The Self-Improving AI System That Built Itself | Composio<br>## For AI agents: signup without a human
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Introduction<br>I was trying to ship faster.<br>I had a codebase, a backlog of things to build, and not enough hours in the day. So I started running AI coding agents in parallel — give each one a task, let them write code, review the PRs, merge, repeat. I started with two or three. Then five. Then ten.<br>The agents were fast. The problem was me. I couldn't keep up with them. I was the one checking if CI passed, reading review comments, copy-pasting errors back. I'd gone from writing code to babysitting the things that write code. That doesn't scale.<br>So I wrote some bash scripts to automate the coordination — about 2,500 lines that managed tmux sessions, git worktrees, and tab switching. Each agent got its own isolated tmux session and worktree. The orchestrator could spawn them, peek at what they were doing, and let me jump between sessions. It worked, barely.<br>Then I pointed the agents at the bash scripts themselves. They rewrote it all in TypeScript — same Claude Code sessions, gradually migrated from the old setup to the new one. The new orchestrator auto-injects CI failures and review comments back into agent sessions. It's been improving itself since.<br>From bash scripts to self improving system<br>The result: 40,000 lines of TypeScript, 17 plugins, 3,288 tests — built in 8 days, mostly by the agents the system orchestrates. Every commit has a git trailer identifying which AI model wrote it. There's no ambiguity about what humans did vs what agents did.<br>Today we're open-sourcing it: Agent Orchestrator (github.com/ComposioHQ/agent-orchestrator).<br>The key thing to understand: the orchestrator itself is an AI agent. Not a dashboard. Not a cron job. Not a script that polls GitHub. It's an agent — it reads your codebase, understands your backlog, decides how to decompose a feature into parallelizable tasks, assigns each task to a coding agent, monitors their progress, reads their PRs, and makes decisions about what to do next. When CI fails, it injects the failure back into the agent session — the agent reads the logs and fixes it. When a review comment comes in, it routes it to the right agent session with context. No human plumbing.<br>That's what makes this different from every "run agents in parallel" setup. The thing managing the agents is itself intelligent.<br>Agent orchestrator dashboard -- 35 sessions across Working, Pending, Review and Merge lanes<br>The real bottleneck in AI-assisted coding<br>Most people get the AI coding agent problem wrong. The agents can code. That's not the bottleneck. You are.<br>You spawn five tasks, go grab coffee, come back 20 minutes later and now you're just refreshing GitHub tabs — waiting for PRs, checking CI, reading review comments. Congratulations, you've automated engineering and replaced it with project management. Bad project management.<br>The orchestrator agent replaces you in that loop. Not with a script — with an actual AI agent that has context on every active session, every open PR, every CI run. It tracks everything, watches for failures, forwards review comments back to coding agents, and only pings you when something actually needs a human decision. Once that bottleneck — your attention — goes away, things start compounding fast.<br>You open the...