Multiplayer agentic engineering — Arjun Singh, Superconductor

Nineteen minutes of concrete architecture decisions from a team that generates 99.9% of its PRs with agent assistance and burns 1.5 billion tokens a month. The lessons are specific and transferable: stay model-agnostic because the best model changes weekly, run agents in isolated cloud environments so credentials stay out of reach, and benchmark on your own codebase because SWE-bench's Python bias tells you nothing about your Rails app.

• Benchmark agents on YOUR codebase — public benchmarks like SWE-bench are Python-heavy and their Rails codebase showed very different model rankings
• Isolated cloud environments solve both 'laptop anxiety' (closing the lid stops work) and the security problem of agents touching credentials they shouldn't
• Same agent session across Slack, desktop, mobile and GitHub prevents context siloing — every human interface becomes an agent interface
• Codex ran 4x more sessions than Claude Code at lower total cost in their stack

Part of Issue Nº 004: OpenClaw's near-burnout, agent teams that ship 99.9% of PRs, and the math behind prompt caching