The conversation around AI is shifting in meaningful ways. We might characterize the latest developments broadly as moving from questions of experimentation to questions of production. This means that many organizations have moved past the initial wonder of AI and entered a phase of practical execution.
With this shift comes a change in focus.
For many organizations, the ultimate goal is no longer just a conversational interface but the deployment of Agentic AI. These AI agents represent the next level of AI impact, autonomous systems that can reason, plan, and execute complex workflows across the entire business landscape.
This raises one important question. What’s required for agentic AI success in production?
Why all roads lead to the context layer and data foundation
The answer is resoundingly, a focus on the data foundations and context layer needed to construct the agentic grounding to prevent LLM hallucination and maximize accuracy.
To achieve this, you can think of agentic success as a factor of three criteria.
| Agentic AI Success Criteria | Proof of Agentic AI Success | |
| 1. | Is the agent accurate? | Did it generate the right answer? |
| 2. | Is the agent consistent? | Can the agent get the right answer more than once? |
| 3. | Is the agent auditable? | Can others understand how the agent got the answer? |
What’s needed to get there? All three of these success metrics depend on the same two basic ingredients:
- The right data
- The right context
How your agents get the data and context that they need relies on access. Specifically, it relies on the ability to access both data and business context, both unstructured data and structured data, both cloud data and on-premises data.
Starburst is uniquely positioned to advance this change. From day one, we built Starburst on the notion that you needed access to data wherever it lived. In the age of AI, that foundation is exactly what enterprise AI agents need to derive trusted, auditable insights at scale.
AI & Datanova is built to drive the conversation around Agentic AI forward
To help drive this forward, we’re calling together our largest customers and supporters to share the best practices, hopes, and excitement around this process.
On May 27 and 28, Starburst will host AI & Datanova 2026 in Miami Beach, focusing on AI and production, especially agentic AI. This will be a conversation about what is required to get AI into production, what is needed to make it a real success in an enterprise setting, and the bottlenecks to getting there.
We’ll also explore the transition from simple chatbots to sophisticated agents that take action, and take a deep dive into the unity between accessing data and business context that’s making AI successful in production.
Let’s take a look.
Empowering AI agents with federated data
An AI agent is only as effective as the data it can access. For example, if an agent is tasked with resolving a complex supply chain disruption, it needs real-time data from logistics providers, warehouse inventory, and historical shipping trends.
That’s structured data, transactional data.
This creates a few problems at once. First, these datasets rarely live in one place, which makes data centralization a nonstarter. Instead, accessing that data requires a federated architecture, and that’s just what we see. Enterprises are deploying federation as a solution to the data access problem required by AI.
Driving the conversation around AI with NVIDIA and Dell
The problem isn’t theoretical. It’s happening each day in production environments at the largest enterprises in the world, and others in the data industry are quickly converging on the need for GPU-powered infrastructure to support AI at enterprise scale.
At AI & Datanova, speakers from NVIDIA and Dell will discuss the latest shifts in this space, including the impact of infrastructure on AI production success.
Data Products as the Execution Layer for AI Agents
There are other considerations as well. Agentic AI doesn’t fail because the model is wrong. It fails because the data lacks real meaning for the business. For example, a human analyst knows that “revenue” excludes refunds, or that “churn” has a 90-day window, and which table is authoritative. An agent doesn’t. Without the business context codified, it guesses. In a regulated enterprise, that’s a major problem.
Data products solve this today. They are the execution-time contract between your business and your AI. They handle certified tables, joins, metric SQL, and business rules that give agents the precise, governed context they need to act reliably. Rather than leaving interpretation to chance, data products encode what the data means and how it should be queried, once, and enforce it everywhere.
But as enterprises scale agentic workflows across teams, systems, and use cases, a new challenge emerges. Agents need to know not just how to query the right data product, but how to find it. That’s where we’re heading next, organizing data products within governed business domains so that discovery is as reliable as execution.
Bala Natarajan from GEICO will share how data products are powering their transition to active AI agents, giving agents the ground truth they need to navigate complex business logic without human intervention at every step.
The agentic control plane and governance
Nowhere is this more important than in regulated industries. In sectors like financial services and healthcare, the move to agentic AI introduces new risks. An agent that can take action must be governed by strict guardrails. These controls define what data an agent can access, what actions it can take, and how those decisions are logged and reviewed.
Sessions featuring Citizens Bank, Highmark Health, and TIAA Financial will focus on the concept of the Agentic Control Plane. This architectural layer enforces fine-grained access controls and maintains automated lineage for every action an agent takes. We will discuss how to implement these guardrails across hybrid and multi-cloud environments to ensure agents remain compliant with global regulations, including data sovereignty requirements.
Why access to data and context is making or breaking AI success today
Overall, it’s worth reflecting on the moment we find ourselves in.
The shift toward Agentic AI represents the next frontier of enterprise intelligence. However, the gap between a successful agent and a failed experiment can almost always be traced back to the union between the data foundation and the business context it relies on.
AI & Datanova 2026 is an opportunity to discuss the technical lessons and peer-led discussions needed to turn autonomous agents into a reality in production. Whether you are a data architect building the underlying pipelines or a technology leader responsible for AI strategy, these sessions will provide the roadmap for an agentic future.
Join us for AI & Datanova 2026, May 27 and 28. Secure your spot to learn how the world’s leading organizations are building the trusted data foundations required for the Agentic AI era.
Register for AI & Datanova 2026
