Hand work to a worker

When a request deserves its own focused context — cited research, bulk extraction, a long draft — the assistant spawns a worker: an ephemeral agent composed for exactly that task, with exactly the capabilities the assistant grants it from its own set. There is nothing to configure; this walk runs one research job end to end and shows you how to read the job card.

The conceptual side (capability subsets, budgets, methodologies) lives in Agent workers.

Before you begin

You need a chat-capable agent (the built-in Assistant works as-is) on a model with tool-calling support. For live web sources, connect a search connector such as Tavily under Settings > Connectors — without it the worker falls back to plain web fetching and says so in its result.

Step 1 — Ask for something worth a worker

Open a chat with Assistant and ask for open-ended, citable work, for example: Research the current state of solid-state batteries — market, key players, cited sources. A quick factual question won't (and shouldn't) spawn anything; workers are for tasks that benefit from isolation.

Step 2 — Watch the job card

The assistant calls spawn_agent and a job card appears under its turn: the worker's name, a live status, and the worker's own progress checklist filling in as it plans and works through sub-questions. The card never blocks the chat — you can keep typing while the worker runs.

If the card shows a "skipped" note, the assistant requested something outside its own grants (say, an unconnected connector); the run continues with what remains, and the note tells you what to connect for next time.

Step 3 — Read the result and the transcript

When the job finishes, the assistant folds the worker's deliverable into its reply — for research, a conclusion, key points with inline citations, and sources. On the card, expand worker activity to see the full transcript: every search, every tool call, and the worker's reasoning. That transcript is the audit trail you point at when someone asks what the agent actually did.

Step 4 — When something goes wrong

A worker that runs out of time or hits an error ends with a visible status on the card — timed out or failed — with its partial progress intact. The assistant reports what it got and continues itself where it can. Nothing fails silently: if the worker needed input only you can give, the assistant asks you directly.

Where this fits

One request, one worker, one card is the smallest useful shape. The same mechanics scale to several workers in a turn — each gets its own card, its own progress, and its own transcript. For fixed stages with approvals or scheduling between them, reach for a workflow instead.