When people talk about multi-agent AI development teams, they usually mean two or three agents working together on a task. Peter Steinberger means something different.
Steinberger — founder of OpenClaw and now an engineer at OpenAI — runs approximately 100 Codex instances in continuous operation. They write code, review pull requests, find bugs, deduplicate GitHub issues, monitor benchmarks, and even attend meetings to draft PRs for features discussed in conversation. In 30 days, his team’s OpenAI API bill hit $1.3 million for 603 billion tokens across 7.6 million requests. The top model powering it all: GPT-5.5.
For most developers, $1.3 million a month is an unthinkable figure. But Steinberger’s experiment is uniquely instructive precisely because of its scale. Here’s what his setup actually does, and what practitioners at every budget level can take from it.
What the Agents Actually Do
Steinberger’s fleet isn’t running a single monolithic task. It’s a distributed system of specialized agents, each with a defined scope:
Code Writing and PR Review
The most straightforward agents take tasks from a backlog and write code. But rather than requiring a human to review every output, Steinberger uses other agents to review those PRs — checking for correctness, security issues, and consistency with the codebase. Agents reviewing agent-generated code is a genuine feedback loop, not just a novelty.
Bug Deduplication
Any active open-source project accumulates duplicate issues. Agents monitor the GitHub issue tracker and collapse near-duplicates, linking them and tagging the originals. This is a classic case where the cost of human time (reading and assessing potentially thousands of issues) is much higher than the cost of automation.
Benchmark Monitoring
Continuous performance testing is another area where agents deliver high ROI. Rather than running benchmarks on a scheduled cadence, Steinberger’s agents monitor for regressions and file Discord reports automatically. A human only gets involved when something breaks.
Security Analysis
The team uses Clawpatch.ai, Vercel’s Deepsec, and Codex Security for automated vulnerability scanning and fix generation. These agents operate on commits, not just the final codebase — catching security issues before they land in main.
Meeting-to-PR Agents
Perhaps the most striking use case: agents that listen to team meetings and automatically open PRs for features discussed in conversation. This effectively collapses the gap between a decision and a code change from hours or days to minutes.
The Cost Reality
603 billion tokens. $1.3 million. 7.6 million requests. Let’s put that in perspective.
At these token volumes, the per-token cost is roughly $2.15 per million tokens on average (blended across model tiers). That’s in line with GPT-5.5 pricing for a high-volume customer. The 7.6 million requests over 30 days averages out to about 253,000 API calls per day — or around 10,500 per hour, continuously.
Steinberger has been transparent that OpenAI covers this cost as a research arrangement. He frames the entire experiment as a study in what unconstrained agentic software development looks like. He’s not arguing that every team should spend $1.3 million a month. He’s mapping the frontier.
What Practitioners at Any Scale Can Apply
1. Specialize your agents by task, not by project
The most expensive failure mode in multi-agent systems is generalist agents trying to do everything. Steinberger’s fleet works because each agent type has a narrow job. A bug-deduplication agent doesn’t also write code. A benchmark-monitoring agent doesn’t also attend meetings. Start with one specialized agent per pain point.
2. Use agents to review agent output
The PR review loop — where one agent checks another’s work — isn’t a luxury. It’s what makes agent output safe to ship without constant human oversight. Any team running more than a handful of agents should invest in automated review coverage.
3. Track token cost per workflow, not just in aggregate
Knowing you spent X dollars on AI last month is less useful than knowing you spent Y dollars per merged PR, Z per security review, and W per deduplicated issue. Build cost attribution from day one.
4. Set escalation thresholds, not just limits
Hard token limits cause agents to fail mid-task. A better pattern: define threshold points at which an agent pauses, summarizes where it is, and hands off to a human. This keeps costs bounded while preserving progress.
5. Let agents observe, not just act
Several of Steinberger’s highest-value agents are observers — monitoring benchmarks, watching the issue tracker, listening to meetings. Observation is cheap. Acting on observations selectively keeps costs proportionate to value.
The Bigger Takeaway
The $1.3 million number will get the headlines, but the more durable insight is architectural: a small human team (three people) is operating at a scale that would have required a much larger engineering organization a year ago. The agents aren’t replacing engineers — they’re making a tiny team capable of maintaining a large, active open-source project without burning out.
That ratio is going to keep improving. The question for every engineering team isn’t whether to run agents at this kind of scale — it’s whether the organizational and cost-tracking infrastructure is in place when you’re ready to scale up.
Sources
- The Decoder — For $1.3 million a month, OpenClaw founder Peter Steinberger runs 100 AI agents that code, review PRs, and find bugs
- Augustine Wheel — Independent coverage, May 16 2026
- steipete on X — API cost screenshot and context
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260516-0800
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