AGENT OPERATIONS (AGENTOPS)

EXAMPLES Example 1: AgentOps as a discipline A company already has strong DevOps practices. Their servers are monitored, deployments are automated, and error logs are kept. They have an MLOps process for a small prediction model. When they launch an agent, a review shows many gaps. Their dashboards show that the servers are healthy, but nobody can see what goals the agents were given, what plans they made, or the sequence of tools they called. Nothing tracks cost per task across agents. There is no continuous evaluation of answer quality. There is no standard way to require human approval for risky actions. They create an AgentOps practice that adds trace views of goals, plans, and tool-call chains, cost dashboards, an automated evaluation run each night, a policy engine, and a human-oversight process. The old DevOps and MLOps tools remain in use; AgentOps adds to them rather than replacing them. Example 2: Runtime governance A company has several agents built by different teams. One handles payments and others handle research and email. Security staff worry that an individual agent could make a mistake or be manipulated. They deploy a policy engine in front of all agents. One policy says that any action tagged "financial transaction over $10,000" is blocked at runtime and sent to a human approver. An agent, after reading a deceptive email, tries to authorize a 45,000 dollar payment. The agent's own reasoning decided it was fine, but the policy engine blocks the action, logs it, and alerts the finance team. The company-wide rule worked entirely outside the agent's control, which is exactly the point. Example 3: Monitoring and drift An AgentOps dashboard shows that the average number of tool calls per completed task has quietly risen from 4 to 11 over two months. Success rates look about the same, and no individual run appears broken. An engineer investigates and finds that a model update made the agent more cautious, so it double-checks everything with extra searches. Cost per task has grown by 80 percent without anyone choosing that. The team adjusts the prompt, adds a limit on repeated checks, and updates the baseline. Without the drift dashboard, the increase would have been discovered only in a surprising invoice. Example 4: Escalation path A written rule states that any agent incident affecting more than 100 users within one hour automatically pages the senior on-call engineer, and that incidents involving possible data exposure also notify the security lead and the legal team immediately. One evening an agent begins sending garbled replies to users. The monitoring system counts 140 affected users in 25 minutes and pages the engineer automatically. The engineer responds in minutes, pauses the agent, and starts investigating. Without the rule, the problem might have continued until morning, because no one was watching the dashboards.