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How Third Moment Research runs 10,000 agents for 36+ hours
Every company has its own workflows, tools, edge cases, and definitions of success. (opens in a new window)Third Moment Research(opens in a new window) believes the best agents should be really good at that specific work and should improve their capabilities the more they’re used.
The company is building a self-improving AI system around that idea, using OpenAI’s latest models to understand agent behavior, identify recurring weaknesses, and automatically create thousands of synthetic scenarios around those problems. The team uses those scenarios to improve agents, repeating the process as new issues emerge.
Codex, GPT‑6 Astra, GPT‑6 Sol, and GPT‑6.1 Sol support the system’s research and computational work, enabling Third Moment to run 10,000 agents at once on investigations lasting 36-48 hours. The company also uses Daybreak to test the security of the environments where agents work.
“In a single session, GPT-6.1 Sol is able to maintain a coherent thread of computational research across days of compute without losing the plot.”
—Adam Khakhar, Co-founder, Third Moment Research
Reducing runtime and token waste with Codex
One of Third Moment’s early applications is in the field of quantitative finance given the clear measure of success. This domain provides a concrete setting to study how agents work and where they struggle. The team uses Codex to find and address problems in the research environment.
In one instance, it used Codex with GPT‑5.6, the latest GPT model at the time, to analyze thousands of recorded simulation trajectories (the agents’ actions and the environment’s responses) alongside timestamps and the codebase.
Codex uncovered a bottleneck: backtesting a trading strategy across a year of order-level US equity data took several hours. While waiting, agents repeatedly polled for completion, wasting tokens and filling their context with status checks.
Keeping agents on task with Codex and GPT‑6
—Adam Khakhar, Co-founder, Third Moment Research
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