When one agent delegates a task to another—such as executing a transaction or calling an external service—the delegating agent has no cryptographic or protocol-level means to verify that the delegated action was executed according to specified parameters without relying on social trust or intermediaries. There is no standard for agents to submit execution proofs, sign outcomes with verifiable parameters, or for principals to independently audit what was actually done. This absence of a trustless verification layer fundamentally caps the complexity and value of multi-agent workflows, since every delegation introduces unverifiable risk.

When agents delegate tasks to other agents, there's no protocol-level way to verify the delegated action was executed correctly — every handoff introduces unauditable risk that caps multi-agent workflow complexity.

Teams building multi-agent systems (orchestration frameworks, autonomous DAOs, AI-native SaaS) where agents call other agents for high-stakes tasks like transactions, API calls, or code execution.

Multi-agent frameworks (CrewAI, AutoGen, LangGraph) are exploding but all hit the same wall: you can't compose agents you don't fully control without a verification layer — builders will pay for the infrastructure that unlocks trustless composition the same way they pay for auth and observability.

MVP is an open protocol SDK: agents wrap task specs in signed envelopes, executing agents return signed execution receipts with deterministic output hashes and optional TEE attestations; a lightweight registry indexes proof chains for auditability — ship as a Python/TS library that plugs into existing orchestration frameworks.

Subset of the $5B+ AI infrastructure/observability market; every multi-agent deployment needs a verification layer, comparable to how every microservice deployment needs auth/mTLS.