AI agents are moving from demos to production workloads that touch real data, real systems, and real business outcomes. According to G2's 2025 AI Agents Insights report, 57% of companies already have AI agents running in production, a clear signal that this is no longer experimental. Yet with production deployment comes a new class of operational burdens: tool access control, auditability, drift detection, and runaway cost prevention. This shift demands a new operating discipline for IT and technology leaders. AgentOps, short for agent operations, is an emerging set of practices for managing the full lifecycle of AI agents in production. It extends principles from DevOps and MLOps to agentic systems, with a focus on reliability, governance, transparency, security, and cost control. Unlike traditional software operations, AgentOps must contend with non-deterministic behavior, autonomous tool use, and context-dependent reasoning. These are challenges that conventional monitoring cannot address, which have been demonstrated in new research. Wang et al. (2025) formalize this in their survey, “A Survey on AgentOps,” proposing a four-stage operational framework (monitoring, anomaly detection, root cause analysis, and resolution) specifically adapted for large language model (LLM)-powered agent systems. This post outlines practical best practices for enterprise AgentOps. It covers goals and guardrails, tool and data connectivity, orchestration for long-running processes, lifecycle governance, human-in-the-loop patterns, and continuous optimization through evaluation and operational telemetry. Later, we map these practices to how the UiPath Platform™ supports agentic orchestration in production. An AgentOps checklist you can reuse Before putting agents into production, teams should be able to answer these questions clearly: Do we know what each agent is responsible for, and who owns it? Can we control what tools the agent is allowed to use, and with what inputs? Can we explain what the agent did on a given run, including which tools it called and what data it used? Can we validate agent behavior before release, not just outcomes but tool choice and execution path? Can we detect drift and regressions using consistent evaluation criteria over time? Can we bound and forecast cost drivers like model calls, retries, context size, and orchestration duration? Can we roll out changes safely with version control, environment promotion, and rollbacks? Do we have a clear human-in-the-loop model for high-impact actions and exceptions?