September 14, 2025
On(e)behalfof: Fitzgerald J. Heslop, Founder & CEO Agent OBO Inc.
Appointed Intelligence™ is introduced as a distinct category of AI governance that goes beyond the traditional paradigms of “Responsible AI” or “Trustworthy AI.” Unlike those frameworks – which focus on broad ethical principles and best practices – Appointed Intelligence defines a formal protocol for delegating AI authority under human oversight[1]. In essence, it treats AI systems not merely as tools to be managed, but as appointed agents acting on behalf of a principal (an individual or organization) within a defined mandate. This approach emphasizes structured accountability: every AI agent is explicitly appointed, given authority within set bounds, attributed for its actions, audited, and if necessary, revoked.
This marks a shift from diffuse responsibility to a concrete, authority-based model of governance. Traditional Responsible AI initiatives often enumerate principles like fairness, transparency, and accountability at a high level[2], but they may lack teeth when it comes to assigning liability or overseeing day-to-day AI actions. Appointed Intelligence fills this gap by establishing clear roles, permissions, and consequences for AI behavior. It is “identity-bound, memory-governed, and ethically delegated” intelligence[1] – meaning the AI is bound to an accountable identity (its principal), operates with governed memory/logs, and acts only under ethical mandates given to it. In short, this is not just artificial intelligence; it is appointed intelligence, built from the ground up for accountability and trust.
Importantly, Appointed Intelligence is not a mere rebranding of existing concepts. It is a systematic protocol – “the first true ‘on behalf of’ AI infrastructure”[3] – that formalizes how an AI agent is empowered and supervised as a Trusted Delegate™[4]. By design, such an AI doesn’t just assist; it represents the principal’s will, with discretion and autonomy, yet under continuous oversight[5]. This briefing will outline the core framework (the A^4R loop), the foundational primitives and tests that define an Appointed AI, example artifacts (like an AAAT “receipt”), and how this model aligns with legal doctrines. We will also contrast it with prevailing approaches (Big Tech RBAC, Responsible AI principles, and LLM content guardrails) to illustrate why those are inadequate in isolation – and how Appointed Intelligence provides a more robust, insurable, and governable path forward.
(Citations are used throughout to ground this framework in existing thought leadership and legal principles. All figures and tables are for illustrative purposes, aligned with Agent OBO Inc.’s modern and clean design aesthetic.)
At the heart of Appointed Intelligence is the A^4R Loop – a cyclical process capturing the life cycle of an AI agent’s authority and accountability. A^4R stands for Appointment → Authority → Attribution → Audit → (Revocation), in a continuous loop (the final step looping back to potential re-appointment or termination). This loop, depicted below, represents an ongoing governance cycle ensuring that any AI agent operates under active supervision and evaluative feedback.
Figure 1: The A^4R Governance Loop – an AI agent’s journey through Appointment, Authority grant, Attribution of actions, Auditing, and potential Revocation (feeding back into re-Appointment or termination). Each step reinforces accountable oversight.
1. Appointment: The process begins with a formal appointment. A person or organization (the Principal) officially designates an AI agent to act “on behalf of” them in some capacity. This is analogous to hiring or assigning a human agent to a role. The appointment defines the identity binding – linking the AI to its principal – and sets the stage for all subsequent oversight. It’s not a casual deployment of an AI; it’s a deliberate act with record-keeping. Every Appointed AI comes into being through a principled appointment event, establishing its mandate and the responsible principal up front.
2. Authority: Upon appointment, the AI is granted specific authority. This means the AI is given permission to make decisions or take actions in defined domains or up to certain limits. Crucially, the authority is limited and explicit – much like how a power of attorney might allow someone to make financial transactions up to a certain dollar amount. The AI’s credentials (API keys, access tokens, roles, etc.) are provisioned according to this scope. No Appointed AI has open-ended power; it operates under the principle of least privilege, only what is necessary for its mandate. This “permission-granted intelligence” approach ensures the AI cannot exceed the authority its principal has delegated[6].
3. Attribution: As the AI begins to act, all its decisions and actions are attributed. Every action carries a signature or trace linking back to the agent and principal[6]. In practice, this means meticulous logging and identity tagging of AI outputs. If the AI sends an email, executes a trade, or makes a recommendation, the system generates an AAAT record (Appointment, Authority, Attribution, Termination) or similar audit trail entry. Attribution answers the question: “Who (or what agent) did this, under whose authority, and when?” – ensuring that nothing the AI does is anonymous or untraceable. This is akin to a bodycam or black-box recorder for the AI’s activities. It provides an immutable ledger of the AI’s conduct (often cryptographically secured), so that responsibility can always be tracked. As noted in industry guidance, accountability is impossible without attribution – “there must be a human or organization responsible for the AI’s actions — AI should not be an excuse for ‘the computer did it’”[7]. Attribution embeds this principle at the technical level.
4. Audit: With continuous attribution, Audit becomes feasible and is the next pillar of the loop. Appointed Intelligence systems are built to be auditable by design[6]. Auditing can be proactive (ongoing monitoring of the AI’s actions against its mandate) and reactive (formal reviews if something goes awry). The audit process examines the attributed logs and outcomes to ensure compliance with the mandate and broader regulations or ethical standards. Importantly, audits in this context aren’t occasional checklists – they are a structured, regular practice. For example, an AI financial advisor agent might undergo a weekly compliance audit, and a continuous anomaly detection might flag any action outside its mandate for immediate review. The goal is to catch issues early and verify that the AI’s “judgment” aligns with the principal’s intent and legal norms. In governance terms, this provides ex post accountability to complement the ex ante controls. Every outcome the AI produces is not just traceable but reviewable, forming a basis for trust. (Indeed, only about 52% of enterprises today say they can even track and audit all data accessed by their AI agents[8] – Appointed Intelligence aims for 100%, with rich context for each action.)
5. Revocation: The final (and critical) element of the loop is Revocation. If an audit uncovers that the AI has violated its mandate, produced unacceptable outcomes, or if the context changes (e.g. the task is done, or new rules apply), the AI’s authority is promptly revoked. Revocation is essentially the “firing” or de-authorization of the AI agent. This could mean deactivating its credentials, shutting down the instance, or otherwise terminating its ability to act on behalf of the principal. Swift revocation is vital – one of the key risk metrics we discuss later is Time-to-Revocation, reflecting how fast an AI can be neutralized once it’s deemed rogue or faulty. In human terms, if Appointment is the hiring, Revocation is the firing (or suspension) for cause or completion of service. By formalizing a “kill switch” or authority termination step, Appointed Intelligence ensures that no AI agent operates beyond the tolerance of its principal or society. The loop then closes: after revocation (or at the end of an agent’s task lifecycle), a new Appointment may occur with updated parameters, or not at all. This cyclic view reinforces that AI authority is not one-and-done but continually earned and re-evaluated.
Why this loop matters: The A^4R loop institutionalizes a core mindset shift – treating AI agents as governed entities with a lifecycle of trust, rather than static software deployed and forgotten. Each element of A^4R corresponds to a lever of control: Appointment gives upfront control over who can act; Authority defines scope control; Attribution provides trace control; Audit provides oversight control; and Revocation provides remedial control. Together, these create a self-reinforcing system of accountability. Big Tech’s standard AI deployments rarely close the loop this way – an AI service might log data and have some content filters, but it seldom has a formal appointment or a clear path to automatic revocation upon rule violations. By contrast, Appointed AI is “not just traceable – it’s accountable, by design”[6].
In summary, the A^4R loop shifts AI governance from passive guidelines to active governance. The diagram above (Figure 1) should be read as a continuous cycle: it is an ongoing governance process ensuring the AI remains within the guardrails set by its principal and by law. In practice, this might be implemented via smart contracts, enterprise policy engines, or specialized “governor” services that monitor the AI in real time. The next sections will detail the conceptual primitives underpinning this loop and how to operationalize tests and metrics around it.
To operationalize the A^4R loop, Appointed Intelligence relies on Seven Primitives – fundamental elements or building blocks that every appointed-AI system should have. These primitives are like the DNA of an Appointed AI framework, ensuring consistency and completeness in governance. They can be presented as a system framework or key pillars. The table below outlines each primitive and its role: