The MLOps practices built for traditional machine learning don't map cleanly onto agentic applications. Model training pipelines, feature stores, and batch inference infrastructure are largely irrelevant when your system calls GPT-4o via API. But agentic applications introduce their own set of operational challenges that require purpose-built tooling. This guide covers the modern MLOps stack for teams building and operating AI agents.
What's Different About Agentic MLOps
Traditional MLOps is centered on the model: train, evaluate, deploy, monitor drift, retrain. Agentic MLOps is centered on the system behavior: the agent's actions, reasoning quality, tool use patterns, and end-to-end task completion rate. You're less concerned with model accuracy in isolation and more concerned with whether the agent completes real tasks reliably.
The four domains of agentic MLOps are: evaluation, observability, deployment, and iteration. Each requires different tooling.
Evaluation Infrastructure
Before shipping any agent update, you need automated evals that catch regressions. The evaluation stack:
- Braintrust — purpose-built for LLM evaluation. Supports custom scorers, dataset management, and CI integration. Run evals on every commit with a GitHub Action.
- RAGAS — for RAG-heavy agents, provides automated measurement of faithfulness, answer relevancy, and context recall without human annotation.
- LangSmith Datasets — capture production examples as eval cases. When an agent fails on a novel input, add it to the dataset so future versions are tested against it.
# Example: automated eval in CI
import braintrust
experiment = braintrust.init(
project="my-agent",
experiment="v2.1.0"
)
for case in eval_dataset:
result = agent.run(case["input"])
experiment.log(
input=case["input"],
output=result,
expected=case["expected"],
scores={"task_completion": score_task_completion(result, case)}
)Observability Stack
Production agent observability needs three layers:
Execution tracing: Every LLM call, tool invocation, and state transition needs to be traced with timing, token counts, and inputs/outputs. LangSmith is the leading option for LangChain-based systems. Weave by W&B and Arize Phoenix are strong alternatives.
Business metrics: Task completion rate, time to completion, user correction rate, cost per successful task. These go into a standard analytics tool — PostHog for self-served product analytics, Datadog for infrastructure-integrated metrics dashboards.
Semantic monitoring: Detecting when your agent's behavior has drifted — answering different questions than it used to, using different tools, producing different output formats. Arize specializes in this for LLM systems.
Deployment Pipeline
Agent "deployment" typically means shipping a new system prompt, new tool definitions, or a new model version. Your deployment pipeline should:
- Run the full eval suite on every change to prompts or tool definitions
- Support canary releases (route X% of traffic to the new version)
- Maintain version history of all system prompts with rollback capability
- Gate production deployment on eval score thresholds
PromptLayer and LangSmith both support prompt versioning. For infrastructure deployment, standard tools apply: GitHub Actions for CI, Docker for containerization, Railway/Kubernetes for hosting.
Experimentation and Iteration
Improving agents is an empirical process. Set up A/B testing infrastructure to test prompt variations against each other on real traffic. Use feature flags (LaunchDarkly, Unleash) to control which agent version a user gets. Measure business outcomes, not just LLM metrics.
The iteration loop for agents looks like: production failure or metric regression → trace analysis to identify root cause → hypothesis (prompt change, tool update, model swap) → eval validation → canary deployment → full rollout. Make this loop as fast as possible — the teams that win are the ones iterating fastest.
The Minimal Viable Agentic MLOps Stack
If you're just starting out: LangSmith for tracing + Braintrust for evals + GitHub Actions for CI + Railway for deployment. Add more tooling as you scale and as specific pain points emerge. Don't over-engineer the stack before you have production traffic.
MLOps engineers specializing in agentic systems are commanding significant salaries in 2026. Browse MLOps and AI platform engineering roles on AgenticCareers.co to see current compensation and requirements.