Getting a model or an AI agent into production is one problem; keeping it healthy there is another. Here is an honest look at the best MLOps and Agent Ops platforms in 2026, from experiment tracking to production model and agent operations.
This category splits into two jobs. Building and training, where the big platforms and experiment trackers lead, and operating models and agents in production, where drift, quality, cost and governed action matter. Most tools focus on the first; the list makes the split clear.
Model and agent operations as one of ten pillars: live telemetry for models, agents, prompts and executions, drift and quality checks, cost and latency, and governed action when something degrades in production.
Explore the platform →The strongest all-around MLOps platform when data engineering and ML overlap, with native MLflow, Unity Catalog governance and serving on the lakehouse.
The leading experiment-tracking and AI developer platform, extending into LLMOps with Weave.
End-to-end build, train and deploy for classical ML and foundation models across the AWS ecosystem.
Google Cloud's unified ML and generative-AI platform, increasingly absorbing LLMOps features.
The open-source standard for experiment tracking, model registry and packaging.
Production ML and LLM observability with drift, quality and RAG tracing (Phoenix, open-source).
LLM and agent observability, prompt versioning and evaluation for LLM applications.
If your need is training and building, Databricks, SageMaker, Vertex AI and MLflow lead, with Weights & Biases for experiment tracking. If your need is watching LLMs and agents in production, Arize and LangSmith are strong. Ops Singularity's AI/ML & Agent Ops pillar sits on the operations side and adds governed action: when a model drifts or an agent degrades, it can respond through a reversible, audited Action Ticket, unified with the rest of your operations.
Agent Ops is the operations discipline for AI agents in production: monitoring their executions, cost, latency, quality and failures, and acting when they degrade, much as MLOps does for models.
No. It operates them. Ops Singularity's AI/ML & Agent Ops pillar keeps models and agents healthy in production and takes governed action on drift or failure, complementing the platforms that build and train them.
Bring a real problem. We will show you Sentinel investigate, act and verify end to end, with every action reversible and audited.