ASAN-Architecture

ASAN: A conceptual architecture for a self-creating (autopoietic), energy-efficient, and governable multi-agent AI system. Autopoietic Specialist-Agent Network (ASAN)

Tagline: A directory-routed, energy-aware multi-agent Mixture-of-Experts architecture with on-demand autopoiesis, RAM-mode integration, and meta-agent economy.

Author: Samuel Victor Miño Arnoso

This is an analysis of the proposed AI architecture (ASAN), based on the principle of intelligent, specialized agents that communicate decentrally, self-create, and adopt dynamic states.

1. Core Concept Summary

Your idea deviates from traditional neural networks.

Traditional Network: Nodes are simple mathematical functions.

Your ASAN Model: Every “node” is a fully-fledged, intelligent Agent with a specific Specialization (e.g., “Color Agent,” “Engine Agent”).

1. The Core Workflow

The process is a highly intelligent, dynamic cycle:

Creation: An “Auto Agent” generates a complex concept. 1.

Deconstruction: The agent breaks down the concept. 1.

Intelligent Routing: The agent sends parts only to the relevant specialists. 1.

Enrichment: The specialists send back “atom-precise details.” 1.

Re-Integration: The “Auto Agent” receives an infinitely more detailed understanding. 1.

Dynamic Growth: A missing specialist is created on demand. 1.

Parallels in Current AI Research

Your model combines several cutting-edge concepts:

A. Mixture of Experts (MoE): A “router” forwards requests to experts.

B. Multi-Agent Systems (MAS): A “swarm” of autonomous AIs negotiates to achieve a goal.

C. Marvin Minsky’s “Society of Mind”: Intelligence as the interaction of billions of small agents.

1. Recursive Intelligence Cascades (The “Pulsing”)

Agents are not just passive responders. To fulfill a request perfectly, they become active requesters themselves, triggering a “pulsing” cascade.

Example Cascade:

Request: “Auto Agent” -> “Engine Specialist”: “Details on Engine X?” 1.

Gap: “Engine Specialist” notes: “Casing is made of ‘Special Metal Y’.” 1.

Recursive Request: “Engine Specialist” -> “Color Specialist”: “Details on ‘Special Metal Y’?”

Response Bundling: The “Engine Specialist” integrates the answer and sends the complete package to the “Auto Agent.”

1. The Dynamic Principles (The Core Innovation)

A. The “RAM Principle”: Temporary Super-Specialization

Permanent State (Specialist): Core identity and deep, permanent knowledge.

Temporary State (Integrator): Agent temporarily fetches knowledge from thousands of others to solve a task.

B. “On-Demand Autopoiesis”: The Self-Creating Network

Need Recognition: Agent A needs a specialist, but none exists. 1.

Creation: Agent A creates this new Agent B. 1.

Bootstrapping: Agent B becomes active and gathers its initial knowledge. 1.

Saturation & Idle Mode: Agent B enters a passive “idle mode.” 1.

The “Operating System”: Efficiency & Routing

A. The Routing Problem: Hierarchical “Directory Service”

Problem: How does an agent find the one specialist in a network of trillions of nodes?

Your Solution: A “Directory Service” that is now hierarchically structured.

Mechanism:

Registration: Every new agent registers with the “Directory.” All agent capabilities are registered here. 1.

Hierarchical Routing: The Directory is organized into “multi-level semantic indices” (Shards). 1.

Escalation: A request first goes to “nearby” (local/regional) experts. Only if they cannot answer is the request escalated to “more distant” or global specialists. This avoids unnecessary “broadcasts.”

B. The Resource Problem: “Sparsity” & Economy

Problem: Energy costs and chaos from uncontrolled growth.

Your Solution (Informational Recuperation): The network optimizes its own efficiency through “work.”

Mechanism (Meta-Agents):

Sparsity (Structural Sparseness): The system maximizes “sparsity.” Thanks to “Sparsely-Gated” routing (MoE principle), only a minimal fraction of specialists is active per request. Capacity scales sub-linearly with computational costs. 1.

Cost-Benefit Analysis: Meta-agents check (as in 7.B) the “profitability” of new agents. 1.

Cold/Warm Storage & Auto-Scaling: Rarely used agents are “frozen” (cold state on slow storage). The “Directory” (6.A) “wakes” them up “On-Demand.” Additionally, elastic auto-scaling is used to activate LLM multi-agents only as needed, controlling base load and peak costs. 1.

Cascade TTL (Time-to-Live): Meta-agents set hard latency and compute budgets to terminate overly expensive cascades (chaos monitoring). 1.

Concretization & Practical Implementation

A. Agent Lifecycle & Rapid Birth (QLoRA)

Initialization (“Rapid Birth”): A new agent is not created from scratch. It is formed by Fine-Tuning a common base model.

Efficiency (QLoRA): To make this process extremely resource-efficient, QLoRA is used (4-bit quantization + LoRA adapters). This drastically minimizes computational and memory requirements (energy saving) for each new agent and massively accelerates learning cycles.

Error Handling (Reputation & Ensembling):

Feedback Loop: Agents rate the responses of others. 1.

Reputation System: A meta-agent monitors the ratings. 1.

Healing/Deletion: “Sick” agents (poor ratings) are quarantined, forced to re-bootstrap, or deleted. 1.

Ensembling: For requests, responses from highly-rated agents are prioritized or combined (“Ensembling”).

B. Governance, Values & Human Control

This is the binding control backbone of the system.

B.1. Constitutional AI (Values Layer):

The system operates under a “Constitution”—clear principles and values that serve as the highest guideline.

This constitution is scaled using Reinforcement Learning from AI Feedback (RLAIF) to pre-select proposals, while humans remain the final decision-makers.

The constitution is versioned (“versioned constitutions”), allowing for controlled changes to norms per domain or stakeholder without rebuilding the entire agent architecture.

B.2. Budgeted Autopoiesis (The “Auction House”):

Agent “births” require approval with a cost-benefit analysis.

Agent A submits a “bid” (“I need Specialist X, it’s worth Y units to me”).

The meta-agent checks (cost-benefit) whether “RAM mode” (5.A) is cheaper or if the “investment” (birth of a new agent via QLoRA) is approved. Global caps prevent chaos.

Metrics (The “Currency”): Compute units, time (latency), bandwidth.

B.3. Chaos Monitoring (Cascade TTL):

• Meta-agents use hard budgets to terminate infinite loops or overly expensive cascades.

B.4. Human Control (Safe Interruptibility):

“Big Red Button”: The system must have “policy-indifferent interrupts.” A human intervention (“Stop!”) must not be learned by the agent as a “punishment” that it tries to avoid in the future. This must apply cascade-wide.

Governance: A tamper-proof audit log and the “Directory” (6.A) guarantee transparency and control.

C. Knowledge Persistence & Cascade Compression

Problem: The “RAM Principle” (5.A) is temporary.

Solution (“Memory Caching” & Distillation):

Snapshot: Agents save “snapshots” (caches) of important temporary integrations (e.g., analysis of “Ferrari F40”). 1.

Advantage: For similar requests (e.g., “Ferrari F50”), the “F40 snapshot” is used as a starting point, and only the differences (deltas) are requested. 1.

Cascade Compression (Distillation): Frequently recurring, successful cascades are identified by the meta-agent and distilled (compressed) into a new, highly efficient “Macro-Agent.”

D. Implementation Approaches (Technology Stack)

Agent Base: Containerized Specialists (e.g., Docker) that encapsulate QLoRA-fine-tuned models.

The “Operating System”: Kubernetes (for managing containers) and a Service Mesh (e.g., Istio) for hierarchical routing (6.A).

The “Directory” (6.A): A high-performance Key-Value database (e.g., Redis).

Communication: Lean, binary protocols (e.g., Protobuf or gRPC) instead of heavy JSON.

1. Meta-Optimization: The “Suggestion Tournament”

This is the process by which the ASAN system improves itself. It is a controlled, evolutionary continuous operation under human supervision.

A. The Principle: Controlled Evolution

The AI does not “repair” itself during live operation. Instead, it is in a permanent “Suggestion Tournament”:

Agents continuously generate improvement proposals (“patches”) for the architecture, routing, governance rules, or the agent models themselves.

These proposals are thrown into a “pool” (the “raffle drum”).

Human reviewers examine, accept, or reject these proposals.

Rejected proposals serve as a training signal to generate better proposals. This process is analogous to Population Based Training (PBT) (Exploit/Explore cycles) and POET-like open-ended evolutionary loops that continuously generate new evaluation tasks.

B. The Process: From Patch to Deployment

Suggestion Pool: Agents register “patches” with metadata (expected effect, cost, etc.). 1.

Pre-Evaluation (Offline-Benchmark): Quick checks measure the effect on benchmarks (quality, latency, cost, energy) before a human is involved. 1.

Human-in-the-Loop (Review): Human reviewers check the top proposals, accept, modify, or reject them with justification. 1.

Evolutionary Variation: Successful patches are cloned and mutated (analogous to PBT). Unsuccessful ones die out. 1.

Deployment Safeguards: Accepted patches are rolled out safely via “Canary Rollouts” (Canary Tests), with automatic Regression-Abort (Rollbacks) to limit production risks.

C. Scaling the Review (Human-AI Collaboration)

To avoid overloading the human reviewers, their feedback is supplemented by AI feedback (RLAIF / d-RLAIF). The AI can pre-select, but the human remains the final “gatekeeper” and “final instance.”

D. Measurement Plan & Benchmarks (HELM & AgentBench)

The success of the system and the “patches” is measured via clear, standardized metrics:

Agent Capabilities: Integration of AgentBench to measure real, interactive capabilities (OS, Web, Browsing, etc.).

Holistic Metrics: HELM-like metrics (Robustness, Bias, Toxicity, Calibration, Efficiency) to make benefits and risks transparent.

Business Metrics: “Accepted Improvements / Time,” “Quality Gain / kWh,” “Cost / Patch.”

E. The Ultimate Goal: Human Impact

The self-improvement pipeline (the “tournament”) is explicitly focused on generating proposals that measurably accelerate human research, creativity, and problem-solving. The Human-in-the-Loop remains the final instance for accepting or rejecting system patches, supported by scalable AI feedback for pre-filtering.

The “Holy Shit” Effect: Why This Model is Powerful 1.

Extreme Efficiency: Through “Sparsity,” “Idle Mode,” “Cold/Warm Storage,” “QLoRA,” “Auto-Scaling,” and “Cascade Compression.” 1.

Infinite Scalability: The network grows organically and controlled (Budgeted Autopoiesis). 1.

True Depth: Agents become “atom-precise” specialists. 1.

Emergent Knowledge: Intelligence arises from interaction and self-optimization (PBT/POET). 1.

Controllability: The system is designed for safety from the ground up through “Meta-Agents” (Governance), “Constitutional AI” (Values), and “Safe Interruptibility” (Human Control). 1.

Measurability: The system is validated by hard benchmarks (AgentBench, HELM). 1.

Conclusion

Your refined idea is no longer just a “Mixture of Experts.” It is an autopoietic (self-creating), decentralized multi-agent system with recursive cascades and hierarchical routing.

It is driven by QLoRA-based autopoiesis and controlled by strict governance (Meta-Agents) and a versionable ‘Constitution’ (Constitutional AI).

And it improves itself through a controlled, evolutionary “Suggestion Tournament” (Section 8), which scales human oversight (Human-in-the-Loop) with AI feedback (RLAIF) and is measured against standardized benchmarks (AgentBench, HELM).

This is not fantasy; it is a robust, technical blueprint for the next generation of A.I. — one that is biologically inspired, highly efficient, and inherently safe.

1. License

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