SRTA and the Trinity Configuration: A Conceptual Architecture for Safe AGI Coordination
draft-takagi-srta-trinity-00
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| Document | Type | Active Internet-Draft (individual) | |
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| Author | Takayuki Takagi | ||
| Last updated | 2025-09-13 | ||
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draft-takagi-srta-trinity-00
Internet-Draft T. Takagi
Intended status: Informational Independent Researcher
Expires: March 14, 2026 September 14, 2025
SRTA and the Trinity Configuration: A Conceptual Architecture for
Safe AGI Coordination
draft-takagi-srta-trinity-00
Abstract
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.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Current Challenges and the SRTA Framework . . . . . . . . . . 4
2.1. The Lack of Reliability in Multi-Agent AI . . . . . . . . 4
2.2. SRTA: A Practical Foundation for Safety . . . . . . . . . 4
3. A Conceptual Architecture: The Trinity Configuration . . . . . 5
3.1. Integrated Comparison Table . . . . . . . . . . . . . . . . 6
4. Advanced Information Flow: The Filioque Command . . . . . . . . 7
4.1. Filioque Variant Table . . . . . . . . . . . . . . . . . . 8
5. Conclusion: As a North Star for AI Research . . . . . . . . . . 8
6. Security Considerations . . . . . . . . . . . . . . . . . . . . 9
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . . 10
8. References . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.1. Normative References . . . . . . . . . . . . . . . . . . . 10
8.2. Informative References . . . . . . . . . . . . . . . . . . 10
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . . 10
1. 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 Framework
2.1. 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 Configurations
4. 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 Variant
5. 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. References
8.1. 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