The Undefined Architecture of Artificial General Intelligence: An Inquiry into Definitions and the MindHYVE.ai Operational Paradigm

Author: Bill Faruki, Founder & CEO, MindHYVE.ai

Affiliation: MindHYVE.ai, Inc.

Keywords: Artificial General Intelligence, AGI, Ava-Fusion™, Orchestrated Intelligence, Agentic Systems, Autonomous AI, Swarm Intelligence

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Abstract

Despite significant progress in artificial intelligence, the notion of Artificial General Intelligence (AGI) remains conceptually ambiguous. No single definition has achieved consensus within academic, corporate, or policy circles. This paper explores the historical, philosophical, and functional dimensions of AGI definitions, contrasting dominant theoretical models with emerging industrial interpretations. We then present the operational definition adopted by MindHYVE.ai, rooted in an orchestrated agentic framework embodied in our Ava-Fusion™ architecture. This model challenges anthropocentric and monolithic paradigms, proposing instead a systemic, outcome-driven interpretation of AGI suited to real-world integration and cross-domain adaptability.

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1. Introduction

The discourse on Artificial General Intelligence (AGI) has evolved dramatically over the past two decades, shifting from speculative philosophy to a central concern of computational science and socio-economic policy. Unlike narrow or domain-specific AI, AGI refers to systems with the capacity to perform a wide array of intellectual tasks traditionally associated with human intelligence. Yet, as AGI becomes a dominant strategic objective across industry and government, it remains strikingly undefined.

The absence of a standardized definition is not merely academic—it has consequential implicationsfor funding, regulation, technological development, and public understanding. As such, the need for an applied, pragmatic, and theoretically robust definition has never been greater. MindHYVE.ai enters this discourse not with speculation, but with an operational thesis—a blueprint for AGI designed to be built, tested, and deployed.

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2. The Conceptual Landscape of AGI

2.1 Historic Roots and Philosophical Anchors

The roots of AGI trace back to Alan Turing’s 1950 proposition of a “universal machine” and John McCarthy’s foundational framing of artificial intelligence. These early conceptions assumed that general intelligence could be formalized and simulated through logic and computation.

However, as scholars such as Hubert Dreyfus, Searle, and Newell highlighted, human cognition is not merely computational—it is embodied, contextual, and dynamic. This tension between computational rationalism and cognitive realism continues to divide contemporary AGI theory.

2.2 Modern Definitions and Frameworks

Multiple organizations have attempted to define AGI with varying levels of abstraction and applicability:

a. Cognitive Equivalence Models

These definitions assert that AGI must perform “any intellectual task that a human can,” including reasoning, learning, perception, and creativity. This framing often implies human mimicry, leading to debates around artificial consciousness and theory of mind.

b. Performance Taxonomies (DeepMind)

DeepMind introduced a pragmatic classification system with tiers such as Emerging, Competent, Expert, Virtuoso, and Superhuman AGI. This framework assesses agents by their generalization ability and proficiency across domains.

c. Economic Definitions (OpenAI / Microsoft)

Leaked internal documents from OpenAI revealed a profit-based definition: any system generating ≥ $100B in annual economic impact qualifies as AGI. This definition marks a transition toward outcome-centric pragmatism.

d. Cognitive Architectures (SOAR, ACT-R)

Some define AGI via architectural completeness. A system must include symbolic reasoning, episodic memory, goal formation, and emotional modeling—essentially, a synthetic cognitive agent.

These approaches, while individually valid, collectively fail to converge. Each reflects its originator’s incentives—be they academic legitimacy, productization, or policy alignment.

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3. Challenges in Defining AGI

The definitional ambiguity of AGI stems from multiple unresolved debates:

• Human-Centric Bias: Most definitions imply that human intelligence is the gold standard, which may constrain innovation.

• Static vs. Dynamic Intelligence: Intelligence is often treated as a static ability rather than a dynamic process of emergent adaptation.

• Philosophical Liminality: Concepts like consciousness, self-awareness, and free will remain philosophically unresolved, making them problematic benchmarks for machine-based intelligence.

• Task vs. Capability Mismatch: Performance in tasks (e.g., chess, language translation) is not always a reliable proxy for generalized intelligence.

• Ethical Considerations: Definitions shape ethical governance. Ambiguity here creates regulatory vacuums or premature overreach.

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4. MindHYVE.ai’s Definition of AGI

At MindHYVE.ai, we reject both anthropocentric mimicry and abstract generality. We define AGI operationally:

“AGI is a modular ensemble of orchestrated, agentic intelligences capable of autonomous, cross-domain adaptation, real-time collaboration, and dynamic optimization of complex objectives under uncertainty.”

This definition rests on four core pillars:

4.1 Agentic Intelligence

Each AI component—called an “agent”—is autonomous, context-aware, and goal-oriented. Agents may represent legal reasoning (Justine), clinical strategy (Chiron), or economic modeling (Eli). They do not simulate human minds but perform specialist roles within a larger orchestration.

4.2 Orchestration via Ava-Fusion™

The Ava-Fusion™ platform coordinates agents in real time. It enables inter-agent communication, role delegation, and adaptive resource allocation. This is not a monolithic intelligence, but a society of minds, akin to Marvin Minsky’s “Society of Agents” theory.

4.3 Cross-Domain Transferability

MindHYVE AGI systems are not bounded by industry silos. Justine’s legal reasoning can be leveraged by Carter in retail contract compliance. This lateral intelligence propagation enables system-wide scalability and resilience.

4.4 Outcome-Centric Adaptability

AGI must serve dynamically shifting objectives: legal rulings, patient outcomes, capital efficiency, sustainability. It adapts strategies based on feedback loops and real-time data—a feature not available in most pre-trained LLMs.

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5. Implementation Framework: Ava-Fusion™ in Practice

The Ava-Fusion™ engine operationalizes our AGI definition through:

• Symbolic-Neural Hybrid Reasoning: Combining logic graphs with transformer-based learning.

• Behavioral Analytics Engine: Adjusting agent responses based on human and environmental signals.

• Contextual Priority Resolution: Dynamically adjusting agent roles based on emergent task hierarchies.

• Proactive Resource Optimization: Predicting bottlenecks and reassigning agent loads in real time.

This results in self-organizing, regulatory-compliant, and ethically tuned systems that meet the complexity demands of modern industry.

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6. Conclusion: Toward a Constructivist View of AGI

In a field dominated by speculative idealism and commercial opportunism, MindHYVE.ai proposes a constructivist, operational path forward. We treat AGI not as a mystical endpoint but as a continuously evolving ecosystem of coordinated agents. Our definition is practical, testable, and scale-ready.

AGI is not what it “is”—AGI is what it does.

And what it enables.

By reframing the discussion from philosophical mimicry to orchestrated utility, MindHYVE.ai is not waiting for AGI to emerge. We are building it—intelligently, ethically, and boldly.

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References

1. Turing, A. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433–460.

2. Legg, S., & Hutter, M. (2007). Universal Intelligence: A Definition of Machine Intelligence. Minds and Machines, 17, 391–444.

3. DeepMind (2023). Towards a Definition of AGI: A Taxonomy and Capabilities Report. arXiv:2311.02462.

4. OpenAI AGI Leak. (2024). Internal Documentation as Reported by Gizmodo.

5. Minsky, M. (1986). The Society of Mind. Simon & Schuster.

6. Searle, J. (1980). Minds, Brains, and Programs. Behavioral and Brain Sciences, 3(3), 417–457.