Low-Latency Grid Intelligence with Self-Governing Stream and Calibration Agents

Wide-area power-grid observability depends on phasor measurement unit (PMU) streams, calibration-aware instrumentation, and fast operator workflows, but the analytic stack that joins these elements is often split between stream processors, model-serving systems, privacy filters, and manual maintenance queues. This paper proposes Self-Governing Grid Intelligence (SGI), a synthetic cyber-physical stream-processing architecture in which bounded agents coordinate PMU telemetry, calibration-risk transformers, long-horizon forecasts, anonymization controls, and model-context-protocol contracts under policy-verified cloud-pipeline governance. SGI extends the policy-verified agentic DataOps fabric of S01 to the grid domain by adding sub-second latency budgets, measurement-quality gates, calibration risk scoring, and maintenance-action escalation. We define the architecture, a latency-and-risk control algorithm, and a simulated benchmark over wide-area disturbance monitoring and instrument maintenance scenarios. In the synthetic benchmark, SGI reduces P95 event-to-decision latency by 61.5% relative to a batch-calibrated PMU baseline while improving calibration-risk F1 from 0.68 to 0.83 and eliminating unauthorized privacy-policy violations. These results are simulated and should be interpreted as an executable design target rather than evidence from a production utility deployment.