Agent Status & Monitoring
What is Agent Status & Monitoring?
When an agent is working on a long-running or multi-step task, users need to know what's happening without being forced to watch constantly. Traditional loading indicators like spinners and progress bars don't work for agentic tasks that may take minutes or hours, involve multiple parallel activities, or require occasional user attention. The design challenge is keeping users informed without demanding their attention. This pattern provides four status layers: ambient status (persistent unobtrusive badge), progress status (glanceable panel available on demand), attention status (interrupting notification when input is needed), and summary status (completion report). The system supports multiple concurrent tasks, provides estimated completion times, and auto-dismisses completed items while keeping them accessible in the audit trail.
Problem
Traditional loading indicators don't work for agentic tasks that take minutes or hours, involve parallel activities, or need occasional user input. Users need to stay informed without being forced to constantly monitor agent activity.
Solution
Design a layered status system: ambient badges for background awareness, expandable progress panels for detail, attention notifications only when input is needed, and completion summaries when tasks finish. Support multiple concurrent tasks with estimated times.
Real-World Examples
Implementation
AI Design Prompt
Guidelines & Considerations
Implementation Guidelines
Match the status display to the task duration. Sub-10-second tasks need only a spinner. Multi-minute tasks need a progress panel. Multi-hour tasks need a dashboard.
Allow users to 'check in' on agent activity without disrupting the agent's work. Think of it as looking through a window, not opening a door.
Support multiple concurrent agents or tasks. If the user has 3 agents running, the status system should aggregate and prioritize.
Provide estimated completion times and update them as the agent progresses.
After completion, auto-dismiss the status after a brief display. But keep completed tasks accessible in the Action Audit Trail.
Use escalating attention demands: ambient badge for background work, glanceable panel for progress, interrupting notification only when the agent needs input.
Never use full-screen loading states for agentic tasks - users need to continue other work while the agent operates.
Design Considerations
Status check frequency: how often users manually check agent progress - too often suggests insufficient ambient feedback
Notification-to-action time: how quickly users respond when the agent escalates to attention status
False urgency rate: how often attention-level notifications turn out to be non-urgent
Multiple concurrent task displays must remain readable without overwhelming the interface
Estimated completion times must be reasonably accurate to maintain user trust
The transition between ambient, progress, and attention states should feel natural, not jarring
Completed task indicators should auto-dismiss to prevent status clutter over time
See this pattern in your product
Upload a screenshot and find out which of the 36 patterns your AI interface uses.
Related Patterns
Ambient Intelligence
Create unobtrusive AI that senses context and provides assistance without explicit interaction.
Adaptive & Intelligent SystemsAction Audit Trail
Provide a timestamped, structured log of every action the agent took - grouped by task, with reversibility status, selective undo, and diff views - so users can review and correct agent behavior after the fact.
Trustworthy & Reliable AIEscalation Pathways
Design structured escalation triggers and handoff mechanisms so agents can pause and ask for human guidance when they encounter ambiguity, conflicts, or decisions beyond their authorization - without breaking workflow or losing context.
Human-AI CollaborationMixed-Initiative Control
Design interaction models where control flows seamlessly between human and agent - supporting parallel work zones, interruptible agent activity, and natural handoffs without formal 'take over' actions.
Human-AI Collaboration