MLOps manages the machine-learning lifecycle; LLMOps extends those practices to the behavior and operation of language-model applications; AgentOps focuses on systems that take multi-step actions or call tools. These are overlapping operating scopes, not three mutually exclusive stacks.
How do MLOps, LLMOps, and AgentOps differ?
The useful distinction is what you need to operate and evaluate in production. MLOps centers on models and data across development, validation, deployment, monitoring, and improvement. LLMOps includes that foundation but also treats the language-model application—its model choice, prompts, retrieval, inference path, and user feedback—as an operational system. AgentOps adds visibility and controls for the execution of workflows that make decisions, call tools, or take other actions.
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| Operating scope | What you operate | What to evaluate | Production signals to watch |
|---|---|---|---|
| MLOps | Models, datasets, and their development and deployment lifecycle | Validation results, model performance, and the effects of data or model changes | Model health, deployment reliability, and changes to data or models |
| LLMOps | A language-model application, including model selection, prompts, retrieval, and inference | Application-specific quality, including answer quality and retrieval relevance | Latency, resource use, inappropriate responses, and privacy issues |
| AgentOps | An action-taking LLM workflow, including its steps and tool calls | Multi-step trajectories, tool-call correctness, and action outcomes | Execution traces, quality changes, security concerns, and cost per interaction |
These boundaries are practical distinctions, not a universal taxonomy. Google Cloud describes generative-AI operations as adapting DevOps and MLOps practices; Microsoft Learn, Databricks, AWS, and MLflow describe additional application- and agent-specific operational concerns.
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Which MLOps practices still apply to language models?
Production generative AI does not make established ML platform work obsolete. Controlled development and deployment, validation, monitoring, and feedback into improvement remain relevant. Google Cloud’s architecture guidance frames generative-AI operations as adapting DevOps and MLOps to applications built on existing foundation models.
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The change is in what those lifecycle controls need to cover. A model release is only one potential change in an LLM application: prompts, retrieval configuration, or other parts of the inference path can also affect results. Treating the application as part of the operating scope helps teams evaluate changes beyond the underlying model.
What does LLMOps add for application quality?
LLMOps makes the application’s behavior an explicit object of experimentation and evaluation. Microsoft Learn’s guidance, last updated April 15, 2025, describes work across prompt engineering, information-retrieval optimization, relevance improvements, model selection, and fine-tuning. It also emphasizes defining metrics suited to the solution and comparing results at meaningful points in its lifecycle.
Evaluate the parts that shape the answer
For an application that retrieves information before generating a response, a model-only check cannot establish whether retrieval returned relevant material or whether the final answer met the application’s quality needs. Evaluate the relevant stages and the complete solution using measures tailored to the task; do not assume one generic score represents every kind of quality.
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Operate the inference path, not just the experiment
LLMOps also covers validation and deployment, inference, monitoring, feedback, and data collection. Microsoft Learn calls out resource use, privacy breaches, and inappropriate responses alongside application performance and health. Databricks highlights production architecture changes, API governance, lifecycle management, and human feedback in evaluation and monitoring. These are examples of concerns to assess, not a prescribed architecture every LLM application must adopt.
What does AgentOps add when a system can take actions?
A tool-using agent’s behavior unfolds over a sequence: it makes decisions, invokes tools, receives results, and may continue before returning an answer or completing an action. Looking only at the final response can obscure where a workflow went wrong. AgentOps brings runtime visibility and evaluation to that sequence.
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Trace execution and check tool use
AWS describes AgentOps practices spanning governance and security, build and operations, evaluation, and observability. MLflow’s agent guidance gives examples of agent-specific capabilities such as execution-graph visualization, multi-turn evaluation, tool-call correctness, and workflow optimization. In practice, assess both the outcome and the steps that produced it: whether the intended tool was called, whether its use was correct, and whether the sequence achieved the intended result.
Monitor runtime quality, security, and cost
Agent operations also need a view of changing quality, security concerns, and cost per interaction. Since a workflow may take multiple steps, measuring its result and its execution can expose problems that a single final-answer check misses. Use an AgentOps framing when the application actually coordinates steps or takes actions; a single-turn text-generation endpoint may call for LLMOps without a separate agent-operations layer.
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Choose practices based on the system’s behavior rather than treating the labels as competing platform choices:
- A predictive model with a conventional serving lifecycle: center operations on MLOps—data and model changes, validation, deployment, monitoring, and improvement.
- An LLM application that generates answers, possibly using retrieval: retain MLOps foundations and add LLMOps attention to prompts, retrieval, tailored quality evaluation, inference, privacy, and feedback.
- An LLM workflow that calls tools or takes multi-step actions: add AgentOps visibility and controls for execution traces, tool use, action outcomes, runtime security, quality, and cost.
The scope can grow as a system gains capabilities. A team can apply MLOps practices to the model lifecycle, LLMOps practices to the surrounding application, and AgentOps practices to its action-taking workflows without replacing one with another.
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