Introduction
In 1967, Arthur Koestler introduced the concept of “holons” in his book The Ghost in the Machine, coining a term that would reshape how we think about hierarchical organization in living systems. A holon is something simultaneously whole and part — from the Greek holos (whole) plus the suffix -on (particle). Every cell is a complete living system while being part of an organ. Every organ is a functional whole while being part of a body. This recursive pattern, which Koestler called “holarchy,” appears throughout nature at every scale of observation.
Six decades later, as we build increasingly sophisticated AI systems with memory and reasoning capabilities, we face a parallel challenge: how do we represent knowledge in ways that preserve context, hierarchy, and the nested relationships that make meaning possible? The answer may lie in recognizing that metagraphs — advanced graph structures that allow edges to contain their own graphs — are the computational manifestation of holonic principles.
The Power of Universal Interfaces
Before diving into knowledge representation, we need to understand what makes holonic systems so compelling from a design perspective: interface unification. The elegance of holonic architecture lies in a deceptively simple principle — when you interact with a system, you don’t need to know whether you’re working with an atom or an organism. The interface remains constant across scales.
This isn’t just philosophical abstraction. It’s the foundation of some of the most powerful computational paradigms we’ve developed.
Smalltalk and the Message-Passing Holon
Consider Smalltalk, one of the earliest and most purely object-oriented programming languages. In Smalltalk, everything — and I mean everything — is an object. Numbers are objects. Classes are objects. Methods are objects. The entire system is objects receiving messages and responding to them.
Here’s what makes this holonic: when you send a message to an object, you have no idea what’s on the other end. It might be a simple integer that just returns its value. It might be a complex collection containing thousands of elements that triggers elaborate iteration logic. It might be a proxy object that routes your message across a network to another machine entirely. You don’t know, and you don’t need to know.
The atomic object and the complex system composed of many objects present the same interface — message reception and response. This is holonic design in its purest form. The part and the whole are indistinguishable from the caller’s perspective. The composition of objects represents a system that also receives messages, creating perfect recursive structure.
This interface uniformity dramatically reduces complexity. You don’t write different code to interact with simple objects versus complex systems. You don’t need separate APIs for different scales of functionality. The complexity is hidden behind interface consistency, allowing you to reason about and manipulate systems at any scale using the same conceptual tools.
The Holonic Nature of Knowledge
This same principle applies to knowledge representation. Traditional knowledge graphs excel at representing binary relationships: “Alice knows Bob,” “Paris is-in France,” “aspirin treats headaches.” But human knowledge doesn’t organize itself in neat pairs. When you remember a vacation, you’re not accessing isolated facts — you’re retrieving a nested structure where the trip contains days, each day contains experiences, each experience contains sensations, conversations, and emotional valences. The vacation is simultaneously a whole experience and a part of your life story. Each day is whole and part. Each moment is whole and part.
This is holonic organization applied to memory and meaning.
Koestler observed that holarchies have several key properties:
Autonomy with integration: Each level maintains its own integrity while participating in larger wholes
Recursive structure: The whole-part relationship repeats at every scale
Emergent properties: Each level exhibits qualities that cannot be reduced to its components
Bidirectional influence: Wholes constrain parts; parts compose wholes
These same properties should characterize any knowledge representation system that aims to capture how humans actually think, remember, and reason.
Why Traditional Graphs Fall Short
Standard property graphs, including RDF and most knowledge graph implementations, impose a fundamental limitation: relationships exist only between pairs of nodes. This binary constraint forces us to flatten naturally holonic structures.
Consider modeling a medical fact: “Male hypertensive patients with serum creatinine levels between 115–133 μmol/L show mild elevation.” In a traditional graph, you must decompose this n-ary relationship into multiple binary edges, losing the semantic unity of the fact. You create separate triples: (Patient, hasCondition, Hypertension), (Patient, hasGender, Male), (Patient, hasLab, CreatinineLevel), creating an artificial fragmentation of what is conceptually a single holonic fact.
The medical knowledge is a whole — a complete diagnostic pattern. But it’s also composed of parts — patient characteristics, lab values, clinical interpretations. And those parts are themselves wholes — each lab value has meaning in isolation, yet also participates in the diagnostic whole.
Binary graphs cannot represent this holonic structure without semantic loss.
Enter Hypergraphs: A Step Toward Holons
Hypergraphs move beyond binary relations by allowing hyperedges — connections that link multiple nodes simultaneously. A hyperedge can connect three, five, or twenty nodes in a single relationship. For our medical example, a single hyperedge could contain {Male, Hypertension, CreatinineRange, MildElevation}, preserving the unity of the diagnostic fact.
This is progress. Hypergraphs capture some holonic properties by representing relationships as wholes rather than fragmenting them into binary parts. A project team can be modeled as a hyperedge connecting all members — the team as a unit, not as a collection of pairwise connections.
But hypergraphs still fall short of true holonic representation. They cannot model hierarchy or nesting. A hyperedge in a hypergraph cannot itself contain other hyperedges. There’s no way to represent that the project team is part of a department, which is part of a division, which is part of an organization, with each level maintaining both autonomy and participation in the larger structure.
As one researcher notes, hypergraphs “do not allow implementing the emergence principle” — they lack the recursive depth that characterizes holonic systems.
Metagraphs: The Holonic Graph Structure
Metagraphs solve this limitation through a radical flexibility: edges can themselves be graphs. A metagraph allows nested, hierarchical structures where any element can simultaneously be a node in one context and contain an entire graph structure in another context.
Consider what this means architecturally:
In a metagraph, a relationship can contain subrelationships. The edge connecting “Team” to “Project” might itself contain a graph showing the temporal evolution of that relationship — meetings, decisions, conflicts, resolutions. The relationship is both an atomic connection (in the outer graph) and a complex process (in its internal structure).
In a metagraph, a concept can expand into its constituent structure. The node “Cell” might contain an entire graph of organelles, molecules, and biochemical processes. When reasoning at the tissue level, “Cell” is a simple node. When reasoning at the cellular level, that same element opens into its full complexity.
In a metagraph, context becomes structurally explicit. Nested metavertices can represent different situations, different time periods, different perspectives — all referencing the same underlying elements but organizing them into distinct holonic wholes.
This is precisely Koestler’s holarchy made computational. Each metavertex in a metagraph exhibits the properties of a holon:
Wholeness: The metavertex has its own identity, can be reasoned about as a unit
Partness: It contains elements (nodes, edges, sub-graphs) that compose it
Autonomy: It can be manipulated independently
Integration: It participates in larger graph structures
The researchers who formalized metagraphs explicitly recognize this, noting “the holonic nature of the metagraph structure” where metavertices can contain vertices and edges, and those elements can themselves be metavertices containing deeper structures.
Practical Implications for AI Memory Systems
This architectural insight has profound implications for building AI agents with sophisticated memory systems.
Context Preservation Through Nesting
Human memory is fundamentally contextual. We don’t retrieve isolated facts — we retrieve facts embedded in their contexts. When you remember where you parked your car, you’re not just accessing a location; you’re accessing it nested in “this morning,” nested in “this particular parking garage,” nested in “the day I was running late.”
Metagraphs enable this through structural nesting. A memory of an event can be represented as a metavertex containing all the constituent elements, which itself is contained in a larger temporal context, which itself is part of a biographical structure. The nesting preserves context at every level while allowing each level to be reasoned about independently.
Relationship Reification Without Semantic Loss
In traditional graphs, making relationships first-class entities (reification) requires creating new nodes to represent them, leading to proliferation of auxiliary structures. In metagraphs, relationships naturally have internal structure — they’re graphs themselves. A “teaches” relationship between a professor and a course can contain the entire context: semester, location, student list, curriculum, all nested within the relationship itself.
This solves the RDF reification problem elegantly by recognizing that relationships are themselves holonic — they’re both atomic connections and complex structures.
Multi-Scale Reasoning
Different reasoning tasks require different scales of abstraction. When an AI agent is planning a route, it reasons about cities as atomic points. When the agent arrives in a city, that same entity opens into neighborhoods, streets, buildings — each with their own nested structure.
Metagraphs support this through their holonic architecture. The same element can be a simple node at one scale and an expanded graph at another. The system can fluidly move between scales, zooming in and out, while maintaining consistency because the nested structures are explicit parts of the model.
Emergent Properties at Each Level
Koestler emphasized that holarchies exhibit emergence — each level has properties not present in its components. A cell has properties that molecules don’t have. An organ has properties that cells don’t have.
Similarly, in metagraphs modeling knowledge, each level of nesting can have semantic properties that emerge from but are not reducible to its components. A “friendship” might contain dozens of interactions, but the friendship as a whole has qualities — trust, depth, history — that are properties of the holonic whole, not just aggregations of the parts.
This enables AI systems to reason about complex social, organizational, or conceptual structures at the appropriate level of abstraction while maintaining the ability to drill down when needed.
Holonic Design for Agentic AI Systems
The principles of holonic architecture become particularly powerful when applied to agentic AI — systems where autonomous agents collaborate to accomplish complex tasks. Here, the Smalltalk lesson about interface unification reveals its full potential.
Agents as Holons
Imagine an AI system where you can interact with:
An atomic agent that’s just a simple wrapper around an LLM, providing a specific capability like text summarization
A specialized agent that uses multiple tools and has complex internal state
A multi-agent system where dozens of specialized agents coordinate through elaborate protocols
A hierarchical organization of agent teams, each with its own leadership and delegation structures
In a holonic architecture, all of these present the same interface. When you, as a user or calling system, want something done, you don’t need to know whether you’re talking to a single LLM wrapper or a massive multiplayer agent system. You send a request. You receive a response. The interface is identical.
This is revolutionary for several reasons:
Composability: Agents can seamlessly incorporate other agents. A task planning agent might use a research agent, which itself coordinates multiple specialized search agents, which each wrap different LLMs or tools. From the task planner’s perspective, it’s just calling an agent. The recursive delegation is transparent.
Scalability: Systems can grow from simple to complex without interface changes. Start with a single agent handling customer support. As needs grow, that single agent becomes a front-end for a team of specialized agents — sales questions go to one subsystem, technical issues to another, each subsystem itself potentially composed of multiple agents. External systems don’t need to change how they interact.
Fault Tolerance: If a complex multi-agent subsystem fails, you can replace it with a simpler single-agent implementation as a fallback. Same interface, degraded capability but maintained functionality.
Development Workflow: You can prototype with simple agents and gradually replace them with sophisticated multi-agent systems as you discover where complexity is needed. The interface stability means other parts of your system don’t break during this evolution.
The Message Passing Paradigm
Just as Smalltalk objects communicate through messages, holonic agent systems benefit from message-passing architectures. An agent receives a request, processes it (possibly by delegating to sub-agents), and returns a response. The internal complexity — whether it’s a single function call or a multi-hour coordination process involving dozens of agents — is hidden behind this simple send-receive pattern.
This maps naturally to modern async programming patterns, event-driven architectures, and even biological nervous systems. The neuron receiving a signal doesn’t care whether the source is a single sensory receptor or a complex processing network in the visual cortex. The signal arrives, processing occurs, and output propagates. Holonic agents work the same way.
Emergent Collaboration
When agents are holonic — when complex agent systems can be treated as simple agents — you get emergent collaboration patterns. An agent designed to manage projects might discover it needs legal review. It simply sends that component to a “legal agent,” which from its perspective is atomic. But that legal agent might be a sophisticated system that routes different types of questions to specialized sub-agents, maintains case law databases, consults external APIs, and synthesizes responses through multi-round deliberation.
The project management agent doesn’t need to know any of this. It just knows it sends legal questions and gets back legal analyses. The interface consistency allows these systems to compose without tight coupling, enabling organic growth of capability.
Temporal Holons: Time as Nested Structure
One of the most fascinating applications of holonic thinking to knowledge representation involves time and events. Our experience of time is inherently holonic, yet traditional knowledge graphs struggle to represent temporal structure beyond simple timestamps.
Events as Holons
Consider how we actually experience and remember time:
A moment is a whole experience (the instant the sun broke through the clouds)
But it’s part of a larger event (the afternoon hike)
Which is part of a day (Tuesday)
Which is part of a vacation (the week in Scotland)
Which is part of a life chapter (my thirties)
Which is part of a biography (my life)
Each of these temporal units is simultaneously:
Complete in itself: The moment has its own qualities — surprise, beauty, emotional valence
Part of larger wholes: It derives meaning from being this moment in that hike on that day during that vacation
Composed of smaller parts: Even a “moment” contains sensory impressions, thoughts, the before and after that give it temporal extension
This is temporal holarchy — time organized not as a flat sequence of timestamps but as nested contexts where each level maintains autonomy while participating in larger temporal structures.
Modeling Temporal Holons with Metagraphs
Here’s where metagraphs show their power for temporal modeling. In a metagraph:
A time point can be a simple node (timestamp: 2024–03–15T14:32:00Z) when you’re reasoning about sequences and ordering. But zoom in, and that same node opens into a metavertex containing:
Sensory data from that moment
Thoughts and internal state
The immediate temporal context (what came right before and after)
Emotional qualities of the experience
Connections to other memories triggered at that moment
An event is a metavertex that contains:
Its constituent time points
The relationships between those points
The narrative arc that makes it a coherent event rather than random moments
Its boundaries (how it began and ended)
Its emergent properties (the meaning of the event beyond its parts)
A memory is a higher-level metavertex that might contain:
Multiple related events
The temporal relationships between them
The thematic connections that make these events part of a single remembered experience
How this memory relates to identity, learning, and future planning
What makes this holonic is that the interface remains consistent. Whether you’re accessing a single moment, an event, or a complex life period, you’re working with temporal entities that have similar properties — they have durations, they can be queried, they can be related to other temporal entities, they have content. The complexity scales, but the fundamental interface doesn’t.
DAG-like Temporal Structures
In practice, temporal holons often form DAG-like (Directed Acyclic Graph) structures rather than simple trees. A single moment might participate in multiple overlapping events. Tuesday afternoon might be part of:
“The Scottish vacation”
“The period when I was thinking about career changes”
“Times when I felt close to nature”
“Experiences that influenced my book”
Each of these is a different temporal holon, a different way of organizing time into meaningful nested structure. The same atomic moments participate in multiple holarchies simultaneously.
This is where metagraphs become essential. A moment isn’t just in one metavertex — it can be a component of multiple metavertices, each representing a different temporal organization. The same structural elements (time points, sensory data, thoughts) participate in different higher-order structures depending on which temporal dimension you’re querying.
Meta-DAGs and Temporal Composition
Taking this further, we can think of temporal structures as forming “meta-DAGs” — directed acyclic graphs where the nodes can themselves be graphs. This sounds abstract, but it captures something fundamental about how memory and experience actually work.
A vacation is a meta-node in your life’s temporal graph. Zoom out, and it’s just a node — “Scotland trip, March 2024.” Zoom in, and it unfolds into a complex DAG of days, events, moments, each with their own internal structure, each participating in multiple overlapping temporal narratives.
This nested DAG structure means:
Efficient compression: You don’t need to store every detail at every level. The vacation-level representation captures high-level features. The day-level adds detail. The moment-level is maximally granular. Query at the appropriate level for your needs.
Multiple temporal projections: The same underlying moments can be reorganized into different temporal structures — chronological, thematic, emotional, causal — without duplicating data.
Emergent temporal properties: The vacation has properties (restfulness, adventure, transformation) that aren’t simply aggregations of daily properties. These emerge from the composition and are properties of the holonic whole.
The Universal Interface of Temporal Entities
Just as agents benefit from interface consistency, temporal entities benefit from presenting similar interfaces across scales. Whether you’re querying:
“What happened at 14:32 on March 15?”
“What happened Tuesday afternoon?”
“What happened during the Scotland trip?”
“What happened in my thirties?”
You’re asking the same kind of question at different scales. The system can respond using the same query patterns, the same reasoning mechanisms, the same relationship types — just operating at different levels of the temporal holarchy.
This interface unification makes temporal reasoning tractable. You don’t need separate query languages, separate storage mechanisms, separate reasoning engines for moments, events, periods, and life chapters. The holonic structure means the same primitives work at every scale.
The Fractal Nature of Holonic Design
There’s a deep connection between holonic structures and fractals that’s worth making explicit. When people describe holons as “fractal,” they’re pointing to the self-similarity across scales — the same patterns, the same interfaces, the same organizing principles recurring at every level.
But there’s a crucial distinction: fractals typically exhibit mathematical self-similarity where the structure at each scale is literally the same. The Mandelbrot set looks identical whether you zoom in or out. A coastline’s jaggedness follows the same statistical pattern at every resolution.
Holons exhibit functional self-similarity. The interface is similar, the organizing principles are similar, but each level has its own character, its own emergent properties, its own unique features. A cell and an organism both maintain boundaries, process inputs, produce outputs, adapt to environments — the functional pattern recurs. But a cell isn’t just a miniature organism, and an organism isn’t just a macro-cell. Each level brings something new.
This functional fractality is precisely what makes holonic design so powerful for software and AI systems:
Same Patterns, Different Scales: A function, an object, a module, a service, an application — all can follow similar patterns of receiving inputs, processing, and producing outputs. But each scale brings different concerns: functions worry about type signatures, services worry about network protocols, applications worry about user experience.
Recursive Composition Without Loss: Unlike mathematical fractals where infinite recursion produces identical structure, holonic systems allow unlimited recursion while accumulating complexity and capability. Each level of composition adds value. An agent containing agents containing agents isn’t just the same thing at different scales — it’s genuinely more capable.
Predictable but Not Repetitive: When systems follow holonic principles, developers can predict how things work at new scales because the patterns are familiar. But they’re not bored by repetition because each scale presents new challenges and possibilities.
Hiding Complexity, Revealing Structure
The genius of holonic design is how it manages complexity. Not by eliminating it — complex systems are inherently complex — but by organizing it so that:
Local reasoning remains possible: You can understand and work with one level without needing to comprehend all levels simultaneously. The agent developer doesn’t need to understand the LLM internals. The LLM developer doesn’t need to understand the multi-agent coordination protocols.
Global properties emerge naturally: When local pieces follow holonic principles, system-wide properties like scalability, fault tolerance, and composability emerge without requiring global coordination.
Interfaces remain stable across scale: As systems grow from simple to complex, the ways you interact with them don’t fundamentally change. This stability is what allows systems to evolve without breaking everything that depends on them.
This is the practical power of holons — not as philosophical abstraction but as design principle. Whether you’re building object systems, agent architectures, or knowledge graphs, thinking holonically means thinking about how parts and wholes relate, how interfaces unify across scales, and how complexity can nest without becoming incomprehensible.
Implementation Challenges and Pragmatic Compromises
The theoretical elegance of metagraphs faces a practical constraint: there is currently no mature, scalable graph database technology that fully implements metagraph semantics. Most graph databases (Neo4j, TigerGraph, Neptune) are built around property graphs with binary edges. Even hypergraph databases like HypergraphDB don’t support the full nesting and emergence properties of metagraphs.
This creates a dilemma for anyone building real systems today. We understand that holonic structures represented through metagraphs offer the most powerful and semantically rich knowledge representation, but the tooling doesn’t exist to implement them at scale.
Several pragmatic compromises have emerged:
Meta Nodes as Relationship Containers
Instead of true hyperedges, we create specialized nodes that represent complex relationships. These “meta nodes” connect to all entities involved in the relationship. This approximates hypergraphs within property graph constraints, capturing n-ary relationships while using existing database technology.
Taking this further, we can represent all relationships — even binary ones — as nodes. This creates a uniform structure where relationships are first-class entities that can have properties, participate in other relationships, and be nested within larger structures. While not a true metagraph, this node-centric approach captures much of the flexibility needed for holonic knowledge representation.
Named Graphs for Hierarchy
The RDF community developed “named graphs” to group sets of triples into subgraphs. These named subgraphs can themselves be treated as nodes, referenced and reasoned about as units. This introduces hierarchy and nesting while maintaining compatibility with RDF tooling.
A named graph is effectively a metavertex — it’s a complete subgraph that can be treated as a single entity in a larger graph. By carefully structuring named graphs, we can approximate holonic organization where subgraphs at one level become nodes at the next level up.
Hybrid Semantic Approaches
The most pragmatic path may be a hybrid architecture where:
The database layer uses augmented property graphs (treating relationships as nodes where needed)
An ontology layer captures the semantic holonic structure
Application logic interprets this flattened structure through a holonic lens
Query and reasoning systems understand the nesting relationships even though they’re represented through indirection
This sacrifices some elegance but gains implementability. The holonic semantics exist in how we interpret the structure, not in the database primitives themselves.
The Path Forward
As AI systems become more sophisticated in their reasoning and memory capabilities, the limitations of binary knowledge graphs will become increasingly apparent. Systems that need to understand context, handle nested temporal structures, reason across multiple scales, and maintain semantic coherence in the face of complexity will require holonic knowledge representation.
Metagraphs provide the formal framework for such representation — a way to encode Koestler’s holarchy into computational structures. The fact that current database technology doesn’t fully support metagraphs is a temporary constraint, not a fundamental limitation.
Several directions point toward bridging this gap:
Advanced graph databases built from the ground up to support metavertices, nested structures, and emergence properties. These would treat the holon — the whole-part structure — as the fundamental unit rather than the binary edge.
Specialized memory systems for AI agents that implement metagraph semantics at the application layer, using whatever database primitives are available but imposing holonic structure through careful design.
Ontology-driven frameworks that use existing tools like ProtoScript or RDF++ to encode holonic relationships while maintaining compatibility with current graph technologies.
Hybrid architectures that recognize different knowledge domains require different structures: using simple property graphs for straightforward facts, hypergraphs for complex n-ary relationships, and metagraph patterns for deeply nested contextual knowledge.
Conclusion: From Atoms to Organisms, One Interface
Koestler’s insight that nature organizes itself into holarchies — nested levels where each element is simultaneously whole and part — applies equally to knowledge, meaning, memory, time, and agency. As we build AI systems that must reason about the world with human-like flexibility and depth, we need representational structures that mirror this holonic organization.
The power of holonic thinking lies not just in its philosophical elegance but in its practical implications:
For data structures: Metagraphs provide the technical framework for encoding holarchy computationally. By allowing edges to contain graphs, by enabling recursive nesting, by making context structurally explicit, metagraphs solve the limitations of binary graphs and even hypergraphs. They recognize that relationships, concepts, and contexts are themselves complex structures that exist at multiple scales simultaneously.
For agent systems: Holonic architecture creates universal interfaces that hide complexity while revealing capability. Whether you’re working with a single LLM wrapper or a sophisticated multi-agent system becomes irrelevant from the interface perspective. This enables agents to compose recursively, systems to scale organically, and complexity to grow without brittleness.
For temporal representation: Treating time as nested structure — where moments compose into events, events into experiences, experiences into life chapters — gives us a way to model memory and temporality that matches how humans actually experience duration. The same interface works whether you’re querying a moment or a lifetime.
For system design generally: The holonic principle of “same interface, different scales” provides a design philosophy that balances local autonomy with global coherence. Parts can be understood in isolation, yet they naturally compose into wholes that have emergent properties beyond their components.
The technical challenge of implementing these ideas at scale remains real. Current graph databases don’t fully support metagraph semantics. Multi-agent coordination still struggles with complexity at scale. Temporal reasoning in knowledge graphs remains primitive. These are engineering problems, not fundamental limitations.
What’s exciting is recognizing that these challenges are related — they’re all instances of the same underlying need to represent holonic structure computationally. Solutions in one domain inform solutions in others:
The meta-node patterns developed for approximating metagraphs also work for agent composition
The named graph techniques for nesting RDF structures apply equally to temporal organization
The message-passing interfaces that work in agent systems mirror the query patterns that work for nested knowledge graphs
We’re discovering that holonic thinking isn’t just one design pattern among many. It’s a fundamental organizing principle that appears wherever we build complex systems that need to maintain coherence across scales. From Smalltalk objects to multi-agent AI, from metagraph knowledge representation to temporal memory structures, the same principle recurs: make parts and wholes indistinguishable from the interface perspective.
This isn’t about making everything the same. It’s about recognizing that the relationship between part and whole — the holonic relationship — is so fundamental that our computational structures should encode it directly. When they do, we get systems that are more composable, more scalable, more maintainable, and more aligned with how complex phenomena actually organize themselves in the world.
The ghost in the machine, as Koestler might say, is holarchy all the way down. From the simplest atomic agent to the most complex multi-agent organism, from a single moment to a lifetime of experience, from a fact to a knowledge graph — when we design with holonic principles, we’re not imposing artificial structure. We’re recognizing structure that’s already there, waiting to be encoded.
And metagraphs are how we encode that ghost in silicon.
Practical Takeaways
For those building systems today, the holonic principle suggests several concrete practices:
Design for interface consistency across scales: When building agents, services, or knowledge structures, think about how the interface at one scale should mirror the interface at other scales. This enables composition and reduces cognitive load.
Use meta-nodes to approximate metagraphs: Until native metagraph databases exist, represent complex relationships as specialized nodes that can participate in other relationships. This captures much of the power of true metagraphs with existing technology.
Make temporal structure explicit: Don’t just timestamp events. Model time as nested contexts where moments compose into larger temporal units, each maintaining its own identity while participating in larger temporal narratives.
Build agents that contain agents: Design agent systems where the atomic agent and the multi-agent system present identical interfaces. This enables recursive composition and graceful degradation.
Think in holons when designing APIs: Ask whether your API could work equally well for a simple implementation and a complex system. If not, you’re probably not being holonic enough.
Embrace functional fractality: The same patterns should recur at different scales, but allow each scale to bring its own unique character and capabilities. Self-similarity in principle, uniqueness in detail.
The holonic revolution in computing isn’t about adopting a specific technology. It’s about recognizing a principle that makes complex systems tractable: when parts and wholes share interfaces, when composition preserves patterns, when scale changes don’t require fundamental rethinking — that’s when systems become truly powerful.