dorsal/arxiv
View SchemaAgent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systems
| Authors | Qing Ye, Jing Tan |
|---|---|
| Categories | |
| ArXiv ID | 2601.08815vv2 |
| URL | https://arxiv.org/abs/2601.08815 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
The Contract Net Protocol (1980) introduced coordination through contracts in multi-agent systems. Modern agent protocols standardize connectivity and interoperability; yet, none provide formal, resource governance-normative mechanisms to bound how much agents may consume or how long they may operate. We introduce Agent Contracts, a formal framework that extends the contract metaphor from task allocation to resource-bounded execution. An Agent Contract unifies input/output specifications, multi-dimensional resource constraints, temporal boundaries, and success criteria into a coherent governance mechanism with explicit lifecycle semantics. For multi-agent coordination, we establish conservation laws ensuring delegated budgets respect parent constraints, enabling hierarchical coordination through contract delegation. Empirical validation across four experiments demonstrates 90% token reduction with 525x lower variance in iterative workflows, zero conservation violations in multi-agent delegation, and measurable quality-resource tradeoffs through contract modes. Agent Contracts provide formal foundations for predictable, auditable, and resource-bounded autonomous AI deployment.
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"abstract": "The Contract Net Protocol (1980) introduced coordination through contracts in multi-agent systems. Modern agent protocols standardize connectivity and interoperability; yet, none provide formal, resource governance-normative mechanisms to bound how much agents may consume or how long they may operate. We introduce Agent Contracts, a formal framework that extends the contract metaphor from task allocation to resource-bounded execution. An Agent Contract unifies input/output specifications, multi-dimensional resource constraints, temporal boundaries, and success criteria into a coherent governance mechanism with explicit lifecycle semantics. For multi-agent coordination, we establish conservation laws ensuring delegated budgets respect parent constraints, enabling hierarchical coordination through contract delegation. Empirical validation across four experiments demonstrates 90% token reduction with 525x lower variance in iterative workflows, zero conservation violations in multi-agent delegation, and measurable quality-resource tradeoffs through contract modes. Agent Contracts provide formal foundations for predictable, auditable, and resource-bounded autonomous AI deployment.",
"arxiv_id": "2601.08815",
"authors": [
"Qing Ye",
"Jing Tan"
],
"categories": [
"cs.MA"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Agent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systems",
"url": "https://arxiv.org/abs/2601.08815",
"version": "v2"
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