Featured case study · Agentic MLOps
DriftSense, train, deploy & monitor models by talking to an agent.
A workflow platform for ML teams where a conversational Drift Agent orchestrates the whole model lifecycle, training, deployment, inference, and drift monitoring, so practitioners spend less time wiring up cloud infrastructure and more time shipping reliable models.
An agent-first control plane for the model lifecycle.
MLOps tooling is powerful but punishing: training, deployment, inference, and monitoring each live in separate consoles with their own jargon and config. DriftSense collapses that into one place, a clean operational dashboard backed by a Drift Agent that can set up an entire workflow from a single sentence like "I want to fine-tune an LLM and deploy it." The human stays in control; the agent does the wiring.
Meet the primary user.
Frustrations
- Context-switching across training, deployment, inference, and monitoring consoles.
- Re-writing the same boilerplate config every time a model needs retraining or redeploying.
- Catching model drift too late, after metrics have already slipped in production.
Needs
- A single place to see every model's training, deploy, and monitoring status.
- One-click (or one-sentence) actions for repetitive lifecycle tasks.
- Transparent, reversible agent actions, never a black box.
"I don't want to learn five dashboards. I want to describe the outcome and approve the steps."
How the product is structured.
Two parallel paths share one mental model. Practitioners can drive surfaces directly, or hand intent to the Drift Agent, which composes the same underlying actions, with a human confirming before anything runs. The drift-monitoring loop feeds back into retraining.
Set up a whole workflow in plain language.
The agent is the headline interaction. A practitioner describes an outcome, "fine-tune an LLM," "deploy the model again," "build a RAG agent", and Drift Agent asks clarifying questions, proposes concrete steps, and only acts once the human approves. Every chat is saved, so workflows are repeatable and auditable.
Direct control for every stage.
For practitioners who'd rather drive directly, each lifecycle stage has a focused surface, consistent tables, the same status language, and an "Agent Action" column that hands any row back to the Drift Agent. One visual system, green-and-ink, end to end.
Designed and built with agents.
DriftSense isn't just about agents, it was made with them. I moved from flows to a working, deployed product using Cursor and agentic AI as a constant design partner: generating component scaffolds, pressure-testing interaction logic, and turning design decisions into shipped React in hours, not weeks.
One workflow, start to finish.
DriftSense turns a multi-console, multi-day setup into a single conversational session that a practitioner can audit and repeat. The agent removes boilerplate; the human keeps judgment and control; drift monitoring closes the loop so models stay healthy after launch.
Next: surfacing live drift charts inline, richer agent "explain this step" affordances, and team-level workflow templates so a whole org can share repeatable agentic playbooks.