AgentOps is the discipline of running AI agents in production: putting them live, watching what they do, handling what they hold for people, changing them safely, and stopping them when needed. It treats each agent as a working member of an operation with a job, bounds and a record, not as a model to be hosted.
Why does AgentOps matter?
Building an agent that works in a demo is the easy half. The hard half starts on day two: the held queue is growing, a source system changed its format, a customer complains about a letter, and someone asks whether the agent's certificate is still current. Without a clear way of running agents, each of these lands on whoever built the agent, who usually has moved on to the next one.
It also moves ownership to the right place. An agent that prepares payments belongs to the payments operation, not to the data science team. AgentOps gives the operation the tools and routines to run it.
How does AgentOps work in practice?
The work falls into a daily rhythm and a slower cycle.
Daily - Work the held queue: approve, change or refuse what agents drafted, within agreed service levels. - Watch the signals: volumes, refusal and hold rates, reviewer edits, errors from connected systems. - Handle exceptions the agent could not process.
Periodic - Recertify each agent before its certificate lapses at 90 days. - Review access, sources and customer limits. - Release changes through a controlled process, with a rollback route. - Run incident drills, including the stop.
Operations teams in different sectors weight these differently. In operations at US capital markets firms, trade breaks and settlement deadlines shape the queue. In US fintech operations, fast product change makes controlled releases and recertification the bigger task.
What does an example look like?
A consumer lender runs four agents across collections and customer service. One operations lead owns AgentOps for all four. Each morning she reviews overnight hold rates and the oldest items in the held queue. Each week she checks which certificates expire in the next month and books recertification runs. When the collections agent's refusal rate jumps one afternoon, she traces it to a stale feed of agreed repayment plans, pauses the reminder capability, and restores it once the feed is fixed.
How is AgentOps different from LLMOps?
LLMOps is about running the language model layer: prompts, model versions, evaluation, latency and cost. AgentOps sits above it. It cares about what the agent did in the business, who approved it, whether it stayed in bounds, and whether its authority is still valid. An organisation can have excellent LLMOps and still have no idea which agent sent a given letter. AgentOps also depends on AI observability, but observing behaviour is only the input; deciding, holding and stopping are the output.
Which terms sit close to it?
MLOps runs predictive models. Runbook: the written procedures AgentOps follows. Held queue: the main daily workload. Change management: how agent changes are approved. For how agents move from pilot to production, see our guide to deploying AI agents.
Where Pryme fits
In Pryme, the routines AgentOps depends on are part of the platform: actions above an agent's bounds are held for a named person or refused, every verdict is filed with the bound that decided it, and certificates lapse after 90 days, so recertification is a scheduled part of operations rather than an afterthought.
Questions people ask
Is AgentOps a separate team?
Usually not at first. It is a role inside the operation that owns the agents' work, often one lead covering several agents. Larger firms may add a central team that sets standards.
What skills does an AgentOps lead need?
Deep knowledge of the process the agents work in, comfort reading records and metrics, and the authority to act. Engineering skills help but matter less than process ownership.