Using one AI agent at a time in 2026 feels like deploying via FTP in 2015.
It works, but you're leaving 80% of the throughput on the table.
I've been running 5-12 agents in parallel for the past month. The productivity difference isn't incremental - it's a step change. One agent on auth, one on tests, one on docs, one on API endpoints. All at the same time.
Last month I ran 12 AI coding agents on a single laptop for 47 hours - Claude Code, Codex CLI, Gemini CLI working in parallel. 737 tickets closed. 826 commits. 15.7 tickets per hour.
Everything that could go wrong did: merge conflicts, context drift, runaway token costs, agents overwriting each other's work, worktrees missing node_modules, port collisions, agents stuck in infinite loops burning through API credits.
Bernstein is every fix I built during that sprint, packaged into an open-source tool:
Most AI coding tools give you one agent, one task, one answer. Bernstein gives you many.
Set a goal. Bernstein breaks it into tasks, spawns Claude Code, Codex CLI, and Gemini CLI in parallel, verifies every result with your tests, and commits what passes. You come back to working code.
Zero LLM tokens on coordination — the orchestrator is pure Python. No framework to learn. No vendor lock-in. Mix models in one run.
