Emergent tool use from multi-agent interaction

**TL;DR:** Emergent tool use from multi-agent interaction

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We’ve observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported. The self-supervised emergent complexity in this simple environment further suggests that multi-agent co-adaptation may one day produce extremely complex and intelligent behavior.

Source: OpenAI Blog

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Last updated: June 16, 2026.

Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.

Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.

Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.

Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.

Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.