Agentic AI is making headlines, but research shows mixed results for enterprises today, with some sources finding that only 14% have brought agentic AI into production. Why the discrepancy between promise and reality? Many organizations are just not set up for agentic AI, which gives “AI ready” a whole new meaning. In this post, I’ll preview some of the insights from one of our upcoming webinars, which explores the stringent requirements for agentic AI, and how to address them with the right data foundation.
A Whole New Ballgame: Why Agentic AI Breaks Traditional Data Pipelines
Why does agentic AI have such different data needs than traditional AI applications? Many around the web have compared the difference between AI and agentic AI to the difference between navigational apps and self-driving cars. Not only do they figure out where to go, but they actually take the turns to get you there, acting in the now. So when it comes to data, they need live data that describes the current moment. They also need access to the right data, not an interim dataset that might have been used once in a training module. And finally, they need guardrails, so that – even if they “wanted” to – they couldn’t, for example, delete a production database.
Live Data: Why AI Agents Fail on Batch Snapshots
Traditional data pipelines move data into central repositories, converting operational information into historical snapshots. Though this works well for BI reports, autonomous agents need live situational awareness at inference time. The moment an agent relies on an outdated data extract… well, that is the moment its decisions will begin to waver and lose accuracy.
Unfortunately, even the latest, most fully equipped data lakehouse cannot deliver live access across all applicable data sources, inside the lakehouse, outside the lakehouse, and in one or more cloud systems. However, inserting a dedicated AI data layer across all these sources connects agents to live source systems on demand, eliminating the latency issues caused by continuous, physical data replication. AI agents don’t really have time for any latency beyond a few milliseconds.
The Right Data: Preventing Failure through Active Context
Having access to live systems is only half the equation; an agent must also be able to always access the right data for the need of the moment. With typical enterprises managing dozens of database technologies and tools, or more, enterprise data sprawl makes direct, uncurated queries unpredictable. Expecting an AI agent to locate the exact right field within thousands of raw tables will lead to processing gaps and hallucinations.
Agents need active context — a combination of metadata intelligence, live availability, and the standardized business definitions made possible through a cross-platform semantic layer. Rather than exposing raw database structures, organizations succeed by delivering pre-curated, API-accessible data products, tailored to specific workflows, and enhanced with active context.
In addition to live data, an AI data layer can also provide agents with access to the right data through active context, no matter the complexity of the underlying infrastructure.
Trusted Guardrails: Keeping Agents’ Power in Line
Because agentic AI operates with autonomy, the blast radius of an error is significantly larger than that of a static dashboard or standard chatbot. AI agents, by their very nature, can cause damage as well as good, as I mentioned above, so they need to be reined in by effective guardrails. To enable these, you cannot simply apply data governance policies across individual source systems. By establishing centralized access controls, security policies, and audit trails within your AI data layer, guardrails are automatically enforced at the point of query, preventing foreseeable catastrophe.
Start Your AI Engines
The good news is that an AI data layer can be added above any existing data infrastructure, including data lakehouses and cloud applications, without requiring complex lift-and-shift maneuvers. To learn more about how an AI data layer can meet the requirements of agentic AI, join us for “Winning with AI Starts With Data.” Featuring guest speaker IDC Research vice president Stewart Bond and data/AI experts from Denodo, this session will provide deep insights on the current trends around agentic AI, with practical strategies for strengthening your data foundation for autonomous agents.
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