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Working with your agent

The heart of biopb is the conversation with your AI agent. This page covers what that feels like and how to get good results.

The workflow

  1. Open your agent. No server to start — the agent launches biopb the first time it needs it, which opens a napari window.
  2. Ask in plain language. Describe the data and what you want done with it.
  3. Watch it happen. The agent writes and runs code; image results appear as layers in napari, and numbers and tables come back in the chat.
  4. Iterate. Adjust a layer by hand, ask a follow-up, or refine the analysis. The agent sees the current state of the viewer and continues from there.

The agent works by a perceive → act → verify loop: it runs code that changes the viewer, then looks at a screenshot to confirm the result actually looks right — the same way you would.

First prompt

"Connect to biopb and report status"
Why? If you are just setting up, this checks:

  • whether your agent hears you and can respond
  • whether the agent can connect to biopb
  • whether all biopb's sub-system is running and healthy

What to ask for real

Some examples to get a feel for it:

  • "Open embryo.nd2, show channel 2, and max-project over Z."
  • "Segment the nuclei in the current image with Cellpose and count them."
  • "Measure the area and mean intensity of each segmented cell and give me a table."
  • "Threshold the membrane channel, clean up small objects, and overlay the result."
  • "Connect to the tensor server at grpc://lab-data:8815 and list what's available."

Division of labor

biopb deliberately keeps you in control:

  • Image results (segmentations, overlays, projections) go to the napari viewer.
  • Quantitative results (counts, measurements, tables) go to the agent's chat.
  • You decide what to keep — save layers and results through napari yourself.

Trained-model algorithms (like Cellpose) run on dedicated algorithm servers because agents can't do accurate segmentation unaided. Everything else — the classical image processing and analysis — the agent does directly in Python, which is what keeps the system open-ended.

Tips

  • Name your files and channels. "the DAPI channel" works better than "the blue one."
  • Work in steps. Ask for one transformation, check it, then build on it.
  • Let it see. If a result looks off, ask the agent to take a screenshot and re-check — it often catches and fixes the problem itself.
  • Point it at the right data. If your images live on a remote server, tell the agent the tensor server URL or set BIOPB_TENSOR_URL before launching.