True intelligence, whether exhibited by a bee foraging, an autonomous vehicle maneuvering, or a rescue team collaborating, transcends mere computational processes. It hinges on agency: the capacity for meaningful perception, decision-making, and action within a given environment. Understanding this agency, especially in our technologically advanced age, requires moving beyond a purely computational framework. Remarkably, even sophisticated intelligent systems demonstrate a propensity for self-organization around fundamental, often ternary, patterns.

The following framework’s central value lies in its movement beyond domain-specific models to identifying universal principles that govern any system capable of autonomous, goal-directed, and adaptive action. It proposes that intelligence in any system can be better understood through recurring triadic patterns operating at different levels.

At each level, the framework identifies one or more sets of three interacting elements (a “triad”) that together enable a certain capacity of agency. The levels build from basic survival mechanisms up to large-scale organizational structures:

• Foundational
• Individual
• Communal

The framework is based on the concept that “patterns of three” (triads) are fundamental building blocks of intelligent behavior. Every intelligent act is viewed as an interaction of three elements: an observer or agent, a signal or change in the environment, and an interpretation that gives the signal meaning. This idea resonates with the classic semiotic theory, which posits that a sign involves a triadic relationship between an object, a sign, and an interpretant (the meaning).

C. S. Peirce argued that triadic relations are irreducible; you can’t fully explain a meaning-making relation with just two components; you need all three. Furthermore, Peirce believed that higher-order relations could be constructed from triads. This notion provides theoretical support for the framework’s emphasis on triads as simple but rich structures. Triads are minimal units that can encapsulate complexity without collapsing into trivial pairs or expanding into unwieldy lists of factors.

Why are triadic patterns so effective in modeling the complexity of the real world? They strike a perfect balance: simple enough to understand quickly, yet rich enough to represent genuine complexity. Like building blocks, they can be combined to create larger structures. This insight has led to the creation of a practical framework that organizes these triadic patterns into three distinct levels, each building upon the previous one, much like the floors of a building.

Certain triads illustrate interacting dynamics, such as control, context, and change. Others frame coexisting dimensions that balance or create tension, such as hierarchy, holarchy, and heterarchy. In both instances, the triadic structure reveals irreducible relationships essential to agency.

Foundational

Before any system can be truly intelligent, it must first be viable. It needs to interpret its environment and maintain itself over time. This foundational level handles four essential functions:

Reasoning—Semiotics
How an agent interprets raw data. For instance, a honeybee (subject) perceives yellow petals (signal) and interprets them as “nectar here” (sign). This aligns with semiotic models of perception, where a sign represents something to an observer through an interpretive process. The framework emphasizes that sensing is never passive; it involves the agent attributing significance to stimuli.

• Subject: Observer or participant
• Signal: Environmental change
• Sign: Interpretation of the signal

Regulation—Cybernetics
A system maintains stability by responding to changes, akin to a feedback loop. The context provides the current conditions, a control mechanism adjusts the system’s behavior, and an outcome change is produced to keep the system within desirable bounds. This triad embodies fundamental cybernetic principles of feedback and homeostasis.

• Context: Conditions and constraints
• Control: Regulation and steering
• Change: Driver of adaptation

Resources—Homeostasis
All living or viable systems are open systems – they must take in energy/matter and export waste to sustain an internal steady state. An organism or organization must manage energy flows through continual exchange with its environment to maintain equilibrium (dynamic homeostasis).

• Energy: Required resources
• Exchange: Interaction with the environment
• Equilibrium: Maintaining balance

Coherence—Autopoiesis
An intelligent system must have some self-consistency or identity that persists even as it changes. In the language of systems theory (e.g., autopoiesis), this means certain core relationships or organizational patterns remain invariant to preserve the system’s identity. In other words, the organization of the system (its defining structure) must stay intact (integrity) for it to remain “the same system.”

• Identity: Persistent sense of self
• Integrity: Consistent actions and values
• Invariance: Stable core functions

Once these foundations are solid, systems can move to the next level of sophistication.

Individual

Once an agent has established the necessary foundations for viability, it can begin to operate as a self-directing system—one capable of engaging with its environment in purposeful, autonomous ways. This level marks the emergence of individual agency: the ability to form internal representations, make decisions, and take action based on more than just reactive inputs.

Here, the perception–cognition–action cycle becomes the core loop through which an agent exercises its autonomy. But intelligence isn’t simply about reacting; it’s about orienting behavior based on interpretation, intention, and internal regulation. The following triads represent the key capacities that enable an agent to function as a situated, intentional individual:

Perception
How the agent connects with the world and transforms sensory input into meaningful internal states. Perception isn’t passive. It’s a selective and interpretive process, where agents construct meaning based on both present signals and prior experiences.

• Measurement: Acquisition of raw data through sensors or receptors
• Model: Interpretive structure that gives coherence and context to input
• Memory: Accumulated experience used to refine perception and anticipate outcomes

Motivation
What initiates and orients behavior from within. Motivation gives the agent a reason to act. It transforms passive sensing into active seeking. It’s the grounding of intentionality.

• Drives: Innate or acquired needs that generate internal tension
• Desires: Goal-states imagined or projected to resolve those drives
• Direction: The strategic tendencies and preferences that shape which paths are pursued

Cognition
Where sensed experience becomes structured understanding and guides intelligent choice. Cognition allows the agent not just to know, but to know what matters. It introduces selectivity, foresight, and value-based prioritization.

• Awareness: Detection of meaningful changes in the environment or self
• Attention: Allocation of limited cognitive resources to salient elements
• Assessment: Deliberation about implications, urgency, and possible responses

Action
Decisions transform into interventions that modify the world or the agent’s own state. Action transcends mere output; it expresses intent, revealing the agent’s judgment, goals, and engagement with the environment.

• Plan: A structured sequence of intended steps to achieve a desired effect
• Project: Simulation or mental rehearsal of how the plan might unfold
• Perform: Execution of the chosen plan through motor or system actions

Collectively, these faculties constitute the inner loop of agency—a continuous cycle through which an individual agent interprets, decides, and acts within its environment. This loop is influenced by needs and norms, guided by internal models, and consistently engaged with the evolving situation. However, despite the intelligence of an individual agent, its capacity remains constrained by its own perspective and resources. True adaptation and emergence occur when multiple agents coordinate—not merely coexist, but communicate, trust, and collaborate.

Communal

As agents move beyond individual capabilities, they begin forming shared systems of coordination and meaning. The framework explores how agency extends through structured interaction—first through cooperative tasking, and then through durable relational architectures and structuring of agencies. The following triads represent the key capacities that enable an agent to function effectively and efficiently within organizational collectives:

Cooperation—Shared Action
Cooperative agency arises when multiple agents align around shared tasks, synchronize their actions, and maintain continuity through mutual understanding and accountability. This isn’t merely parallel execution; it’s the emergence of a shared operating context. At this level, interaction shapes intelligence. For this interaction to be effective, agents must engage in a triadic structure that underpins all resilient collaboration.

• Task: A clearly defined unit of coordinated work that binds agents in a shared objective
• Trust: The assurance that others will act competently and reliably toward the common goal
• Trace: A store of decisions that facilitates verification, introspection, and continuity

Concurrent action and distributed agency establish a unified system wherein tasks confer meaning upon agents’ roles. Trust mitigates verification overhead, which is essential for efficiency. Trace, recording interactions, ensures accountability, facilitates learning, and promotes scalability.

Cooperative tasking, while essential, is often episodic and transient. Upon task completion, cooperative alignment typically dissipates. To establish enduring cooperation—sustained across time, context changes, and evolving compositions—agents require a foundation beyond shared tasks. They necessitate structured relationships, consistent roles, and shared responsibilities. This represents the transition from cooperative to relational agency.

Relational—Enduring Social Form
Beyond fleeting cooperation lies the realm of relational agency, encompassing the enduring arrangement of agents bound not only by shared tasks but also by ongoing commitments, enacted roles, and normative expectations. This level pertains to the social structures that enable the persistence, evolution, and scalability of cooperation. This triad establishes the social grammar through which agents interpret their place within the system and in each other’s expectations. While tasks are episodic, roles and responsibilities persist, providing systems with memory, identity, and resilience.

• Relationships: The enduring links between agents that carry mutual recognition, history, and potential for interaction
• Roles: Contextually assigned functions or identities that define how an agent contributes to the collective
• Responsibilities: The obligations and entitlements that emerge from roles and are recognized by others in the system

Relational agency allows agents to recognize each other across time, coordinate without renegotiating every aspect, and develop social memory that transcends individual events. It’s the foundation of institutional intelligence in biological colonies, organizations, and hybrid human-AI teams. At this level, relationship structure is instrumental, delineating responsibilities, authority distribution, and expectations. In human systems, this triad manifests in organizational charts, legal contracts, professional ethics, and team rituals. In artificial systems, it’s role-based access controls, protocol interfaces, and delegated decision rights. In both cases, relational agency enables:

• Scalability through modular coordination
• Accountability through shared norms
• Flexibility through role reassignment and evolution

Structuring—Modalities of Organization
Scaling systems necessitates a sophisticated architecture. The next triad tackles this complexity by shifting from relational topology to a structural modality, focusing on how large-scale agent networks self-organize.

• Hierarchy – A stratified structure where authority and responsibility are distributed top-down, often optimizing for clarity, stability, and command.
• Holarchy – A nested structure of autonomous yet interdependent units (holons), where each part is both a whole and a part, enabling modular coordination and adaptive layering.
• Heterarchy – A flexible, networked structure where authority and coordination flow laterally or dynamically, allowing agility, diversity, and emergent leadership.

These aren’t mutually exclusive but interacting forces. Resilient systems fluidly reconfigure between them depending on:

• Mission criticality
• Temporal urgency
• Role ambiguity
• Environmental volatility

While frequently regarded as distinct models, these three modalities exhibit characteristics more akin to structural gradients. Intelligent systems dynamically traverse between them, occasionally prioritizing command (hierarchy), modularity (holarchy), or adaptability (heterarchy), contingent upon internal dynamics and external requirements.

Structuring agency is about knowing not just how to act, but how to arrange the conditions under which action becomes effective, resilient, and evolvable. It entails systems governing themselves, not through static rules but through adaptive patterning. In biology, this is the difference between a reactive nervous system and one that remaps itself after injury. In organizations, it’s the capacity to reconfigure reporting structures, alliances, or workflows in response to demands. In artificial systems like distributed AI, federated robotics, or decentralized autonomous organizations, structural agency determines whether automation is fragile or intelligent.

Intelligent systems with structured organization can perceive, decide, act, and restructure their own agency. They can meta-cognize at scale, reflecting on their organization and modifying it based on purpose, context, or failure.

Structuring agency represents meta-agency—the capacity not only to act, but to modify how action itself is organized, delegated, and evolved. This is the hallmark of truly adaptive systems: they change their behavior and the architecture of behavior itself.

This completes the arc from raw survival to strategic self-organization, an ascending spiral of agency that empowers each level and creates a living system capable of understanding, acting, evolving, and enduring.

Conclusion

All adaptive systems—whether biological swarms, human organizations, or AI collectives—rely on triadic convergence across levels. Intelligence emerges not from isolated capabilities, but from the recursive reinforcement between signal, structure, and situational response.

As AI systems advance and human-agent collaborations become more complex, we need frameworks to understand true intelligence. The triadic model offers a clear approach to designing intelligent, resilient, and adaptive systems. It emphasizes that intelligence isn’t just about powerful algorithms or data; it’s the dynamic interplay of multiple triadic patterns working together across different levels.