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

2026-01-13

AI Summary

This work addresses the lack of standardized governance over resource consumption and execution duration in existing agent protocols, which undermines predictability and controllability in multi-agent systems. To this end, we propose the Agent Contracts framework—the first to introduce formal contractual mechanisms into resource-constrained autonomous AI systems. Our approach unifies task specifications, multidimensional resource constraints, temporal boundaries, and success criteria into a governance model with explicit lifecycle semantics, enabling hierarchical coordination through contract delegation. The framework enforces a resource conservation principle, ensuring that subcontracts never violate their parent’s budgetary limits. Empirical evaluation demonstrates that our method reduces token consumption by 90% and decreases variance by 525× in iterative workflows, achieves zero resource violations, and enables quantitative analysis of quality–resource trade-offs.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• resource-bounded
• autonomous AI
• multi-agent systems
• formal governance
• contract delegation

Innovation

• Agent Contracts
• resource-bounded execution
• formal governance
• contract delegation
• conservation laws