Abstract
Abstract (Part 4 of 27)
The Reviewer Decides What Comes Next — But Cannot Make It Happen
In many multi-agent and agentic AI systems, the planner, the reviewer, the credential holder and the executor are effectively the same runtime or trust the same process. A compromised or prompt-injected agent can therefore influence its own “approval,” or an automatically trusted evaluation result can be treated as permission to act. When organizations have different risk limits and legal rights, no single agent should be able to authorize another entity’s resources. Signed approvals do not prove the truth of sensor data, human ratings can be biased or forged, and AI simulations are estimates rather than proof that real effects are allowed. Even after a plan is frozen, the evaluator may still see stale policy, omitted prior losses, or an outdated resource snapshot. Agent-generated natural language can also try to redefine the evaluator’s own rules.
This embodiment (Part 4 of a 27-part series; full details at https://zenodo.org/records/23940766) develops the independent consequence evaluator that operates after the joint-plan commitment, verified freeze, and snapshot-integrity layers of Parts 1–3. The evaluator receives the exact accepted plan, freeze evidence, causal snapshot and classified observations through a protected intake, without being given the resource-effectuation credentials of the participating agents. Untrusted agent text is isolated so it cannot grant tools, change policy roots or modify committed state. A deterministic hard-constraint engine first applies categorical prohibitions, per-domain vetoes, identity restrictions and uncertainty limits; only then may any optimization or risk scoring occur. Evidence is classified by provenance (verified receipts, sensor observations, human reviews, simulations, unresolved items) so that a favorable model score or five-star rating cannot override a hard prohibition or failed interlock. The evaluator produces a structured, version-bound decision that selects a precisely scoped next actor or role (or holds, requests more evidence, revises, renegotiates, substitutes, or denies). That decision is non-effective: it does not unlock agent gateways or confer resource authority. A separate issuer and each domain’s local Finality Sink must independently authorize any subsequent communication or effect against the actual pending operation and current conditions. During evaluation, governed agent-to-agent channels and protected effects remain on hold.
The architecture therefore keeps consequence assessment, risk limits and next-actor selection independent of effectuation authority—giving multi-agent systems a fail-closed evaluation step that cannot be turned into permission by the agents or by the evaluator itself.
Keywords: multi-agent systems, agentic AI, independent evaluator, consequence evaluation, risk-constrained decision, non-effective authorization, fail-closed systems, zero-trust agents, protected evidence admission, next-actor selection, AI safety, AI governance, prompt-injection resistance, hard-constraint policy, evaluation handoff, execution finality, cross-organizational collaboration.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Das, Sangam Kumar, "The AI Agent Reviewer Decides What Comes Next — But Cannot Make It Happen", Technical Disclosure Commons, (November 05, 2026)
https://www.tdcommons.org/dpubs_series/12747