Managing Multiple AI Agent Tasks with Kanban Boards

AI Agent Workflow Challenges in Terminal Environments

Running Claude code in terminal environments becomes problematic when managing multiple concurrent tasks. According to a developer's three-week experience, common issues include:

• Multiple terminal tabs open with no clear indication of which agent is doing what
• Waiting on rate limits that disrupt workflow continuity
• Complete context loss when switching between terminal sessions

Three Specific Pain Points Identified

The developer tracked what actually slows down AI agent work:

• Progress visibility: No indication of whether an agent is stuck, almost done, or has failed silently until it exits
• Context loss: Returning to a task after 20 minutes means forgetting what was asked, what's been done, and what remains
• Rate limit interruptions: Hitting rate limits mid-task forces terminal babysitting until limits reset

Kanban Board Solution

The developer's fix involves treating AI tasks like standard work items on a Kanban board. Instead of the traditional run task → wait → check terminal pattern, tasks follow a structured workflow:

• Queued: Tasks waiting to be processed
• Running: Active AI agent work
• Review: Completed work ready for human verification
• Done: Finished and verified tasks

Each task becomes a Kanban card, providing at-a-glance visibility into what the AI is working on. This approach preserves context when returning to work later and eliminates the need to monitor terminal tabs directly.

The developer invites discussion about alternative methods for managing AI agent tasks, seeking community input on what approaches have worked for others.