AI Agents Enable Signal-Driven Autonomous Workflow Execution

This article explains how AI agents and orchestration layers convert noisy alerts into actionable execution in today's AI-driven organizations. It highlights that thousands of daily alerts often lack context, and describes architectures—data ingestion, inference, decision, and execution layers—and techniques like prioritization, feedback loops, and autonomous agents to close the signal-to-action gap. The piece outlines use cases across IT, security, customer experience, and supply chains, and implications for skills and system design.