AI is evolving from static question-and-answer systems to dynamic agents capable of reasoning, learning, and making decisions in real time. These intelligent systems rely on vast amounts of data to function, but the real challenge lies not in building the AI itself, but in ensuring that the data feeding it is clean, accurate, and accessible.

In this episode of ATP, Shanmuga Muniandy, Director of Architecture & Chief Evangelist, APAC at Denodo, argues that most AI failures are really data failures: stale copies, siloed systems, and patchwork security.

Some of the topics that Shan covered in detail included:

Agentic AI chains reasoning steps, maintains context, calls tools/APIs, and makes decisions toward a goal.
To get accurate agent behavior, data must be complete, clean, contextual, and timely.
Real-time decisions require live context, which requires real-time access to data.
Business leaders should not need to hunt tables or beg IT for extracts. Self-service data unlocks velocity. It also gives agents stable, well-defined inputs aligned to outcomes.
Ethics and regulation should be part of the architecture that drives Agentic AI.

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