What is AgentOps?
AgentOps is the practice of operating AI agents in production — enforcing what an agent may do, monitoring what it costs, and recording why it acted.
Also called: agent operations, LLMOps for agents
In more detail
Where MLOps manages models, AgentOps manages autonomous behaviour. It covers runtime policy enforcement, spend and latency limits, tool permissions, escalation to humans, evaluation gates in the deployment pipeline, and an audit trail detailed enough to reconstruct a decision months later.
Why it matters
Most enterprise AI stalls between demo and production because nobody can answer what happens when the agent is wrong. AgentOps is the answer to that question, which makes it the difference between a prototype and a system a business can be accountable for.
How iLeaf approaches it
iLeaf implements AgentOps through a Governor Engine that enforces limits at runtime rather than documenting them afterwards. Every agent run records its goal, tool calls, reasoning, cost and outcome, and escalates to a person instead of guessing.