As AI agents move into production, someone has to operate them. This guide explains what Agent Ops is, how it differs from MLOps, and what it takes to run agents reliably.
Agent Ops is the operations discipline for AI agents in production: monitoring their executions, cost, latency, quality and failures, and taking action when they degrade, much as MLOps does for models and AIOps does for IT systems.
AI agents are not static models. They reason, call tools, chain steps and take actions, which means they can fail in new ways: a wrong tool call, a runaway loop, a degraded prompt, an unexpected cost spike. Running agents in production requires visibility into each execution and the ability to intervene when one misbehaves.
An Agent Ops practice tracks live agent executions and their outcomes, token usage, cost and latency, pass and fail rates and failure analysis, and quality or feedback signals. It also governs access, which tools and data an agent can use, so an autonomous agent operates within safe, auditable bounds.
Because agents take actions, governance is central. Every agent action should be scoped, observable and, where it touches production, reversible and audited. Agent Ops is as much about control and trust as it is about performance.
Ops Singularity's AI/ML and Agent Ops pillar provides telemetry for models, agents, prompts and executions, quality and failure analysis, cost and latency, and governed action when an agent degrades, with an agent studio and RBAC to keep autonomy safe. Explore the AI/ML and Agent Ops pillar.
Agent Ops is the operations discipline for AI agents in production: monitoring their executions, cost, latency, quality and failures, and taking governed action when they degrade.
MLOps operates machine learning models across their lifecycle. Agent Ops focuses on autonomous AI agents that reason and take actions, which introduces new failure modes and makes governance and execution monitoring central.
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