AI Agent Operations

AI Agent Operations is the operating discipline for running AI agents reliably, safely, and economically after the prototype stage. It covers session and task management, tool permissions, API keys, rate limits, queues, logs, monitoring, fallback models, and clear human escalation paths. Unlike classic MLOps, AI Agent Operations does not only manage a model or prediction pipeline. It manages an acting system that can execute code, change files, query databases, call APIs, or coordinate other tools over time. Teams therefore need visibility into which agent is doing which task, which tools it can access, what each run costs, and when a human decision is required. Strong agent operations connect observability, governance, and infrastructure: logs explain behavior, control planes limit risk, capacity planning prevents outages, and runbooks make incidents repeatable to handle. The term matters because production agents otherwise become hard-to-audit one-off automations. With an operations layer, they become manageable digital workers that can be measured, controlled, improved, and scaled across teams without losing accountability.