Governing Distributed Systems with Intelligent Agents

Large distributed systems are now operated through layers of schedulers, container controllers, service meshes, monitoring pipelines, and human runbooks. These mechanisms improve scale, but they also create governance problems: local controllers can fight one another, remediation rules may violate service-level or compliance constraints, and operators often lack a compact record of why a system changed. This paper presents Policy-Bounded Agent Governance (PBAG), a 2021-era architecture for coordinating intelligent software agents that monitor, diagnose, and repair distributed systems while remaining subject to explicit operational authority. PBAG combines local agents near services and clusters with a governance plane that checks action rights, conflict constraints, freshness requirements, and audit completeness before any adaptation is committed. The method builds on autonomic computing, agent-based software engineering, self-adaptive architecture, cluster management, cloud elasticity, and machine-learning systems practice. A controlled prototype study evaluates PBAG across four operational scenarios: load surge, replica degradation, stale forecasting input, and accelerator-backed training contention. Compared with manual runbooks and independent local agents, PBAG reduces median recovery time by 37%, lowers conflicting actions from 18 to 4 per 120 incidents, and keeps p95 request latency within 7% of a static-policy baseline. The central result is that intelligent agents are most useful for distributed-system governance when their autonomy is bounded by shared policy, explicit conflict resolution, and durable decision records.