What is MLOps?
Operations (LLMOps & AgentOps)The classical discipline for running machine-learning pipelines: deterministic data flows, retraining cycles, model registries. It assumes the system changes when you retrain it; agents drift without any model change, which is where the MLOps toolkit stops.
Why It Matters
MLOps is the discipline the industry already has, and it is genuinely good at what it was built for: deterministic data pipelines, training runs, model registries, and redeployments on a retraining cadence. It matters here mostly as the contrast that explains why agents need a different operating model. An agent calls a model someone else retrains, invokes tools whose contracts change, and accumulates context that reshapes its behavior, so the system drifts without a single event MLOps would recognize as a change.
Where the Toolkit Stops
The retraining assumption. MLOps versions models because the team retrains them. An agent’s model is a provider endpoint that updates underneath you, so the versioned artifact is not weights but a manifest of pins, prompts, and tools.
The determinism assumption. Pipeline monitoring asks whether the job ran and what it emitted. Agents produce fluent output while their decisions drift, so “the run succeeded” and “the behavior is still correct” are separate questions.
The registry assumption. A model registry tracks artifacts you build. An agent’s behavior lives in a combination of model pin, prompt, tools, memory schema, and configuration, none of which a weights registry captures.
Where It Breaks
The failure is inheritance without adaptation: a team runs its agent on the MLOps checklist (model versioned, pipeline monitored, deployment gated) and considers operations covered, while the agent’s prompt is edited in a console, its tools drift, and its credentials accumulate scope. Nothing in the classical cadence looks at any of that, so the gaps stay invisible until they surface as incidents.
How Flytebit Handles It
We keep what MLOps got right (versioning discipline, gated releases, monitored production) and extend it for agents: the versioned artifact is the five-component manifest, the gate is a behavioral eval, and the cadence watches drift channels the classical checklist never had. The full operating model is in Operating Agentic AI Systems, and it is delivered through our LLMOps work.