LeviathanMatrix is an agent accountability and execution-control system built for AI agents that can touch capital, wallets, tools, and production workflows. Most agent systems focus on making agents more capable, but enterprises also need to know whether an agent was authorized to act, what policy allowed the action, what limits applied, who executed it, what actually happened, and whether the result can be audited after the fact. Our open-core AEP layer turns an agent request into a governed execution lifecycle: structured intent, policy evaluation, execution authorization, capital capsule, bounded execution, receipt, proof bundle, and post-execution review. In the hosted LeviathanMatrix pilot, this becomes a full accountability surface for live agent workflows, including security inspection, verified execution evidence, clearing readiness, and arbitration-oriented review. For the demo, we show a third-party agent using LeviathanMatrix before executing a Solana Devnet transaction. A valid request receives an execution ticket and produces a verified case with transaction evidence, receipt binding, agent attribution, executor attestation, chain fact verification, and proof status. An over-limit or unsafe request is rejected before execution. This project is designed for the Agent Security & AI Governance track because it gives AI agents stronger guardrails than prompt rules alone. It creates a practical bridge between autonomous execution and enterprise-grade auditability, compliance, accountability, and dispute review.
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