’ll be the most prepared.

The companies that define clearly, modernize early, and invest in their people will build the systems agents need to thrive.

AI agents won’t save you – but smarter foundations might.