LLMOps and AgentOps Explained

What is LLMOps?

LLMOps (large language model operations) is the set of practices, tools and workflows for taking LLM-powered applications from prototype to reliable production — covering prompt management, evaluation, observability, deployment, cost control and guardrails. It adapts MLOps to systems whose behaviour is probabilistic, prompt-driven, and changes whenever the underlying model is updated.

What makes it its own discipline is that the unit of change is rarely a retrained model. It is a prompt, a piece of retrieved context, a tool definition, or a switch from one model version to another — and any of those can shift behaviour overnight. The same input can produce different outputs on different runs, so 'correct' becomes a judgement rather than a fixed value.

LLMOps therefore spans the full lifecycle: experimentation and prompt iteration, evaluation before release, controlled rollout, and continuous monitoring of quality, cost and safety once the application is live.

What is AgentOps?

AgentOps is LLMOps for autonomous, multi-step agents. Where LLMOps largely concerns single prompt-and-response calls, AgentOps adds everything agents introduce: tool calls, multi-step trajectories, sub-agents, memory, and actions with real-world side effects — plus the identity, permissioning and runtime control those actions demand.

The shift matters because with an agent the process is part of the product. An agent can return a plausible final answer while having called the wrong tool, looped unnecessarily, or grounded a claim in a document it never retrieved. Monitoring only the final output misses all of that, and cost compounds across every step rather than landing in a single call.

AgentOps is emerging as both a discipline and a tooling category, sitting on top of LLMOps to keep agents reliable, affordable and in-scope once they are operating on their own.

LLMOps vs AgentOps vs MLOps: what is the difference?

MLOps operationalises trained models — data pipelines, training, deployment, and monitoring of accuracy and drift. LLMOps operationalises applications built on pre-trained LLMs, where the unit of change is the prompt, context and model version rather than training data. AgentOps extends LLMOps to multi-step agents that plan, call tools and take actions, so it must govern process and behaviour, not just outputs.

Put in terms of what each one watches: MLOps watches a model's predictive accuracy over time. LLMOps watches an application's output quality — correctness, groundedness, tone — as prompts and models change. AgentOps watches an agent's whole trajectory and the consequences of its actions: which tools it called, what it accessed, what it changed, and what it cost across the session.

They are layers, not rivals. A team can run MLOps for an in-house model, LLMOps for the application built on a frontier model, and AgentOps for the autonomous agent that uses both.

What does an LLMOps workflow include?

A mature LLMOps workflow spans six areas: prompt and version management, evaluation (offline regression plus online sampling), observability and tracing, deployment and rollout, cost and token monitoring, and guardrails for safety and policy. The connecting thread is evals — a green eval suite is the ship gate, the same role automated tests play in software CI/CD.

Prompt management versions prompts and context so changes are tracked and reversible. Evaluation scores each change against a dataset and a sample of live traffic. Observability captures traces of every call. Deployment rolls changes out gradually, often behind canaries. Cost monitoring attributes spend to features and teams. Guardrails enforce safety and policy at the edges.

Without the eval layer the rest is flying blind: you can deploy and observe, but you cannot say whether a change made the system better or worse.

What is different about operating agents?

Operating agents adds four concerns LLMOps alone does not fully cover: trajectory-level evaluation that judges the whole sequence of steps rather than just the answer, tool and action governance, agent identity with scoped permissions, and runtime enforcement that can block or escalate an unsafe action before it takes effect.

These exist because agents act, not just answer. An agent that can issue refunds, send emails or modify records needs controls on what it is permitted to do, attribution for every action it takes, and the ability to be stopped mid-task. That is operational territory a single-prompt LLM app never enters.

This is where AgentOps overlaps with governance: the same trajectory data that tells you whether an agent performed well also tells you whether it stayed in scope and within policy.

What is eval-driven development?

Eval-driven development is the LLMOps and AgentOps equivalent of test-driven development: you write the eval for a desired behaviour or a known failure before the fix, and a change ships only when it passes the eval suite without breaking the rest. It turns 'did the agent get better?' from a Slack argument into a measurable trend you can plot.

The loop is simple and self-reinforcing: trace what the agent actually did, score it with evals and user feedback, turn the failures into new eval cases, ship a change, and re-score against the now-larger dataset. Because the dataset grows from real production failures, it stays representative of what the agent actually faces rather than what you imagined it would.

It is the operating rhythm that separates teams who ship agent changes confidently from teams who ship and hope.

How do you monitor LLMs and agents in production?

Production monitoring instruments traces and spans for every model call, tool invocation and retrieval, then runs the same evals you use offline on a sample of live traffic. Dashboards track task success, tool-call accuracy, drift, latency and cost per request — broken down by agent, route and model — so quality regressions surface before users report them.

Continuous monitoring matters more for LLM systems than for traditional software because they can degrade without any code change: a provider ships a model update, a tool's API starts returning different data, or user behaviour drifts. An eval suite's pass rate can fall while your repository stays untouched, which is why evals belong in monitoring and not just in CI.

For the deeper mechanics of traces, spans and what to put on a dashboard, see our AI Agent Observability guide.

What tools do LLMOps and AgentOps teams use?

The stack splits into observability-with-evals platforms (Langfuse, LangSmith, Arize Phoenix), eval-first platforms (Braintrust, plus open-source DeepEval and Ragas), cost and gateway tools (Helicone, LiteLLM), and agent-specific operations layers. Most teams pair an observability tool for traces with an eval workflow on top; the common gap is agent-level quality, cost and governance in one place.

The right starting point depends on the gap. If you have no offline testing, adopt an open-source eval framework this week. If you have traces but no scoring, add evals to your observability platform. If you can see individual calls but cannot answer whether a given agent is getting better or more expensive over time, that per-agent view is the gap.

Prefactor sits at that agent layer: judge-based and golden-dataset evals, per-agent quality and cost analytics, and runtime governance over multi-step agents — sessions, sub-agents and tool calls, not just single completions — integrating with the traces you already collect rather than asking you to switch stacks.

How LLMOps and AgentOps come together at Prefactor

LLMOps gets a model-powered feature to production; AgentOps keeps an autonomous agent reliable, affordable and in-scope once it is there. Prefactor operates at the agent layer — evals, per-agent quality and cost analytics, and runtime governance — so the Track, Evaluate and Act loop runs continuously rather than as a one-off launch check.

In practice that means the same platform that scores an agent's quality with evals also shows what it accessed and cost, and can block or escalate an action that crosses a policy threshold. Evaluation and governance stop being separate tools and become one operating loop. For the evaluation methods underneath this, see the Agent Evals and LLM-as-a-Judge guides; for the runtime side, see Runtime Enforcement.