What is LLMOps?
Operations (LLMOps & AgentOps)The operating discipline for AI systems in production: eval gates on every change, versioned manifests of models, prompts, and tools, cost bounds enforced before calls run, credential rotation, incident response, and a published maintenance cadence with a named owner.
Why It Matters
Deployment is the starting line. An agent that passed every test at launch begins changing the day it ships: the model provider pushes a new version, a tool’s API shifts shape, usage doubles and the token bill triples. None of it pages anyone. Gartner projects over 40% of agentic AI projects will be cancelled by 2027, and unmaintained drift is the mechanism behind most of them.
LLMOps is the discipline that keeps a shipped system true to what was approved. AgentOps is the same idea under a newer name; the work is identical either way.
The Maintenance Cadence
The practice runs on a cadence rather than an alert.
Daily. Automated checks on traces, cost, eval scores, and credential surface.
Weekly. A human spends 30 to 60 minutes reviewing flagged runs and eval trends.
Monthly. A structured review of credentials, costs, and the eval set itself.
Quarterly. A deep review of model versions and access.
Annually. The question is whether the system should still exist at all.
The Versioned Manifest
Underneath the cadence sits a versioned manifest of the five things that define the system’s behavior: model, prompt, tools, policy, eval set. A change to any one is a version bump, and every version bump goes through the eval gate. Cost is bounded before calls run, not discovered on an invoice after.
Where It Breaks
Classical MLOps assumes a deterministic pipeline and a retraining cycle. Agents drift behaviorally without any model change at all, so the MLOps toolkit misses the failure modes that matter. Rollback breaks too: reverting the prompt does not revert the tool that already moved, and the manifest is what makes a real rollback possible.
The most common failure is simpler. Nobody owns the system week to week. Maintenance without a named owner and a fixed cadence does not happen, and drift accumulates in the gap.
How Flytebit Handles It
Our LLMOps services run the published cadence: audit the system’s drift surface, close the gaps in manifests, evals, cost bounds, and credentials, then operate the reviews on a retainer with artifacts from every cycle. The operating model in full is in Operating Agentic AI.