Multiplayer agents demand team-wide infrastructure, not just better models

Agentic coding is shifting from individual experiments to team-wide infrastructure. Arjun Singh, co-founder of Superconductor, argues that the real bottleneck isn't model capability but how humans and agents collaborate. His team runs 99.9% of PRs through agents, but every change is human-reviewed. The key is making sessions visible across Slack, GitHub, and custom apps—so support,

growth, and engineering can all interact with the same agent context. Security and cost control are the hidden enablers. Singh insists on isolated cloud sandboxes to prevent credential leakage and data exfiltration, especially as agents become more autonomous. His team also stays model-agnostic, switching defaults as benchmarks shift—they found Anthropic 4x more expensive than Codex

for similar quality on their codebase. This agility avoids disruption and lets them chase the best cost/quality frontier. The talk is grounded in real usage data—1.5B tokens/month, benchmarked against their own Ruby on Rails repo—but it's also a vendor pitch for Superconductor. The lessons, however, are portable: build shared agent sessions, turn meeting notes

into tickets automatically, and benchmark on your own codebase rather than trusting public benchmarks like SWE-bench. The tradeoff is governance vs. autonomy. Sandboxing and human review add friction, but without them, agent-driven velocity becomes a risk. Engineering leaders should invest in infrastructure that makes agents collaborative, observable, and cost-controlled—not just chase the latest model.