Foxglove goes agentic

Foxglove Technologies introduced agentic workflows in its robotics data platform, enabling users to interact with robotics data using natural language through a built-in agent sidebar and an MCP server that lets external AI agents like Claude, ChatGPT, and Cursor control the desktop app. The agents are powered by LLM infrastructure, tools, and evals, and can perform tasks such as searching recordings, building layouts, and writing user scripts.

Foxglove goes agentic Put an agent to work on your robotics data in the app, or through MCP. Agents have revolutionized software development. Writing, testing, and debugging code, for instance, is largely a solved problem, thanks to agents. But if you are in the business of building robots, you know the bottleneck has always been elsewhere. The challenge lies in capturing, organizing, and reasoning about massive amounts of multi-modal data produced by robots. There are tools for each of these jobs, but every tool poses its own learning curve. Unfortunately, this critical part of robotics development has not benefited from agentic workflows the way the rest of software development has. That changes now. Today, as part of our data platform announcements for Physical AI /blog/introducing-the-agentic-data-platform-for-physical-ai , we are introducing agentic workflows in Foxglove, with the ability to work with your robotics data in natural language. You can ask questions about your data, build and refine layouts, write user scripts, and search your recordings, simply by describing your intent. The agent figures out which tools to use to perform the requested operation. What are the agentic workflows in Foxglove? Foxglove’s agentic capabilities are available in two forms today: - Agent sidebar: Foxglove’s built-in agent, inside the app. - MCP server: Lets your existing AI agents control Foxglove through the desktop app. Agent sidebar The built-in agent in the Foxglove app is your navigator when you are already working through a recording and want to either ask questions about the underlying data or configure your visualization panel to better understand it. The agent derives the intent of the request based on the context. For example, if the agent sees a clear search intent in your query, it will take you to the search page and actually perform the search instead of just answering your question in the chat window. Here is an example of a Semantic Search /blog/introducing-semantic-search that originated in the agent’s chat interface. MCP server The MCP server runs as part of the Foxglove desktop app and provides a gateway for your AI agents Claude, ChatGPT, Cursor, etc. to interact with Foxglove. This can come in handy when you are building a workflow involving tools beyond Foxglove. For example, an agent could look at a test failure in code, correlate it with the relevant robot recording, and prepare that recording for inspection in the visualization interface. How do the agents work? Agentic workflows are powered by three architectural pieces: LLM infrastructure, tools, and evals. The LLM layer gives us a single interface to models that the agent runs on. Tools are organized by where they execute. Local tools run inside the Foxglove app you have open, which is how the agent can fix a broken layout for you. Cloud tools reach your organization’s data to search and act on it. These tools also act as the building blocks for skills, which are playbooks for common robotics data jobs writing FoxQL https://docs.foxglove.dev/docs/visualization/foxql , diagnosing a broken MCAP https://mcap.dev/ file, etc. . The agent automatically determines which skill to use based on context. Our local MCP server uses the same infrastructure, so you can point your own agent at Foxglove to perform these tasks. The eval pipeline tells us how a prompt or tool change affects the agent’s ability to solve problems accurately. Our first step has been to give agents the same platform functionality humans already have. This alone opens up many possibilities for iterating faster and triaging issues better. However, we are not stopping here. We believe there is enormous scope for accelerating robotics development through agents. The next step will be to automate complex multi-step operations where human judgment, visual inspection, etc. are still bottlenecks. More on this soon. Try agents in Foxglove Agentic workflows are available in beta to all customers. In the Foxglove app, click the agent sidebar sparkle button in the top-right corner, or press Cmd+J macOS or Ctrl+J Windows/Linux . Learn about agents in Foxglove https://docs.foxglove.dev/docs/agents Our engineers will demo these features live on September 2, 2026. Register now https://luma.com/foxglove-agentic-workflows .