Running many coding agents in parallel makes attention the bottleneck. A board view of agent status, with linked tasks, targets that overhead across different agent CLIs.
Key takeaways · AI-distilled
Cline argues that once a developer runs many coding agents in parallel, the constraint becomes human attentionThe mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.Full definition →: each check-in costs 30 seconds to a minute of mental context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → reload, repeated across 15 to 20 agents.
Cline Kanban shows every live AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → task as a card on a board, so a developer sees at a glance which agents are running, finished or blocked instead of cycling through terminal windows.
Tasks can be linked as dependencies, and when a parent task completes its dependent tasks can start automatically, which turns separate agent sessions into something closer to a pipeline.
The board is agent-agnostic: Cline says it can run its own agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition → as well as Claude Code and Codex, positioning Kanban as an orchestration layer rather than another agent.
Terms in this piece · Glossary
attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
