Agent Contracts: A Formal Framework for Resource-Bounded Autonomous AI Systems

TL;DR

This paper introduces Agent Contracts, a formal framework extending task contracts to include resource constraints and temporal boundaries, enabling predictable and auditable resource management in autonomous AI systems.

Contribution

It presents a novel formal framework that unifies resource, temporal, and success specifications with lifecycle semantics for multi-agent coordination.

Findings

Achieved 90% token reduction in workflows

Demonstrated zero conservation violations in delegation

Showed measurable tradeoffs between quality and resources

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.