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AI Agents Blindfolded: The Data Gap
27 Jan
Summary
- Agentic AI needs sensory data, not just models and infrastructure.
- Lack of operational data makes AI agents blind and dangerous.
- Unified, contextual, and traceable data infrastructure is key.

Organizations are rapidly adopting agentic AI, aiming for proactive operations. However, a fundamental flaw exists in this transformation, with excessive focus on AI models and infrastructure, neglecting the crucial 'senses' – the operational data AI agents require. This oversight risks creating expensive and dangerous chaos, akin to a self-driving car without sensors.
Agentic AI, whether for security or customer service, needs continuous, high-quality machine data. This includes real-time operational awareness across the technology stack, contextual understanding to correlate information, and historical memory to recognize patterns. Without these sensory capabilities, AI agents make critical decisions while effectively blindfolded.
The data infrastructure for successful agentic AI has long been a low priority. Poor data quality, manageable in traditional analytics, becomes an immediate operational risk in agentic environments due to the speed of AI. This amplifies data problems, turning them into existential threats.




