If you’ve spent any time around agentic AI over the past year, you’ve probably seen some impressive demos.
AI agents that reason, plan, and act autonomously.
Customer conversations handled end-to-end.
Operational decisions made in seconds.
In POCs and early pilots, agentic AI looks transformative.
In production, it’s a very different story.
Across industries, we’re seeing the same pattern repeat itself: teams build promising agentic AI prototypes, only to struggle when they try to operationalize them. Projects stall. Reliability degrades. Costs climb. Security teams get nervous. What looked straightforward in a POC suddenly becomes very hard to run at scale.
The usual explanation is that the technology “isn’t ready yet.” The LLMs aren’t good enough. The prompts need work. The agents need better planning logic.
In my experience, that diagnosis misses the real problem.
Demos and POCs are forgiving environments. They work with limited data, simplified assumptions, and relaxed constraints. Production environments are not.
Operational AI systems have to operate on live enterprise data. They must respond in real time. They must be accurate, consistent, and explainable. They must respect governance, privacy, and regulatory boundaries. And they must do all of this reliably, at enterprise scale.
That’s where many agentic AI initiatives start to break down.
The issue isn’t that AI agents can’t reason.
It’s that, in production, reasoning is only as good as the context an agent can access at runtime.
There’s an important distinction that often gets blurred in AI discussions: analytical AI versus operational AI.
Analytical AI tolerates latency. It works over large datasets. It explores trends, patterns, and correlations. If a query takes 30 seconds or even 1-2 minutes, that’s acceptable.
Operational AI is different.
These are systems that interact with customers, employees, devices, or financial processes in real time. They need precise, current context about specific entities. A customer. An order. A device. A transaction.
In these environments, incomplete or outdated context isn’t just inconvenient.
It directly leads to incorrect actions.
When agentic AI systems are deployed against real enterprise data, a familiar set of symptoms emerges.
First, inconsistent behavior.
The same question yields different answers at different times, because the agent is reasoning over partial, conflicting, or overly broad data.
Second, latency.
Assembling context on the fly often requires querying multiple systems, joining fragmented data, enforcing policies, and transforming results. Even small delays quickly degrade the user experience.
Third, cost.
Large language models charge by volume. When agents ingest far more data than they actually need, inference costs escalate rapidly.
And finally, risk.
Granting AI agents broad access to enterprise systems increases the attack surface and complicates compliance with privacy and regulatory requirements.
None of these issues are visible in a demo or POC. All of them show up in production.
Agentic AI represents a real shift in how software is built. Giving systems the ability to reason and act autonomously is powerful.
But autonomy without discipline doesn’t scale.
In operational environments, the hardest problem isn’t making AI agents smarter. It’s giving them the right context, at the right time, under the right constraints.
Until we treat data architecture and runtime context as first-class concerns in agentic systems, pilots and POCs will continue to promise more than production can deliver. O
ver the next few posts, I’ll share what we’re learning about why context breaks in operational AI, what architectural patterns are missing today, and what it actually takes to run agentic AI reliably against enterprise data.
Because the future of agentic AI won’t be decided in demos, POCs, or short pilots.
It will be decided in production.