Your AI agent aced the demo. Your data may still derail it.

Ahhh, demo AI agents. Don’t you just love them? Cute and cuddly, solving all your company’s problems without complaints, missteps, or even a stray hiccup. You’re feeling proud, accomplished, and ready to release your “baby” out to your eagerly waiting users.

The only problem is that the adorable AI agent made a complete mess in the real world. It couldn’t handle the disparate sources of information. Or it didn’t know what to do with data that changed daily, if not hourly. Or maybe it wasn’t prepared to manage system permissions. Whatever went wrong, building a convincing demo agent is increasingly straightforward. Launching one in production, however, is not.

Join the live conversation: On October 14, Ravi Marwaha, COO and Chief Product & Technology Officer at Arango, breaks down six data requirements to move your AI agent from the demo phase into production. He’ll share practical examples showing that a successful demo showcases your agent in a controlled environment, but doesn’t prove your surrounding data architecture can reliably support it in production.

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You might be thinking it’s a retrieval issue; it’s more than that. You probably already tested your vector search or RAG in the demo, and they might have worked perfectly well. Your agent can have semantically relevant information and still not have the right context. Production agents need business context to take informed actions that move your business forward. For example:

What if a customer appears differently across multiple systems?

What if the retrieved information is relevant, but not current?

What if you need to know why an agent made a particular decision?

And if you think building the right business context for your agent initially is tough, try keeping that context accurate as your business changes over time. Agents can also magnify the problem as they repeatedly retrieve information, make a decision, take an action, and use that result in future steps. Stale information can easily pollute later steps.

If your business logic is spread across many different sources, your agent has to comb through multiple resources and reconstruct context on the fly for each task. This leads to inconsistency, increased latency, and higher costs, not to mention more manual work for your team. How will you enforce data governance when context is fragmented across so many systems?

There’s a better approach: Build a persistent, unified business context instead of asking your agent to reconstruct it for every task. Your architecture needs a contextual data layer that your agent can rely on. Of course, this is only one aspect of preparing your system for production.

On October 14, you’ll learn which aspects of your data infrastructure deserve a closer look before deployment. Ravi Marwaha from Arango joins us live for “Six Data Requirements to Get Your AI Agents from Demo to Production.” He’ll cover real production patterns from the financial services industry and clinical research.

Register here to join us on October 14 and get an assessment framework to test if your agentic system is ready to deploy.

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