The race to deploy AI agents for data work is intense, focusing on tasks like SQL writing and pipeline debugging. However, many AI implementations fail due to agents lacking crucial context about data platforms, leading to erroneous outputs. This “context wall” highlights the gap between AI capabilities and operational reality. Most AI strategies ignore the need for live, operational context, resulting in systems that are unreliable for critical decision-making.
Effective orchestration functions as a “flight recorder,” capturing essential metadata on data lineage, health, and ownership, which is vital for trustworthy AI, especially in regulated industries. An AI-native platform integrates orchestration intelligence, enabling agents to assess data quality based on real-time analytics. This transforms AI from a superficial tool into a reliable part of the data ecosystem. Ensuring that AI understands operational nuances is key to enhancing accuracy and safety in data-driven decisions, ultimately paving the way for a more dependable AI future.
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