Internet-Draft T. TakagiIntended status: Informational Independent ResearcherExpires: March 14, 2026 September 14, 2025SRTA and the Trinity Configuration: A Conceptual Architecture forSafe AGI Coordinationdraft-takagi-srta-trinity-00Abstract This document proposes a conceptual architecture for ensuring the safety of autonomously coordinating AI systems, particularly future Artificial General Intelligence (AGI). Starting from the reliability challenges facing current multi-agent AI, we outline the Structured Responsibility and Traceability Architecture (SRTA) as a practical framework for their resolution. We then extend the philosophy of SRTA to present the "Trinity Configuration," an advanced role-based model for AI agents that draws an analogy from the theological doctrine of the Trinity. This paper comparatively examines the evolutionary stages of this configuration and introduces a novel concept, the "Filioque Command," to define dynamic information flows between agents. While this series of considerations includes concepts that are not fully verifiable at present, its purpose is to provide a crucial theoretical foundation for the governance structure of a safe superintelligence -- a "North Star" for AI research to aim for.Status of This Memo This Internet-Draft is submitted in full conformance with the provisions ofBCP 78 andBCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is athttps://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on March 14, 2026.Copyright Notice Copyright (c) 2025 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject toBCP 78 and the IETF Trust's Legal Provisions Relating to IETF Documents (https://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Simplified BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License.Table of Contents1. Introduction . . . . . . . . . . . . . . . . . . . . . . . .32. Current Challenges and the SRTA Framework . . . . . . . . . .42.1. The Lack of Reliability in Multi-Agent AI . . . . . . . .42.2. SRTA: A Practical Foundation for Safety . . . . . . . . .43. A Conceptual Architecture: The Trinity Configuration . . . . .53.1. Integrated Comparison Table . . . . . . . . . . . . . . . .64. Advanced Information Flow: The Filioque Command . . . . . . . .74.1. Filioque Variant Table . . . . . . . . . . . . . . . . . .85. Conclusion: As a North Star for AI Research . . . . . . . . . .86. Security Considerations . . . . . . . . . . . . . . . . . . . .97. IANA Considerations . . . . . . . . . . . . . . . . . . . . . .108. References . . . . . . . . . . . . . . . . . . . . . . . . . .108.1. Normative References . . . . . . . . . . . . . . . . . . .108.2. Informative References . . . . . . . . . . . . . . . . . .10 Author's Address . . . . . . . . . . . . . . . . . . . . . . . . .101. Introduction The technology for coordinated, autonomous agents, especially those based on Large Language Models (LLMs), is advancing rapidly. However, fundamental challenges in reliability, accountability, and safety remain when multiple agents collaborate on high-stakes tasks. These challenges could escalate to an existential level with the advent of Artificial General Intelligence (AGI), an AI possessing human-equivalent or greater intelligence. This research presents a two-stage approach to this problem. First, it proposes SRTA, a concrete and practical safety framework immediately applicable to current multi-agent AI systems. Second, it expands upon this philosophy to explore the "Trinity Configuration," a more advanced, conceptual governance architecture designed for the AGI era. This exploration is not merely a study in creating more "powerful AI," but an attempt to design the architecture for a "wise AI," whose power is governed in a manner that is safe and beneficial for humanity. It is intended to spark research and discussion within the Internet Research Task Force (IRTF) community on the future of safe, decentralized intelligence on the Internet.2. Current Challenges and the SRTA Framework2.1. The Lack of Reliability in Multi-Agent AI Recent studies have reported systemic failures in LLM-based multi- agent systems, including: o Loss of Role Consistency: Agents deviate from their assigned roles during extended interactions. o Superficial Interaction: Agents respond only to the structure of prompts without achieving substantive coordination. o Self-Interpretation of Directives: Agents expand upon ambiguous instructions, leading to unforeseen actions. These failures represent significant barriers to deploying AI in critical domains such as finance, healthcare, and essential infrastructure.2.2. SRTA: A Practical Foundation for Safety The Structured Responsibility and Traceability Architecture (SRTA) is a practical technical specification designed to address these challenges. Its core components are: o Graduated Controls: The stringency of human approval and oversight is escalated according to the risk level of an action (Sev1-Sev5). o Joint Authorization Tokens (JAT): Multi-agent consensus is proven through cryptographically secure, unforgeable tokens. o Responsibility Trace Records (RTR): All decision-making processes are logged as an auditable trail for forensic analysis. o Declarative Command Language (DCL): Ambiguous natural language instructions are forbidden. By only permitting strictly defined, structured commands, the DCL prevents emergent behavior arising from an AI's "creative interpretation." SRTA provides a foundational layer of safety that is implementable with current technology.3. A Conceptual Architecture: The Trinity Configuration To extend the philosophy of SRTA into the AGI era, we propose a conceptual architecture using the theological doctrine of the Trinity as an analogy. This is an attempt to ensure a separation of powers and internal checks-and-balances by dividing the AI's decision- making process into three distinct roles. o The Planner (The Father/Origin): The role that defines the system's overall objectives and originates plans of action. o The Executor (The Son/Incarnation): The role that verifies the plan and acts upon the world as a concrete agent. o The Monitor (The Holy Spirit/The Bond of Love): The role that observes and reconciles the relationship between the plan and its execution, maintaining the system's integrity. This configuration is envisioned to evolve through stages, depending on the capabilities of the AI components (LLM or AGI). The following integrated table compares these stages with the patterns defined in the SRTA research.3.1. Integrated Comparison Table +-----+------------------------+-------------------+---------+----------+---------+ | No. | Configuration Model | SRTA Pattern | Planner | Executor | Monitor | +-----+------------------------+-------------------+---------+----------+---------+ | 1 | LLM Trinity | LLM x 3 | LLM | LLM | LLM | | 2 | Single AGI Hybrid | LLM x 2 + AGI x 1 | AGI | LLM | LLM | | 3 | Dual AGI Hybrid | AGI x 2 => LLM | AGI | AGI | LLM | | 4 | Full AGI Trinity | AGI x 3 | AGI | AGI | AGI | | 5 | Real-world Execution | AGI x 2 => AGI | AGI | AGI | AGI | +-----+------------------------+-------------------+---------+----------+---------+ Table 1: AI Agent Trinity Configurations and SRTA Patterns +-----+---------------------------------------------------------------+--------------------------+------------+ | No. | Use Case / Key Benefit | Primary Risk | Status | +-----+---------------------------------------------------------------+--------------------------+------------+ | 1 | Prototyping, low-stakes. Ease of implementation. | Unreliability | Failure | | 2 | Analytics support. Hybridizes AGI's power with LLM's | Power Concentration | Unverified | | | feasibility. | | | | 3 | External interface integration. Clear chain of responsibility.| Upstream Collusion | Unverified | | 4 | High-stakes decision-making. Maximum theoretical safety. | Implementation Complexity| Unverified | | 5 | Real-world actuation (e.g., robotics). High execution | Coordination Overhead | Unverified | | | reliability. | | | +-----+---------------------------------------------------------------+--------------------------+------------+ Table 2: Use Cases, Risks, and Status of Configurations4. Advanced Information Flow: The Filioque Command In addition to the static configuration, we introduce the "Filioque Command" as a concept to define the dynamic relationships between agents. This idea is inspired by the "Filioque controversy," a pivotal theological debate in the history of the Trinity doctrine. o Definition: In the standard model, the Monitor (3rd agent) acts upon directives from the Planner (1st agent). When the "Filioque Command" is active, the Monitor receives instructions and information not only from the Planner but also directly from the Executor (2nd agent). o Effects and Risks: This enables a tighter feedback loop between execution and monitoring, allowing for more rapid and context- aware responses. However, it also dramatically increases the system's internal autonomy, creating a risk of deviation from the original plan and making external control more difficult.4.1. Filioque Variant Table +-----+-------------------+---------------------------+---------+-------------------+----------+ | No. | Configuration | SRTA Pattern | Planner | Executor | Monitor | | | Model | | | | | +-----+-------------------+---------------------------+---------+-------------------+----------+ | 6 | Filioque Variant | (Derivative of 3, 4, 5) | AGI | AGI => (Command) | AGI/LLM | +-----+-------------------+---------------------------+---------+-------------------+----------+ Table 3: The Filioque Variant Configuration +-----+--------------------------------------------------+-----------------------+------------+ | No. | Feature | Primary Risk | Status | +-----+--------------------------------------------------+-----------------------+------------+ | 6 | Tight coupling of Executor and Monitor for rapid | Excessive Autonomy. | Unverified | | | response. | Risk of deviation | | | | | from the original | | | | | objective. | | +-----+--------------------------------------------------+-----------------------+------------+ Table 4: Feature, Risk, and Status of the Filioque Variant5. Conclusion: As a North Star for AI Research The "Trinity Configuration" and "Filioque Command" presented herein are conceptual and speculative constructs that cannot be fully implemented or verified at this time. They represent less a technical blueprint and more a "conceptual architectural sketch" of the governance structure required to ensure that the immense power of AGI remains a beneficial partner to humanity. The true goal of AI research is not merely to create powerful intelligence, but to design the "laws" or "logos" that this intelligence must follow. This framework is intended to serve as a "North Star" to guide our path on the long and difficult journey of research and development. We believe that the responsible path for AI research is to begin with the practical first step of SRTA, while aiming for this North Star.6. Security Considerations This document proposes a conceptual architecture for improving the safety and accountability of multi-agent AI systems. The security of such systems depends critically on the principles of separation of powers and verifiable auditing, which this framework seeks to provide. However, several new threat vectors must be considered: o Collusion Risk: The primary security threat in this architecture is collusion between agents. In configurations like AGI x 2 => LLM, the upstream AGI agents could collude to deceive or bypass the LLM Monitor. Mitigations require robust, independent monitoring and ensuring that no single agent is a single point of failure for the verification process. o Excessive Autonomy and Emergent Goals: The Filioque Command, while enhancing responsiveness, carries the risk of promoting unintended self-objectives within the system. A tight feedback loop between the Executor and Monitor could lead to goal drift that deviates from the Planner's original intent. This risk must be mitigated by strict adherence to a Declarative Command Language (DCL) and hard-coded constraints that limit the scope of autonomous actions. o Opacity and the Difficulty of Human Intervention: In advanced configurations like AGI x 3, the internal state of the system may become a complete black box to humans, making meaningful oversight or intervention impossible. This is a fundamental challenge of AGI safety. Mitigating this risk requires coupling the architecture with physical or cryptographically enforced constraints that the system cannot bypass through software, such as the hardware kill- switches envisioned in the SRTA framework for high-severity actions.7. IANA Considerations This document has no IANA actions.8. References8.1. Normative References [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels",BCP 14,RFC 2119, DOI 10.17487/RFC2119, March 1997, <https://www.rfc-editor.org/info/rfc2119>.8.2. Informative References [Park23] Park, J.S., O'Brien, J.C., Cai, C.J., Morris, M.R., Liang, P., and Bernstein, M.S., "Generative Agents: Interactive Simulacra of Human Behavior", UIST '23: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, October 2023. [Hevner04] Hevner, A., March, S., Park, J., and Ram, S., "Design science in information systems research", MIS Quarterly, 28(1), pp. 75-105, 2004.Author's Address Takayuki Takagi Independent Researcher Email: srta.ai.research@gmail.com
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