If you walk into any board meeting or CIO offsite, you’ll hear the same thing:
“We need agents.”
Organizations everywhere are pushing vendors to pitch “agent-powered” solutions, and every IT roadmap is being agent-washed.
Yes, a universe of agents is being built at lightning speed — but on a foundation that resembles a house of cards. Islands of agents are rising across the enterprise, but they are solo agents, with no memory of each other, no shared context, and no architectural foundation to support long-term value.
This is not just an implementation flaw — it’s a foundational architectural gap. Without shared memory, shared context, and shared learnings, no collection of agents will ever scale across an enterprise.
We need to remember our data basics and avoid recommitting the sins of past architectural waves. We need reusable, scalable architectures for Agentic AI — and we need them before it’s too late.
Bring Me More Agents!
A strange new arms race is emerging. It’s no longer about how many dashboards or models you have — it’s about how many agents you’ve deployed.
I keep hearing versions of the same conversation:
“How are you tackling your data challenges?”
“We’re deploying a ton of agents!”
Long pause.
“Why a ton? What you described could be solved by a few targeted agents.”
“But more agents are better, right? We need more agents!”
“Bring me more agents!” has become the new rallying cry — even when the problem doesn’t require more agents at all.
This market-driven FOMO — fed by top-down pressure and competitive dynamics — is rapidly creating agent sprawl and setting the stage for a data and governance crisis. What’s being built isn’t well-architected, interconnected, or intelligent. It’s a proliferation of isolated tools that can’t talk to each other, don’t share context, and aren’t designed for reuse.
That’s not transformation — that’s a modern version of old-school tech debt.
But What Is Agentic AI ?
Let’s baseline on what an Agentic AI system should look like. I don’t mean a chatbot with a nice UI. It plans, acts, and learns from the outcomes of its actions. I’m talking about systems that pursue goals, make bounded decisions, and improve over time based on shared context and feedback. If you’re familiar with the Avengers and Iron Man, I’m talking about a step toward Jarvis.
But before we get into architecture, we should ask: how does Agentic AI benefit the business?
- Better decisions: Agents share context across functions instead of acting in silos. This makes every decision holistic, not isolated.
- Better customer understanding: Agents continuously capture and loop back real emotions, preferences, unstructured feedback, and behavioral patterns. This can help guide how to design products, services, and experiences that reflect how humans want to work.
- Lower tech debt: Agents built on reusable patterns reduce duplication and one-off automations.
If those outcomes aren’t on the table, you don’t have an Agentic AI strategy. You’ve just created a buzzword overlay on top of existing architecture.
Why Reusable Agentic Architectures and Why Now ?
There’s a clear understanding of defined goals. There are plans and options based on reasonable constraints that enable (within defined bounds) autonomous operation. Ready access to institutional memory and context. Learnings are created over time that are shared for enterprise intelligence.
That’s not what’s happening with the wide array of so-called “agents” being deployed that filter a report or change page colors. Those are UI enhancements, not agents. Calling every automation and API an agent might make someone in the organization feel good, but that’s not real Agentic AI.
Real Agentic AI strategies, built on reusable agentic architectures, are:
- Composable by design – Agents are modular and can be orchestrated together.
- Share context – Agents don’t live in silos; they pull from a common knowledge base and data foundation.
- Have standardized interfaces – Agents communicate using consistent APIs and schemas.
- Work from a central memory layer – Interaction data, outcomes, and learnings are stored and accessible to other agents.
- Leverage feedback loops into enterprise systems – Insights and behavioral data are not lost – they’re fed back into the enterprise data fabric.
Today, most implementations fail at nearly all of these.
And here’s the kicker: if you don’t design for reuse and integration from the beginning, Agentic AI becomes just another point solution. It might solve a short-term problem, but it won’t drive enterprise transformation. We’ve seen this before with RPA (bots in silos) and IPA (loosely connected logic).
This will be a huge lost opportunity for organizations, since when deployed correctly for the right reasons, agents can deliver something teams have yearned for for decades. And that’s unique insights based on real emotions, preferences, unstructured feedback, and behavioral patterns that bring you closer than ever to the customer. If you’re not pushing that type of data back into your decisions, you’re throwing away value every day.
Reusability Starts with Data Foundations
For agents to be reusable and intelligent, they need a trusted foundation of high-quality source data, context from metadata, traceability for governance and audits, and a flexible data lake approach that serves as an insight and authorized data layer instead of a data dumping ground.
Landing data does not mean copying everything — it increasingly means landing curated artifacts such as telemetry, features, embeddings, and training sets.
In fact, at PivotX, we’ve started telling clients: don’t measure your progress by “agent count.” Measure it by how many of your agents can reuse each other’s context, decisions, and learnings across the enterprise.
That means the more advanced your Agentic AI strategy becomes, the more important your data architecture becomes.
Without this, agents will behave randomly, without context and at warp speed. With it, they become seasoned analysts – context-aware, adaptive, and genuinely useful.
So, it’s not about how many agents you’ve built. It’s about deploying the right number of agents who can learn from each other, building on shared data and insights and scaling across use cases.
The Future: Few Agents, Many Learnings
Enterprises don’t need hundreds of agents. They need a small number of well-designed agents operating on a shared foundation.
In a well-architected ecosystem, just a few agents can drive exponential transformation because their learnings are reusable across the enterprise.
- Your procurement agent learns from finance interactions.
- Customer service agents tap into emotional feedback from prior conversations.
- Agent-generated insights inform broader analytics and decision-making across the enterprise.
Agentic AI can’t be another checkbox. If it’s going to fundamentally change the way we operate, we must build reusable agentic architectures where context, memory, and learning flow across the enterprise — not just more agents.
