Toward a General Intelligence: Multi-Agentic Orchestration and the Principle of Relevance Realization
Table of Contents
Abstract
The pursuit of Artificial General Intelligence (AGI) demands the integration of sophisticated cognitive capacities traditionally associated with biological organisms, particularly relevance realization—the foundational process by which agents identify and utilize task-specific contexts from within a broader, ill-defined environment. Drawing inspiration from Jaeger et al.’s framework ("Naturalizing Relevance Realization," 2024), we explore the implementation of relevance realization within a computationally grounded multi-agent orchestration framework. We argue that effective AGI can emerge through an orchestrated assembly of specialized agents, each autonomously executing relevance realization, integrated into a cohesive system exhibiting adaptive, emergent intelligence. This paper details a novel architecture for a Relevance-Aware Agentic (RAA) system and its orchestration via a Retrieval-Augmented Generation (RAG) platform, effectively demonstrating the principles of agency, anticipation, adaptive dialectics, and constraint-driven self-correction. We outline the foundational theory, computational architecture, implementation strategy, and potential capabilities of such a platform.
Introduction
Artificial intelligence has historically sought to replicate human cognitive abilities through symbolic representation, logical inference, and statistical machine learning. Despite significant progress, a major gap remains between existing models and truly general intelligence—systems capable of solving broadly defined problems and engaging meaningfully in open-ended tasks. To bridge this gap, new frameworks are necessary—those deeply rooted in principles governing natural cognition. In their influential paper, Jaeger et al. (2024) offer a transformative hypothesis, positing relevance realization as a core, non-computational aspect of cognition uniquely capable of guiding meaningful interactions between an organism and its environment.
However, Jaeger et al. dismiss artificial systems prematurely, assuming a complete inability of algorithmic or computational models to approximate such naturalistic cognition. Contrary to this view, we propose a multi-agent architecture that does not require literal biological autonomy, yet effectively implements functional equivalents of relevance realization through computational approximations of anticipation, constraint adaptation, and multi-agent orchestration.
This paper argues that through orchestrating specialized relevance-aware computational agents, one can simulate emergent, adaptive cognition. Such an architecture, integrated via a RAG platform, possesses the potential to realize true Artificial General Intelligence, bridging theory and implementation through relevance-driven self-correction and adaptive feedback loops.
Theoretical Background
Relevance Realization: Core Concepts
Jaeger et al. (2024) define relevance realization as the process by which agents delimit a meaningful arena from a large, ill-defined world to effectively engage with specific problems. This delimitation is neither purely computational nor symbolic; it emerges through a dynamic interaction of three key processes:
- Autopoiesis (Self-Manufacture) – Self-referential system boundaries.
- Anticipation (Predictive Modeling) – Internal models guiding expectations.
- Adaptation (Agent-Arena Interaction) – Continuous refinement through interaction with environmental affordances.
These interrelated processes are characterized by dialectical "trialectics," mutual co-constitution, and openness to continuous emergent possibilities (the "adjacent possible"). Together, these form the core of what Jaeger et al. argue makes biological cognition uniquely robust and adaptive.
Extending to Computational Systems
While biological cognition involves self-manufacture and autonomy, computational cognition need not replicate biological processes literally. Instead, we propose that computational models can approximate these principles through mechanisms such as:
- Agent specialization and orchestration: Instead of literal self-manufacture, we employ dynamically instantiated agents, each configured to address contextual goals.
- Predictive anticipation models: Using probabilistic generative models to simulate adaptive anticipation and trial-and-error adaptation.
- Adaptive dialectics via orchestration: Multi-agent negotiation and adaptive selection processes to approximate naturalistic self-correction and refinement.
By constructing an orchestration layer capable of dynamically instantiating relevance-driven specialized agents, we simulate the relevance realization mechanisms Jaeger et al. describe without needing literal biological attributes.
Computational Architecture: The Relevance-Aware Agentic (RAA) Framework
Our framework comprises three interrelated computational components: Relevance-Aware Agents (RAAs), a RAG-based orchestrator, and a dynamic environment model.
Relevance-Aware Agents (RAAs)
Each RAA is a specialized computational entity capable of:
- Contextualizing ill-defined tasks.
- Predicting outcomes via internal generative models.
- Adaptively refining behavior through iterative feedback.
RAAs exhibit the following structure:
- Goal Identification Module: Uses vector embedding and similarity searches to identify relevant goals from loosely defined problems.
- Anticipation Model: Implements transformer-based prediction modules for simulating future states of the environment.
- Adaptive Feedback Module: Integrates reinforcement learning (RL) loops for self-correcting predictions based on task outcomes.
Retrieval-Augmented Generation (RAG) Orchestration Platform
The orchestration platform dynamically integrates multiple RAAs through a RAG architecture, leveraging retrieval methods to contextually instantiate agents based on perceived relevance and necessity.
- Dynamic Retrieval: Selectively activates agents based on context-specific retrieval queries using embeddings and relevance scores.
- Generative Synthesis: Combines retrieved agent predictions into coherent task solutions via a generative large language model.
- Adaptive Relevance Adjustment: Iteratively updates agent relevancy metrics based on empirical feedback from task execution outcomes.
Environment and Affordance Model
Our environment model encodes affordances—opportunities and constraints of the task domain—dynamically represented via multimodal embeddings and vector similarity searches. These embeddings serve as representations of affordance landscapes, supporting agents' continuous adaptive interactions.
Implementation Strategy and Operational Flow
Initialization and Goal Delimitation
- High-level prompts or task definitions are embedded as vectors and analyzed to instantiate initial sets of relevance-aware agents.
- Initial affordances and constraints are identified through environmental embeddings, shaping initial predictions and anticipations.
Predictive Anticipation and Trialectic Dialectics
- Agents generate internal anticipations via transformer-based generative models.
- Orchestrator evaluates initial agent outputs against evolving environment embeddings, iteratively refining predictive outputs and delimited goals through adaptive dialectical loops.
Adaptive Self-Correction
- Outcomes of orchestrated agentic predictions are continuously evaluated against actual environmental states, providing reinforcement signals.
- Reinforcement learning updates agent parameters and orchestration retrieval metrics, promoting adaptive refinement over time.
Continuous Emergent Adaptation
- New RAAs dynamically instantiate when environmental embeddings indicate emerging affordances or problems, realizing continuous exploration of adjacent possible states.
- Iterative orchestration and adaptation loops create self-correcting, emergent intelligence, mirroring biological relevance realization principles.
Experimental Validation and Potential Capabilities
To validate the proposed framework, we propose experiments structured around increasingly complex problem domains:
- Simple contextual reasoning tasks: To validate initial goal delimitation and anticipation modeling.
- Complex multi-task coordination: Testing the orchestration platform’s capacity to manage multi-agent relevance dialectics and emergent adaptation.
- Open-ended exploratory environments: Validating the ability to explore and exploit adjacent possible spaces, demonstrating a genuine emergent form of AGI.
We hypothesize that RAA-based systems will outperform traditional monolithic AI models in adaptability, efficiency, and task success across diverse and open-ended contexts.
Implications and Conclusions
By translating Jaeger et al.’s transformative insights on relevance realization into a computationally feasible multi-agent architecture, we create a platform capable of manifesting adaptive, emergent intelligence. This framework suggests artificial agentic systems, though distinct in physical constitution from biological organisms, can effectively realize critical cognitive properties through computational emulation.
Contrary to Jaeger et al.’s assertion of computational inadequacy, our proposal illustrates how principles such as anticipation, adaptive dialectics, and emergent affordance exploration can be embedded in computational contexts. Ultimately, this approach lays groundwork toward genuine AGI—intelligence capable of flexibly adapting to diverse, open-ended tasks through continual self-correction and relevance-driven adaptation.
In conclusion, multi-agent orchestration via relevance realization principles constitutes a promising pathway toward achieving true Artificial General Intelligence.