The Bottleneck in Parallel AI Agents: Human Approval Queue Bottleself

Running multiple Claude Code agents in parallel sounds like a throughput multiplier — 5 agents should mean 5× output. In practice, after two hours, the human becomes the bottleneck. A Reddit post details the pattern: one agent stops on a yes/no, you alt-tab to approve, two more pause, you lose context, and suddenly you're managing a decision queue instead of writing code.

The author calls this the bottleself: the ceiling where adding agents stops increasing output and starts generating approvals faster than one person can process. The limiting factor isn't tokens, model speed, or context window — it's the human-in-the-loop latency.

Proposed Solution: A Planner Layer

The author built a higher-level planner (available as npx gekto) that:

• Takes a high-level goal
• Decomposes it into parallel subtasks
• Spawns one Claude Code sub-agent per subtask
• Runs a QA sub-agent to review output
• Only pings the human when the system truly can't decide

Currently supports Claude Code only. Integrations for Codex, Cursor, and Aider are next. For a fresh repo with Claude Code, the planner handles decomposition and parallel execution end-to-end without keyboard intervention.

The honest question to anyone running 5+ agents: how much of your day is actually writing code vs clearing the queue your agents created? Where does the bottleself hit for you?

Source: github.com/gekto-dev/gekto