""When contextual, event-driven customer data and external signals are captured and analyzed in real time, the efficacy and responsiveness of AI agents significantly improves," Srinivasamurthy said. Since event-driven agentic automation enables instantaneous decision-making based on the most current information, it not only improves responsiveness for time-sensitive workloads but also reduces compute waste, bringing down cost, he said."
""Agents can proactively respond to events such as errors, updates, or user requests, fostering real-time problem-solving and adaptive behavior. Examples where this is getting traction includes fraud detection, risk management, emergency response, predictive maintenance, dynamic pricing, traffic management, IT operations, and customer experience," Srinivasamurthy added."
Event-driven automation and analyzing data in motion are presented as critical enablers for scaling agentic systems. Capturing contextual, event-driven customer data and external signals in real time improves the efficacy and responsiveness of AI agents. Event-driven agentic automation enables instantaneous decision-making using the most current information, improving responsiveness for time-sensitive workloads and reducing compute waste and associated costs. Agents can proactively respond to errors, updates, or user requests, fostering real-time problem-solving and adaptive behavior. Practical traction appears in fraud detection, risk management, emergency response, predictive maintenance, dynamic pricing, traffic management, IT operations and customer experience. Databricks acquired Tecton, its fourth acquisition this year and second machine-learning purchase after Fennel in April.
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