SPEAR: An Engineering Case Study of Multi-Agent Coordination for Smart Contract Auditing

SPEAR is a multi-agent AI framework designed to automate smart contract auditing through coordinated specialist agents that prioritize contracts, allocate tasks, and recover from failures autonomously. The research demonstrates how established multi-agent system patterns can improve security analysis workflows beyond centralized or pipeline-based approaches.

SPEAR addresses a critical pain point in blockchain security: smart contract auditing remains a bottleneck for safe protocol deployment. By architecting auditing as a multi-agent mission rather than a sequential or centralized process, researchers demonstrate how specialized AI agents can improve both efficiency and resilience. The framework's three-agent design—Planning, Execution, and Repair—mirrors real-world audit workflows where human auditors prioritize high-risk contracts, delegate specific analyses, and handle unexpected issues during review.

The technical contribution lies in applying mature multi-agent system patterns to a domain where security analysis traditionally relied on manual expert review. Using AGM-compliant belief revision and formal coordination protocols (Contract Net, auction mechanisms), SPEAR agents maintain consistency while adapting to new information—critical for accurate vulnerability detection. The Repair Agent's autonomy in recovering from brittle generated artifacts addresses a practical problem: automated analysis tools often fail on edge cases, requiring expensive manual intervention.

For the blockchain ecosystem, robust automated auditing directly impacts deployment speed and security confidence. Current bottlenecks delay smart contract launches and increase audit costs, particularly for smaller projects. A system that coordinates multiple specialized analysis agents could scale security reviews without proportional cost increases, democratizing access to professional-grade auditing.

The empirical comparison under controlled failure scenarios provides practical evidence for the multi-agent approach's advantages in coordination overhead, recovery time, and resource utilization. Future work should validate SPEAR against real-world smart contracts and integrate findings into production audit tools.

• →SPEAR demonstrates multi-agent coordination patterns improve smart contract auditing efficiency compared to centralized or pipeline-based alternatives
• →Autonomous repair agents autonomously recover from failed analysis artifacts, reducing manual intervention requirements
• →AGM-compliant belief revision enables agents to maintain consistency while adapting to new vulnerability information
• →The framework addresses real-world audit bottlenecks that currently delay blockchain deployments and increase security costs
• →Empirical testing under failure scenarios shows improved coordination, recovery behavior, and resource utilization metrics