Production data analysis agents need more than a chat loop and a function call. They work the dataset in cycles: load, transform, fit, compare, refine, render. Stateless runtimes force agents to rebuild the same environment each time, making analysis slower, more brittle, and harder to reproduce. Runloop gives each agent a dedicated Devbox that holds state across the loop, so the agent keeps its working context and produces faster, more reproducible analysis.
Each session gets a dedicated Devbox with a persistent filesystem, environment, and installed packages. The agent generates Python or R, executes it in the Devbox, reads structured outputs back through the API, and refines within the same session.
Run the same analysis across 50 datasets, prompts, models, or pipeline versions at once. Hold the environment, data snapshot, and scoring contract constant, then vary the model, prompt, or pipeline.
When the agent pulls from Postgres, Snowflake, BigQuery, or a SaaS API, the Credential Gateway injects an opaque token scoped to that Devbox.
Build data analysis agents without assembling your own sandboxing, code execution, package management, artifact persistence, and structured-output layer. Each agent gets a dedicated Devbox that runs Python or R, preserves state across turns, and returns outputs through the API — standardize how agents execute across datasets, customers, models, and pipeline versions.
Run agents that work through the full analysis loop without restarting from scratch. Loaded dataframes, fitted models, plots, and intermediate files stay available while the agent fits, inspects residuals, and refines — then returns score distributions and durations across parallel runs. Compare approaches on the same data with the analysis as the only variable.
Runloop is isolated by architecture. It runs every session in its own MicroVM and injects credentials through the Credential Gateway as scoped, revocable tokens, so raw credentials never touch the runtime.
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© 2025 Runloop AI, Inc.
© 2026 Runloop AI, Inc.
