Agent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systems
This provides a formal mechanism for predictable and auditable resource-bounded deployment of autonomous AI systems, addressing a critical gap in multi-agent coordination.
The paper tackles the lack of formal resource governance in autonomous AI systems by introducing Agent Contracts, a framework that unifies specifications, constraints, and success criteria, resulting in 90% token reduction with 525x lower variance in iterative workflows and zero conservation violations in multi-agent delegation.
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.