AgentOps (agent operations) is the practice of running AI agents in production: monitoring them, changing them safely, and deciding who owns what after go-live. It applies habits from DevOps and MLOps to agents that talk to customers and act in business systems.
The name is also used by an open-source developer tool that "helps developers build, evaluate, and monitor AI agents." This entry covers the practice.
For a contact center, go-live is often where much of the change work begins. Prices change, products launch, policies get rewritten, and customers find new ways to ask old questions. Each of these is a change to the agent.
What AgentOps covers
Monitoring. Resolution, transfers, failed conversations, response time, and cost, tracked daily and per request type (see AI quality monitoring).
Change management. Each change goes through a staging version, a regression test, sign-off, and release, with the previous version ready to restore.
Incident response. A wrong answer or an outage gets an owner, a fix, and a new test case so the same failure is caught next time.
Model and vendor lifecycle. Model providers release and retire versions on their own schedules. A model change can shift behavior across every request type at once, so it goes through the same tests as any other change.
Records. A step-by-step record of each conversation and each change to the agent supports audits and complaint handling (see AI audit trail).
NIST's AI Risk Management Framework, a voluntary US framework, covers similar ground. Its MANAGE function calls for post-deployment monitoring plans with mechanisms for "decommissioning, incident response, recovery, and change management." It also asks for ways to "supersede, disengage, or deactivate AI systems" whose results are inconsistent with their intended use.
Who owns the agent after go-live
A common split gives the contact center ownership of what the agent says and does. IT, compliance, and the vendor each own a defined part.
Agent owner. One named person in the contact center, often a team lead or CX analyst, who signs off changes to flows and watches quality.
Content owners. The people who own the policies behind the answers, in product, pricing, or operations. They approve changes to knowledge.
Compliance. Signs off changes to regulated flows, such as disclosures, identity checks, and payment arrangements.
IT. Owns integrations and call routing, often on its own release calendar.
Vendor. Owns the platform, uptime, security, and model updates, with the agent owner's sign-off on any change that affects behavior.
An AgentOps rhythm
Daily. The agent owner checks a short dashboard and opens a ticket for anything unusual.
Weekly. The agent owner, a content owner, and the vendor's contact review sample conversations, the top failure reasons, and the change backlog. Small changes ship that week.
Monthly. Operations, compliance, and IT review results per request type and check a sample of regulated conversations against the audit log.
Quarterly. A business review with the vendor covers cost per resolved conversation, the model roadmap, and the exit plan.
Frequently asked questions
What is the difference between AgentOps, MLOps, and LLMOps?
MLOps covers training, deploying, and monitoring machine learning models. LLMOps applies the same ideas to language models, including prompts and model versions. AgentOps adds what agents do: tool calls, actions in business systems, and the people who own each change.
Who should own an AI agent after go-live?
In many contact centers, a named agent owner in the customer service team is accountable for the agent's behavior, and compliance signs off regulated flows. IT owns integrations, and the vendor owns the platform. A short RACI makes the split explicit.
What should be monitored for an AI agent in production?
Resolution and transfer rates per request type, failed or abandoned conversations, response time, and cost. Quality scores across conversations, plus a sample of regulated flows checked against the audit log, show problems that totals can hide.
How do you change a live AI agent safely?
A common path starts with a staging version, the regression set, and a few test calls or chats. After sign-off, the change goes out at a quiet time and the first day is watched closely. The previous version stays ready to restore (see phased rollout).
How we approach this at Zowie
Zowie builds AI agents for voice, chat, and email. In each conversation, the language model speaks. A separate, deterministic Decision Engine returns every amount, rate, offer, disclosure, and escalation from your rules, with an audit log. If you would like to compare notes on running an agent after launch, you are welcome to book a demo.
Explore: AI agent evaluation, AI quality monitoring, Traces, Supervisor, AI audit trail, Phased rollout