Chat-native workflows

Describe the workflow. Watch the graph appear. Keep humans in control.

WorkSwarm treats workflow authoring as a conversation. The system turns intent into a DAG, wires missing tools in chat, and runs the workflow with checkpoints, live progress, and the ability to pause or fork when reality changes.

Workflow lifecycle

1

Describe the workflow in chat

Users express the work in natural language rather than starting in a drag-and-drop canvas.

2

Generate a DAG

SwarmGuide turns the request into a visible graph of triggers, actions, checkpoints, and waits.

3

Connect missing tools

Required MCPs are recommended inline and connected without leaving the thread.

4

Run, pause, fork, approve

The workflow executes with live state while preserving human checkpoints and editability.

Why it matters

The workflow should adapt to the operator, not force the operator into a builder first.

WorkSwarm keeps the authoring surface in chat and treats the graph as a visible execution contract. That lets operators iterate in natural language without giving up control, auditability, or runtime safety.

DAG visibility

The graph is visible in chat so the system intent remains inspectable.

Pause and fork

Operators can stop a flow or branch it when a real-world exception appears.

Checkpoint injection

High-blast-radius actions pick up explicit approval nodes instead of silently continuing.

MCP continuity

Tool recommendations and auth events happen in the same thread as the workflow itself.

Execution matters more than builder aesthetics.

The workflow story is strongest when it stays attached to real work: live progress, tool state, checkpoint approvals, and the same chat thread where the request began.