In June 2026, Peter Steinberger reported that his system spent $1,305,088.81 over 30 days and processed more than 603 billion tokens across roughly 100 Codex instances, with a team of three people running it. Not a benchmark, not a weekend experiment. A real production workload, big enough to expose every weakness an agent can have.
Most of the reaction went straight to the obvious question: which model powered it? Fair place to start. Better models write cleaner code, follow instructions more reliably, and recover better when a task goes sideways. If you're building coding agents, picking the strongest model available feels like the decision that matters most.
But at $1.3 million across 100 Codex instances, there's a more interesting question.
What was the harness?
Almost nobody asked it, even though it's the question that explains how the system around the model keeps long-running work on track. It's also what supports hundreds of parallel agents at once and what. It's also what supports hundreds of parallel agents at a go and what keeps producing useful results after thousands of tool calls and decisions. None of that is a property of the model alone. It's the job of the production harness and the discipline of building one well now has a name: agent harness engineering.
