Agentic AI Runs on Data: What Enterprises Must Build Before Deploying Multi-Agent Systems

A year ago, most enterprise AI conversations still focused on whether a model could “summarize this” or “classify that.” Then the questions changed, and they changed quickly. That’s when clients started asking whether a model could take an action on its own.

“Once your AI can act, it’s a workflow you have to trust,” says Raja Rajenderan Anbazhagan, Vice President of Technology at Blackstraw, where he leads the Cloud and Data Engineering Practice. “Workflows don’t run on prompts. They run on data. For agents to perform, that data must be governed, traceable, current, and consistent across systems. If the data foundation is weak, agentic AI will magnify the mess.”

How enterprise agentic AI takes the model from one brain to a team

To understand why data is foundational for agentic AI, it helps to stop imagining a single model reasoning like one brain in a vacuum. A better way to think of multi-agent systems is as teams.

Each agent has its own assigned role to play. It finds the data it needs to get its work done and completes the task, then passes context and results to the next agent.

Enterprises already operate in ways that resemble this model. Everyday workflows take human teams through handoffs between departments such as finance, supply chain, customer ops, and IT. Each team is supported by a stack of SaaS tools and legacy systems that store data. Agents can compress those handoffs from hours to seconds, and do so continuously without fatigue.

Raja sees the move to agentic AI as already underway. “These agentic systems are the next standard operating model for large enterprises, but the timeline depends entirely on whether or not the underlying data and governance are ready. Unfortunately, most companies aren’t there yet.”

In other words, many multi-agent systems are poised to fail. However, they won’t fail because the model is not smart enough. They will fail because the agents can’t reliably find the right order status, the latest pricing contract, the valid return policy, or the customer’s current entitlement, and then prove what they did.

Data trust and observability are prerequisites for agentic AI automation and multi-agent systems

Agents that make decisions based on scattered or untrusted data make mistakes. Worse still, they make them quickly and with a confidence that feels convincing.

In the pre-agent world, a pipeline hiccup might mean a dashboard refreshes late. In today’s world with agents, however, that same hiccup can trigger automated actions. Cases may be created, and orders may be reprioritized. Refunds may be issued, and customers may be messaged. But the most troubling thing is that all of these tasks will be done based on bad context.

To help organizations prepare for these systems, Raja always starts with the part nobody wants to talk about: the data foundation.

“Creating a strong data foundation requires the hard engineering people tend to postpone,” he says. “You get data out of operational systems and into AI-ready layers through governed pipelines. You enforce reliable identity and access controls so agents can only see and do what you allow them to. You build observability so failures and strange behavior are detected before they turn into business incidents.”

In practice, Raja’s teams look for five things before an agent is allowed to act:

• Governed, AI-ready data: Curated datasets with named owners and freshness targets, so an agent’s “latest price” really is the latest.
• One catalog with lineage: Agents discover data through a governed catalog, not ad hoc connections, so every answer traces back to its source.
• An identity for every agent: Each agent gets its own scoped permissions for data and tools, never a shared super-user account.
• End-to-end tracing: Every retrieval, tool call, and handoff is logged and tied to the decision it informed.
• Evaluation and escalation rules: Agents are tested against known-good outcomes before release, with clear thresholds for when a human takes over.

Architecture for enterprise agentic AI requires centralized tracing and shared guardrails

If multi-agent systems behave like teams, they cannot operate without the accountability that governs human teams. An enterprise can’t deploy dozens of agents and then shrug when someone asks, “Why did the system do this?” They need to know what it saw and which tool it used. They need to be able to find out what logic it followed and what policy constrained it.

Every decision and handoff must be logged so that it can be explained after the fact. This is what makes incident response and iterative improvement possible at enterprise scale.

“We built out a strong data foundation for a client recently,” reflects Raja. “The result was one shared workspace where dozens of agents run under a single set of guardrails instead of every team building its own black box. It cut their time to resolve an agent failure by 60-70% and got them above 95% traceability on every decision the agents made.”

The real competitive edge is agentic AI automation that no one needs to babysit

Enterprises love the idea of autonomy right until an agent makes a costly mistake. Then, autonomy vanishes and “human in the loop” becomes a permanent crutch. Most of the time, that’s a foundation problem, not a model problem.

Agentic AI is coming, but in most cases, the required data foundation is not ready. Good data engineering does much more than reduce errors. It raises the ceiling for autonomy.

When the platform can prove what the agent did, constrain what it can do, and detect issues early, organizations can let agents carry more of the workflow without fear.

“Models will keep getting better and keep getting swapped out,” says Raja. “The data foundation is what lasts. Build that first, and every agent you deploy after it gets safer and more useful.”