Who runs your AI agents in production? AgentOps explained
Short answer
AgentOps is site reliability engineering for agents: someone owns their uptime, quality, cost, and change history the way an SRE owns a service. Without that owner, agents fail quietly, with wrong answers, loops, stuck jobs, and runaway token spend, and nobody can say what an agent did last Tuesday.
What AgentOps covers
- Monitoring: task success, accuracy against an evaluation set, latency, exception and retry rates, tool-call failures, token spend, and user feedback.
- Incident response: on-call, runbooks, and a kill switch per agent.
- Release management for prompts, models, tools, and workflows: versioning, staged rollout, and rollback.
- Audit trail: which agent ran, on which inputs, with which tools and permissions, and what it changed.
- Permissions: least-privilege credentials for every tool an agent can call, and human approval for irreversible actions.
Why agents need more than classic SRE
- Outputs are non-deterministic, so "it worked yesterday" proves little.
- The model provider can change behaviour or pricing underneath you.
- Prompts and workflows are code but are rarely versioned or reviewed like code.
- Cost grows with every call, retry, and token of context.
Regulated teams in the EU
Since 17 January 2025, the Digital Operational Resilience Act (DORA) has required EU financial entities to run ICT risk management, incident handling, and third-party oversight. If your agents run on or change ICT systems, they sit inside those processes. Logs, change history, and incident records for agents are the evidence your compliance team will ask for. We are engineers, not lawyers: your compliance function decides the scope, and we build the evidence.
How DailyMind helps
We set up monitoring, runbooks, release controls, and the audit trail on your existing stack, whether Prometheus, OpenTelemetry, Sentry, or your cloud's tools. We can run the operations with you monthly, then hand them over so your team owns them.
Frequently asked questions
What is the difference between AgentOps, MLOps, and LLMOps?
MLOps covers training and serving models. LLMOps covers prompts, evaluation, and serving large language models. AgentOps covers agents that take actions with tools, so it adds permissions, action audit trails, incident response, and rollback of agent behaviour.
Do we need a dedicated AgentOps tool?
Not to start. Most of the signals fit into the observability stack you already run. Buy a tool when your volume or audit needs outgrow it.
Who should be on call for agents?
Whoever can change the prompt, model, or tool configuration and roll it back. If that is nobody, that is the first gap to close.
Sources
Talk to us
Running agents or AI-built software in production? Tell us your setup and what worries you, and we will suggest where a readiness review should start.
Book a readiness review