AI-Delegation-Learning-Lab

This paper documents the design, implementation, and formal verification of AI-Delegation-Learning-Lab, the third lab in the AMO Research Series on structural governance of autonomous systems. The central thesis is that delegation does not transfer authority — it issues a new, scoped, time-bounded authority object derived from the original grant. The lab implements and verifies four structural invariants: (a) delegated grants cannot exceed the scope of their parent; (b) every delegation embeds a chain recording the full issuance path; (c) delegated grants are independently revocable without revoking the parent; and (d) the full chain is machine-verifiable through ledger replay at any depth within the recorded ledger history. The implementation introduces four new modules (chain.js, delegate.js, replayChain.js, delegation-bridge.js), reuses the append-only ledger from AI-MCP-Learning-Lab without modification, and verifies all invariants through 39 unit tests and 4 end-to-end scenarios. Scope violations produce first-class DenialEvents in the ledger — not runtime exceptions. Revocation cascades are recursive, ledger-driven, and auditable. The paper includes formal invariant notation, a threat model, and an explicit discussion of scope limitations. The primary contribution is the operationalization of chain integrity as a machine-verifiable governance primitive for delegated authority.