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Interview Preparation

Two-minute script

“DeepAgent Sandbox is a planned agentic data-analysis product with a React chat UI, a LangGraph/DeepAgents reasoning runtime, and a separate Fastify microsandbox executor. The agent reads uploaded data, writes and runs Python in a microVM, persists generated artifacts to MinIO, and resumes threads through Postgres checkpoints. The executor already implements important control-plane behavior: limits, per-session locks, runtime leases, workspace manifests, diff persistence, and job metadata.”

Critical interview honesty

Do not claim the Python product backend or DeepAgent graph is implemented in this checkout. Explain the executor as the strongest implemented subsystem and the implementation plan as the target architecture.

Questions

  • Why is a custom DeepAgents backend cleaner than an ad hoc execute tool?
  • How does manifest diffing avoid losing generated files?
  • Which stores are required for restart-safe resumption?
  • Why should presigning live in the product backend?
  • What would you test before allowing arbitrary user uploads?

Improvement story

The next milestone is an authenticated backend skeleton with thread/file metadata and presigned MinIO URLs, followed by the Python backend adapter, Postgres checkpointing, and an end-to-end chart-generation acceptance test.