Overview
How might we design task queue so people can trust and act on AI output?
When this pattern fits
- Ideal for autonomous AI agents, workflow automation tools, and multi-step task systems where users need visibility into agent progress and task status.
When to skip or lighten it
- Single-step assistants that never batch work.
- Strictly linear chats where a queue would duplicate the transcript.
- Realtime collaboratives where a kanban elsewhere already tracks the same work.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Hidden queues that keep running after the user leaves the page with no notice.
No cancel or reorder once items are enqueued.
Queues that do not show failures inline.
Mixing unrelated users’ tasks in one personal queue.
How products use it
| Product | Implementation |
|---|---|
| AutoGPT-style agents | Visible objective lists and step queues. |
| LangChain / LangSmith | Traced step lists for multi-tool runs. |
| CrewAI | Multi-agent task assignment boards. |
| AgentGPT | Goal breakdown into queued agent tasks. |
Implementation
Copy this prompt to generate a production-ready implementation in Cursor, Claude Code, Lovable, or any AI coding agent.
Generate a production-ready implementation of the "Task Queue" AI interface design pattern.
Pattern Definition:Frequently asked questions
What is an agent task queue?
It is a UI list of agent tasks with states (queued, running, blocked, done) so users can manage multi-step automation.
What controls belong on each item?
Cancel, retry, view log, and sometimes reorder or change priority. Show blockers clearly.
How does a queue differ from plan-and-execute?
Plan-and-execute presents the strategy. The queue is the operational board that tracks execution of those steps.
Should completed tasks stay forever?
Keep recent history, then archive. An endless done list hides what still needs attention.