Measuring Success: KPIs for Agentic AI in Data Quality Management
Agentic AI systems now monitor, correct, and negotiate data integrity across enterprise systems. They operate semi-autonomously, making decisions that once required human judgment. But here's the challenge: how do we know they're actually performing well? Success in this new paradigm isn't just about accuracy. It's about trust, speed, resilience, and measurable business impact.
Synthetic Sensors and Surrogate Data: How Agentic AI Fills Gaps and Fights Fraudulent IoT Streams
What happens when your sensors lie, or simply go silent?
In our hyper-connected world, IoT systems power our most critical infrastructure. Smart factories run around the clock. Energy grids balance supply and demand in real time. Logistics networks track millions of shipments across continents. All of this depends on one thing: continuous, trustworthy data streams flowing from thousands of sensors.
The traditional answer has been redundancy and maintenance. Add more sensors. Check them more often. But there's another way, one that doesn't just patch the problem but reimagines how we think about sensing itself. Agentic AI offers a path beyond physical sensors, using intelligence and inference to synthesize and validate the data we need.