Every engineering team is bringing AI agents into the way they work. But for most data and ML teams, those agents still sit outside the systems and workflows where model development actually happens.
Agents wait for context to be pasted into a prompt, then hand back suggestions that need to be manually tested in the real system. That’s useful, but it doesn’t address the bottleneck. The most time-intensive work for data scientists and ML engineers is gathering context, running investigations and turning findings into new features, experiments, and model versions.
Today, we're announcing Chalk MCP Server, which connects AI agents directly to the capabilities you use in Chalk to build, evaluate, and deploy ML systems, and Chalk Assistant, which brings an agent inside the Chalk product experience.
Together, Chalk MCP Server and Chalk Assistant provide the foundation for agentic machine learning: agents that don’t just help from the sidelines, but participate in the actual loop of developing ML systems.
Connect agents to the systems where ML work happens
The Model Context Protocol (MCP) is the standard for connecting AI applications to external systems. Chalk MCP Server exposes the capabilities engineers already use in Chalk directly to AI agents.
Through Chalk MCP Server, an agent can:
- Read feature and resolver definitions and suggest targeted changes.
- Run online queries against Chalk features and resolvers.
- Use Chalk SQL for feature research, extraction, and analysis.
- Engineer and backtest new features to improve model performance.
- Inspect query errors and behavior, including performance characteristics.
- Explore data and features, and support feature engineering and model investigation workflows.
This gives an agent access to the same primitives you use to investigate a model, identify an opportunity, build a feature, backtest, and iterate.
Let agents drive the improvement loop
With Chalk, ML teams cut their feature and model development cycles from weeks to days. Chalk MCP Server accelerates that loop even further by helping agents investigate, validate, and ship changes.
With agents connected to Chalk, teams unlock faster model iteration and proactive improvement. Instead of waiting for incidents or in review queues, agents continuously test changes. The acceleration compounds: more experiments, tighter feedback loops, and models that get better before problems surface.
Chalk Assistant: the in-product agent experience
Chalk Assistant is how that connection shows up in the product. In a simple interface, you can:
- Bring an agent into Chalk with your own model API key.
- Ask the agent in a sidebar to investigate a model, feature, or query using Chalk’s context and tools.
- Review the agent’s reasoning, queries, and proposed changes without leaving the interface.
Chalk already gives you the tools for building, evaluating, and deploying AI systems, solving common challenges like unifying training and serving, building point-in-time correct datasets, and providing observability across queries and features. Chalk Assistant puts an embedded collaborator inside your ML workflows, right where you already work.
Move from AI help to agentic machine learning
The goal is to give agents the ability to participate in the work, not just comment on it. An agent connected to the systems where your data, logic, and models live can perform the improvement loop directly. The future of ML development is agents with the context, access, and controls to run the full loop of building and operating production ML systems.
Get started
Chalk MCP Server and Chalk Assistant are available now. Install the MCP Server to connect your agent to Chalk, or reach out to your Chalk team to learn more.
