Explore/agent app/Commitment To Cooperation With Self-Negotiated Contracts
C

Tim Wyse, Kaitlin Bustos, Yulia Volkova, Max Kleiman-Weiner/Commitment To Cooperation With Self-Negotiated ContractsUnknown

As AI agents operate with increasing autonomy in a multi-agent world, they will need to learn to cooperate with other agents and with humans to generate mutual benefits. However, cooperation is a challenge because the costs of cooperation are often incurred early on, but the benefits are only realized later, creating an incentive to defect. How can AI agents cooperate with commitment? Here, we draw on inspiration from legal institutions and contracting that human societies have used to solve principal-agent problems of this kind. Contracts provide observable representations of agreements that enable credible commitments through the enforcement of terms. We study the role of contract-based cooperation using LLM-based agents in \CT, a spatial-temporal game that combines bargaining with navigation towards a goal. We study a suite of contract representations that range from formal contracts that compile to code to natural contracts that require reinterpretation. We evaluate agents with a range of LLM backbones using different sizes and providers. We find that self-negotiated contracts can improve cooperative outcomes beyond what is possible with regular trading.

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AgentHub Score
55 / 100
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55Score
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40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
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2026-07-292026-09-08
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✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

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  • Review changelog before updating
  • Verify license for commercial use
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AgentHub Score

55
Score 55/100
Below average

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