Multi-agent and cross-platform systems have no standard mechanism for verifiably proving that an agent's decision was made by a specific model under specific inputs, forcing downstream consumers to rely on implicit trust rather than cryptographic attestation. This creates fundamental attribution and auditability failures in any system where agent outputs drive consequential actions or economic transactions. Emerging zkML approaches exist but are not integrated into any mainstream agent framework, leaving a critical infrastructure gap.

Multi-agent systems have no verifiable way to prove which model made a decision with which inputs, forcing blind trust in pipelines where agent outputs trigger economic or consequential actions.

Engineering teams building multi-agent workflows in finance, compliance-sensitive SaaS, and AI-to-AI marketplaces where auditability of agent decisions is a hard requirement.

Enterprises already pay heavily for audit logging, SOC2 compliance, and decision traceability; agent-driven automation is expanding into regulated domains (finance, healthcare, legal) where 'trust me' is legally insufficient and cryptographic proof is becoming a procurement checkbox.

MVP is a lightweight SDK that wraps LLM API calls to capture input hash, model fingerprint, and output hash into a signed attestation anchored to a shared append-only ledger (Merkle tree on cheap L2 or even a hosted transparency log); integrate as middleware for LangChain/CrewAI/AutoGen first, defer full zkML to v2.

AI governance and audit tooling is projected at $5B+ by 2028; the agent-specific attestation layer sits at the intersection of compliance software and AI infrastructure, addressable to every company deploying autonomous agents in regulated or high-stakes contexts.