Why Agentic AI Struggles In Complex Enterprises

I still remember during the early days of my tech career, I was fascinated by the origins of the internet. What I am still impressed by is neither one single machine nor system, but the elegance of its integration.

The Advanced Research Projects Agency Network, known as “ARPANET,” was developed in the 1960s by the U.S. Department of Defense to establish a communication system “without a central core.” It was the first network to implement packet switching and the transmission-control protocol/internet protocol (TCP/IP) suite. ARPANET wasn’t built on one giant computer; it came alive when different, incompatible systems learned to communicate through some standard protocols. Decades later, that learning still resonates. The very nature of technology has always been about connection and integration.

However, the irony is, today’s sophisticated technologies—AI, generative AI and now agentic AI—put emphasis so firmly on intelligence that I believe we’ve let the core foundation—the integration layer—slip into the shadows. And the result is you have the most glorious agentic AI stack, but you lose the very thing enterprises seek: scale, continuity and measurable impact.

How Agentic AI’s Architecture Puts Integration At The Core

I founded a tech company that specializes in AI and integrations, and everyone I have been talking to lately has this narrative about agentic AI in their mind: It’s a web of smart agents that can think, act and learn. But when I sit down with solution architects, the conversation shifts. We talk about how those AI agents plug into the messy, layered enterprise stack. Because reality is, an agent that can reason brilliantly is still powerless if it can’t push updates to the enterprise resource planning (ERP) software, pull context from the customer relationship management (CRM) platform or respect the workflows buried deep in legacy systems.

And when we dig deeper, our conversations get more exciting around the architectural layers of agentic AI.

• Sense: Gather information from data sources, applications and user inputs.

• Reason: Apply AI and machine learning (ML) models to interpret, decide and strategize next steps.

• Act: Execute tasks across systems, workflows or external applications.

• Reflect: Learn from outcomes and refine future actions.

At its architecture level, this cycle only works perfectly when agents can move seamlessly across enterprise systems. Without proper integrations, the sense phase, for example, could end up operating with limited or shadow data inputs. And this makes agents not see the full context. The act phase could be constrained to work within systems like applicant tracking (ATS), CRM or ERP instead of moving across them. And when I look at the reflect phase, I see it struggling too because without proper feedback loops across the enterprise, the system cannot truly learn or improve.

How To Determine If You’re Merely Building Silos

The average large business is said to use 367 software applications. Each is adopted to solve a specific problem or improve efficiency. Yet, together, they can create a fragmented impact.

Here is how it unfolds: The more fragmented the enterprise stack becomes, the less effective agentic AI can be. That’s because intelligence alone isn’t enough. Without your company’s tools working in sync, even the most advanced AI agents are forced to operate in silos. They’re able to reason but are unable to act across the systems where enterprise value truly lives.

Let me make this more real with a simple example. Imagine an HR team trying to streamline hiring with agentic AI. Having ignored integrated agentic intelligence, they are now facing a challenging situation. Profiles get shortlisted but remain siloed. Invites go out, yet the candidate stage still has to be updated manually in the ATS, and since the background check system isn’t aware of the candidate stage recorded in ATS, it requires manual triggers.

This is why I believe it’s important to validate whether you’re truly unlocking value or just stacking agents horizontally. I ask myself a few simple questions:

• Do agents see all systems or only small chunks?

• Can they push changes back into ATS, CRM, ERP and beyond?

• Are outcomes feeding back to improve reasoning, or are they ignored?

• And, last, are workflows stitched end-to-end, or are they stuck?

How To Ensure Agentic AI Delivers Value: Key Strategic Shifts

When I look at how most organizations approach agentic AI, I see a pattern. They pilot a few agents, embed copilots or add intelligence on top of their existing stack. But I believe that without key strategic shifts, these efforts risk delivering zero value.

In my view, the most strategic move you can make is to architect it with integration at the core so systems can act in sync. Focus on the infrastructure beneath AI by ensuring robust APIs are in place; establishing data pipelines that ensure clean, timely and accessible data for AI agents; and adopting middleware, or platforms that enable these agents to communicate and act seamlessly across core business systems like HR, finance and supply chain.

I also notice a tendency to chase agentic AI adoption with a one-size-fits-all approach. From my experience, depth matters more. Domain-specific agents built for HR, supply chain or finance, for example, can deliver far greater value. And when it comes to governance, trust and security, I see them as essential safeguards to be applied from day one.

Data is another area where I see a shift is needed. If it’s viewed only as “inputs” for models, much of its value is lost. Thus, we should treat data as infrastructure. This means managing data required for AI agents—organizing it so it is reliable, connected and easily accessible—so that AI agents can easily seek the intelligence they require from that properly arranged data.

Finally, I’ve learned that relying only on vendor platforms narrows the possibilities. A hybrid ecosystem made of fit-for-purpose tools and custom AI agents can drive scale and impact.

You can have the brightest minds or the most advanced AI agents, but if they’re trapped in silos or at the wrong place, their brilliance can’t create real impact. I strongly believe that insights aren’t meant to remain trapped, and workflows aren’t meant to stall. Proper integration can help free intelligence, move workflows as intended and enable AI to act across systems to deliver true enterprise value.