Abstract
Location-Independent Evaluator Architectures, Protected Appointment, Evidence Verification and Authority-Separated Decision Control for Multi-Agent and Cross-Organization Systems
As autonomous AI agents increasingly negotiate, plan and act together — often across organisations — systems rely on evaluators to judge them: rule engines, AI "judge" models, simulations, human reviewers, panels or hybrid services. Yet the evaluator itself is rarely governed: it may appoint itself, claim neutrality, see evidence it should not, keep authority after a project ends, issue verdicts that silently go stale, or have its approval treated as permission to move money, release data, change production systems or actuate equipment. This disclosure describes location-independent evaluator architectures in which evaluation is a bounded, separately governed function. An evaluator may be ephemeral (instantiated for one task or review round), persistent (a long-lived service holding separate per-project mandates) or hybrid (a persistent host creating short-lived evaluation contexts), and may run on the same device as an agent, on a separate host, in the cloud or edge, in a trusted execution environment or as a human-assisted interface. Protected appointment establishes eligibility, permitted functions, evidence-access scope, independence and conflict rules, decision authority, validity and revocation; an evaluator cannot become one by self-assertion or by selection from the agent it reviews. Separate lifecycle controls distinguish evaluator infrastructure, runtime instances, appointments, review rounds, decision validity and evidence retention, so that destroyed runtimes, lost connectivity or long-lived hosts never extend or revive authority. Evaluator outputs — approve-for-consideration, deny, hold, request-evidence, dispute or unknown — remain non-effective: any payment, data release, privileged command or physical action still requires its own authorisation and verification at the point of effect. Twenty-seven embodiments cover protected multi-agent negotiation and communication freeze, sandboxed consequence reconstruction, risk and tail-exposure evaluation, next-actor selection, single-use next-move passports, finality-sink enforcement, neutral substitute agents, pending-work escrow, phased completion receipts, verified outcome feedback, collusion-resistant review, network partitions and unknown outcomes, session closure and authority retirement, cross-domain appointment and dispute containment, trust and policy compatibility, leaderless coordination, dynamic multi-organisation threshold governance, minimal-disclosure outcome evidence and multi-domain execution finality, with mathematical models, pseudocode, flowcharts and runnable reference implementations. This disclosure corresponds to Indian Patent Application No. 202631131650 and builds on the author's international patent application PCT/IB2026/055615 (the "Mothership" application), filed on 4 June 2026 and publicly disclosed on 16 July 2026. The author is open to acquihire or strategic collaboration.
KEYWORDS AI agent governance, multi-agent systems, independent evaluator, LLM-as-judge, AI oversight, human-in-the-loop, separation of duties, least privilege, appointment and mandate, delegation, conflict of interest, evidence provenance, collusion resistance, quorum review, policy enforcement point, cross-organization agents, agent-to-agent coordination, negotiation, distributed consensus, network partition, fencing tokens, revocation, privacy-preserving evidence, selective disclosure, outcome verification, fail-closed, zero trust, execution finality
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Das, Sangam Kumar, "Approval Is Not Permission: Appointing, Bounding and Retiring the Evaluators That Judge Autonomous AI Agents", Technical Disclosure Commons, (November 05, 2026)
https://www.tdcommons.org/dpubs_series/12801