AgentOps
Also known as: agent operations, AI agent operations
As organizations deploy AI agents to handle real tasks in production, managing those agents becomes a distinct operational challenge. AgentOps is the name for the practices and tooling being built to meet that challenge. It borrows from MLOps and DevOps but is specifically oriented toward the unique properties of agents: they take sequences of actions, call external tools, and make decisions that may be hard to reverse.
Observability is the core problem. A traditional software bug leaves a deterministic error log. An agent failure might involve a correct tool call that returned misleading data, which caused the model to reason incorrectly, which led to a subtly wrong action three steps later. Tracing the full decision chain, not just what the agent did but why, requires purpose-built tooling that captures the agent's reasoning process alongside its actions.
The term was gaining traction in 2025 as enterprises began running agents at scale. Analyst firms including Deloitte anticipated companies building dedicated AgentOps functions as agent deployment matured. For any team running agents in production, the minimum viable AgentOps stack includes logging of tool calls and responses, alerting for stuck or looping agents, and a mechanism for human review of high-stakes decisions.