Active Inference & The Spatial Web

Web 3.0 | Intelligent Agents | XR Smart Technology

The Future of Agent Communication: From Protocol Proliferation to Spatial Web Convergence

ByDenise Holt

April 21, 2025

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Why today’s agent protocols matter — and why they’ll need the Spatial Web to survive what’s coming next

The Age of Intelligent Agents Has a Protocol Problem

The rise of autonomous intelligent agents has triggered a new frontier in digital coordination. No longer confined to reactive chatbots or standalone systems, these agents are now being designed to reason, plan, and collaborate — across tasks, domains, and even organizations. But as this vision becomes reality, one challenge is quickly surfacing: how do agents reliably talk to each other, share context, and make decisions together in a way that is secure, scalable, and interoperable?

A new class of communication protocols has emerged in response. From Anthropic’s Model Context Protocol (MCP) to collaborative standards like IBM and Cisco’s Agent Communication Protocol (ACP), and from Google’s cross-vendor Agent-to-Agent Protocol (A2A) to the decentralized ethos of the Agent Networking Protocol (ANP), these protocols are racing to define how agents will coordinate in the age of AI.

Each of these approaches brings unique strengths. They are not theoretical. They are being implemented, piloted, and standardized today — helping developers solve urgent problems and enabling the first wave of autonomous systems to operate in production environments. Their contributions to interoperability, context sharing, and secure communication are not just valuable; they are essential at this moment in time.

But a larger shift is underway.

Parallel to these protocol efforts, the Spatial Web is rising — a radically unified infrastructure designed to contextualize everything, from people and places to devices and data. At its core are the Hyperspace Modeling Language (HSML) and the Hyperspace Transaction Protocol (HSTP), which enable a globally consistent framework for describing, discovering, and interacting with entities across the physical and digital realms. This framework is already becoming an IEEE global standard. And it doesn’t just accommodate agents — it redefines the terrain they operate in.

This raises a pressing question:

As the Spatial Web’s Universal Domain Graph begins to take shape, will today’s agent communication protocols evolve to align with it — or will they eventually become redundant?

This article explores that intersection. We’ll look at what each of these protocols does well, how they differ in architecture and scope, and why their current utility may give way to a new kind of interoperability. A convergence is not only likely — it’s necessary. And the future of agent communication may depend on how well these protocols fold into a spatial, semantic, and context-aware internet that’s already beginning to form around them.

Agent Protocols at a Glance: Bridging the Gaps in Today's Intelligent Systems

The emergence of intelligent agents has demanded new infrastructure for communication. Each of the current protocols — MCP, ACP, A2A, and ANP — was created to address specific pain points in coordinating tasks, data, and intent across independent systems. They reflect the needs of machine learning-based agents operating in today’s world of web APIs, databases, and cloud services.

But importantly, they also reflect a legacy of centralized computing and static data models. These protocols were not designed with the principles of Active Inference in mind — principles that favor real-time perception-action loops, causal modeling, and dynamic adaptation to unfolding environments. As we explore each protocol, it becomes clear that while they are solving today’s problems, they may not be structurally suited for the agents of tomorrow.

Model Context Protocol (MCP): Structured Context Injection for Machine Learning Models

The Model Context Protocol (MCP) provides a standardized way for large language models or agent frameworks to retrieve structured context — like files, APIs, or system tools — from an external source. It acts as a content feed that simplifies how a model pulls in relevant data.

Why it’s useful now:

Makes tools and knowledge accessible to language models like GPT or Claude
Reduces the need for hardcoded integrations or custom pipelines
Enables developers to plug in “live” information to otherwise static models

But here’s the limitation:

MCP agents don’t reason about the world — they pull in data, process it, and output a result based on prior training and prompted instructions.
The agent’s understanding is contextually injected, not self-modeled.

For Active Inference Agents, which build internal generative models of their environments and act to minimize prediction error, this kind of tethered, pull-based architecture is inherently limiting. Active Inference doesn’t just fetch context — it continuously infers it, updates it, and acts upon it in real time. The MCP model isn’t designed to support that cycle.

Agent Communication Protocol (ACP): Structured Collaboration in Controlled Environments

Developed by IBM and Cisco, the Agent Communication Protocol was built to facilitate structured, persistent collaboration between agents in enterprise environments. It supports stateful threads of communication and allows agents to negotiate and reason together across tasks.

Strengths today:

Excellent for multi-agent workflows in businesses or cloud-based agent ecosystems
Uses standardized APIs and JSON schemas
Designed to scale collaborative efforts among ML agents using planning logic

But looking forward:
ACP is still fundamentally centralized. Discovery and coordination require a central directory, and context is carried in shared messages — not modeled internally. Agents are often specialized by function but lack general situational awareness. While it brings a collaborative layer to ML-based agents, it doesn’t match the situated, context-aware reasoning that defines Active Inference.

In an Active Inference architecture, agents don’t just cooperate — they co-regulate. They exchange beliefs, align internal models, and adapt as they act. ACP enables inter-agent chat; Active Inference enables shared intentionality and dynamic adaptation.

Agent-to-Agent Protocol (A2A): Cross-Vendor Interoperability for Enterprise ML Agents

Google’s A2A protocol introduces a modular, open format for agents from different vendors to coordinate using shared task objects and semantic descriptors. Agents can discover each other, delegate tasks, and return results in structured formats.

Why it’s gaining traction:

Solves real interoperability pain between tools like CRMs, HR systems, or productivity agents
Well-supported by major enterprise vendors
Built on familiar standards (HTTP, JSON, OAuth)

Where it falls short for the future:

A2A is optimized for task exchange, not cognitive modeling. Tasks are passed, performed, and returned — but there is no real-world grounding or embodied understanding. Agents operate like modular services in a cloud-native workflow.

Active Inference changes that paradigm. With RGMs (Renormalizing Generative Models), agents develop hierarchical, scale-free internal models of the environment. They simulate future states, update their beliefs, and reason over multiple spatial and temporal layers. A2A has no language for that kind of modeling. It enables interoperability — yes — but not inference.

Agent Networking Protocol (ANP): A Decentralized Vision Closer to What's Coming

Of all the current agent protocols, the Agent Networking Protocol (ANP) comes closest to aligning with the demands of Active Inference and the Spatial Web. Built on decentralized identifiers (DIDs) and JSON-LD linked data, ANP allows agents to describe themselves semantically, discover each other globally, and communicate peer-to-peer.

Why it matters:

Decentralized, identity-secure communication
Semantic modeling using linked data
Discovery via open registries or search-indexed agent descriptions

Its relevance to Active Inference:
ANP supports autonomous discovery, decentralized identity, and semantic reasoning — key ingredients for Active Inference Agents operating in real-world contexts. While ANP does not currently support predictive or hierarchical inference architectures like RGMs, its infrastructure could provide the transport and discovery layer for such agents.

But again, ANP lacks the shared global context and transactional knowledge graph that the Spatial Web provides. It connects agents, but not their environment. That’s where the Spatial Web begins to take over.

The Spatial Web Paradigm: Building the World Agents Can Think Inside

While today’s agent protocols are constructing bridges between individual agents and their tools, the Spatial Web is laying the groundwork for something much larger — a shared, context-rich world where agents, devices, people, and environments are all part of a unified semantic and spatial fabric.

This is not just an upgrade to the Web. It’s a transformation in how data, identity, location, time, and intent are understood across systems. Where traditional web protocols serve pages and resources, Spatial Web protocols serve meaning and context. And that changes everything for agents — especially those powered by Active Inference.

HSML and HSTP: A Common Language and a Global Contract Layer

At the heart of the Spatial Web are two foundational protocols:

HSML (Hyperspace Modeling Language): A semantic modeling language that lets any entity — person, device, location, organization, or AI agent — describe itself and its relationships in a machine-readable format. Think of it as the ontology backbone of the Spatial Web, enabling shared understanding and automated reasoning.
HSTP (Hyperspace Transaction Protocol): A multi-dimensional communication and transaction protocol that allows entities to interact with each other — querying, updating, and committing changes to a distributed, secure graph of knowledge and state.

Together, these protocols support the emergence of the Universal Domain Graph (UDG) — a decentralized, permissioned, and constantly evolving model of everything. Not just information, but real-time, spatially anchored representations of the physical world.

In this paradigm, context is not fetched — it is embedded. Entities don’t just reference data; they are part of the global graph. Every agent, location, object, and event can be described, queried, and updated in a universally consistent way.

Why This Matters for Agents

For agents, particularly those based on machine learning, the Spatial Web offers a richer operating environment: one where they can locate themselves, interpret relationships, and respond to unfolding changes without relying solely on private databases or hardcoded integrations.

But for Active Inference Agents, the Spatial Web is something more profound.

Active Inference Agents operate on the principle of minimizing uncertainty about the world by continuously updating internal generative models based on incoming data. They don’t merely perform tasks — they infer the hidden causes of their observations and act in the world to fulfill expectations while adjusting their beliefs.

This requires a deeply contextual understanding of space, time, causality, and intent.

The Spatial Web delivers exactly that. It provides:

Spatio-temporal grounding: Every entity is located in time and space, enabling agents to align their models with real-world dynamics.
Semantic consistency: HSML ensures that “room,” “temperature,” “battery level,” or “flight path” mean the same thing across domains.
Contextual granularity: Agents can navigate the Universal Domain Graph to retrieve just the data they need — at the right level of abstraction and scale.
Permissioned access and trust frameworks: Crucial for real-world applications, agents can access context securely and act within regulated boundaries.

In short, the Spatial Web provides the shared external memory, sensory input, and environmental structure Active Inference Agents need to operate effectively at scale.

From API Calls to Adaptive Worlds

If today’s agent protocols represent the tools and roads agents use to communicate and cooperate, then the Spatial Web is the terrain they exist within. It offers not just a medium of interaction — but a model of the world itself, accessible in real time.

And that model isn’t static. It evolves as agents act. When an agent books a room, delivers a package, or completes a manufacturing step, the Universal Domain Graph updates to reflect that reality. This feedback loop between agent and environment is central to Active Inference, and it is what makes the Spatial Web a uniquely compatible architecture for the next era of intelligent systems.

Where legacy agents process tasks in isolation, Active Inference Agents embedded in the Spatial Web can act as contextual participants — constantly sensing, reasoning, and responding to a dynamic, multi-scale world.

Capability Comparisons: Protocols vs. the Spatial Web

While MCP, ACP, A2A, and ANP have each made strides in addressing today’s agent communication needs, they were born out of a specific context — machine learning agents, operating within the current structure of the World Wide Web. As we shift toward Active Inference Agents and distributed intelligence, that foundation starts to show its limitations.

The Spatial Web, in contrast, was designed from first principles to support contextual reasoning, environmental awareness, and interoperable semantics. Let’s break down how these two classes of protocols compare across key dimensions — and what those differences mean for the future of agent interaction.

1. Architecture: Point-to-Point vs. Shared Context

Agent Protocols (MCP, ACP, A2A, ANP) are fundamentally point-to-point in structure. They enable agents to discover each other, negotiate tasks, and share messages directly. In most cases, each agent manages its own local state and context.
The Spatial Web takes a graph-based approach. Context, state, and interactions are externalized into the Universal Domain Graph (UDG) — a distributed, permissioned map of all relevant entities and relationships. Agents don’t pass context around; they live inside it.

Why it matters for Active Inference:
Active Inference Agents depend on continuously updated internal models that are aligned with the external world. The Spatial Web supports this by maintaining a persistent, structured reality they can pull from, update, and adapt to — without the need for each agent to reinvent context on its own.

Discovery and Identity: Isolated Registries vs. Global Semantics

MCP/ACP/A2A use registries or service descriptors (like Agent Cards) to advertise agent capabilities. Each protocol defines its own discovery method, often requiring a known directory or endpoint.
ANP goes further by enabling decentralized discovery through JSON-LD and DIDs, giving agents a form of self-sovereign identity and semantic visibility on the open web.
The Spatial Web, however, uses globally unique identifiers (akin to spatial URLs), linked through HSML models and structured according to ontologies. Entities are discoverable by meaning, location, time, function, or any combination.

Why it matters for Active Inference:
Agents that reason causally and infer hidden states need more than just “who to talk to” — they need to know what exists, where, when, and why it matters. Discovery via semantic graphs enables agents to build rich, multiscale world models — essential for high-level reasoning and decision-making.

State Management: Local Threads vs. Global Synchronization

ACP and A2A allow for message threads or long-lived tasks between agents, but the state is local to the conversation.
MCP supports context injection, but not persistent world-state synchronization.
The Spatial Web’s UDG becomes the single source of truth for all agents. Every entity, event, and state change is part of the global context — accessible and queryable by permissioned agents at any time.

Why it matters for Active Inference:

To minimize free energy and make accurate predictions, Active Inference Agents must align their internal models with an externally observable state. A common source of synchronized reality (like the UDG) allows agents to coordinate without needing custom logic to reconcile individual perspectives.

Semantic Interoperability: Message Schemas vs. Shared Ontologies

Most agent protocols define a schema (a JSON format for tasks, threads, etc.) that lets agents interpret each other’s messages — but these are syntactic, not semantic. The meaning of terms is left to implementation.
ANP and the Spatial Web take a semantic approach. Using JSON-LD, schema.org, and HSML, they ensure that a term like “location” or “battery status” refers to the same concept across systems.

Why it matters for Active Inference:
Active Inference relies on modeling causal relationships. That requires a shared understanding of what things are, not just what data looks like. Semantic consistency across agents and systems enables causal modeling, explanation, and emergent reasoning — core features of RGMs and scale-free inference systems.

Causal Reasoning and Adaptation: Fixed Workflows vs. Embodied Understanding

Today’s agent protocols treat agents as isolated services, reacting to tasks and triggering responses. They excel at chaining workflows and executing known patterns, but they lack mechanisms for continuous learning, model updating, or environmental inference.
Active Inference, by contrast, enables agents to simulate multiple futures, plan under uncertainty, and infer hidden variables based on context and change.
The Spatial Web enhances this by providing real-time, structured context that agents can reason about causally — bridging the gap between perception and action across a shared world model.

Why this is transformational:
This combination — Active Inference Agents embedded within the Spatial Web — ushers in a new era of autonomous intelligence. Agents don’t just cooperate; they learn, infer, and adapt in concert with their environment. They don’t just follow commands — they understand situations, simulate consequences, and act accordingly.

Collaboration or Redundancy? Navigating the Road Ahead

At a glance, the agent protocols emerging today — MCP, ACP, A2A, ANP — seem to be solving different problems than the Spatial Web. They manage task execution, data retrieval, peer messaging. The Spatial Web, by contrast, aims to model the entire digital and physical universe. But when we examine how these systems interact, and where technology is heading, a pattern begins to emerge:

These protocols are not competitors to the Spatial Web.

They are early bridges — temporary scaffolding across a digital landscape that is still taking form.

And that’s precisely why their long-term survival depends on how well they converge with or fold into the Spatial Web architecture.

A Necessary Layer — For Now

There’s no doubt that today’s agent protocols are doing important work:

MCP simplifies how machine learning agents access tools and data.
ACP introduces structured collaboration for enterprise agent ecosystems.
A2A solves the vendor lock-in problem by creating a shared task language.
ANP pushes forward a decentralized vision of agent identity and discovery.

These protocols are gaining traction not just because they’re useful — but because there is no shared substrate yet. Each protocol is a patch for a fractured internet: stitching together siloed APIs, services, and data under a common interface.

They are vital in a world where agents are still grounded in machine learning architectures that lack self-modeling, physical grounding, or native contextual awareness.

But Active Inference Is Changing the Game

As Active Inference AI becomes more widely adopted — particularly through frameworks like Renormalizing Generative Models (RGMs) — the assumptions baked into these agent protocols will start to show strain.

Current protocols treat agents as task performers; Active Inference Agents are model-based learners and decision-makers.
Current protocols pass messages and payloads; Active Inference Agents pass beliefs, priors, and predictions.
Current protocols expect agents to request context; Active Inference Agents maintain and update context continuously, using inference rather than instruction.

Most critically, Active Inference Agents don’t need to be tethered to central databases or cloud tools. They can operate on the edge — inferring, adapting, and acting autonomously. But to thrive, they need an environment where context is available as structure, not just as data.

That’s where the Spatial Web becomes indispensable.

The Spatial Web: From Protocol Stack to Contextual Substrate

The Spatial Web is not simply a next-generation protocol stack. It’s a paradigm shift in how agents interact — not just with each other, but with their entire environment. It collapses the artificial boundary between “agents” and “systems,” “tasks” and “states,” “requests” and “observations.”

This is especially powerful when coupled with Active Inference:

Active Inference provides the internal generative model;
The Spatial Web provides the external semantic and spatial context.

Together, they enable real-time, scale-free, causally grounded intelligence across distributed networks.

In this convergence, the Universal Domain Graph becomes the ambient knowledge base for all agents — no matter who built them. HSML becomes the language agents use to interpret their world. HSTP becomes the protocol they use to act upon it.

So What Happens to the Protocols?

Three paths emerge:

Collaboration (Short-Term):
Protocols like A2A and ACP continue to provide communication scaffolding — allowing agents to coordinate, even as some begin tapping into Spatial Web data for context. They serve as orchestration layers, while context lives in the UDG.

Convergence (Mid-Term):
As Spatial Web standards mature, protocols will begin to integrate or evolve to align with it:

A2A may adopt HSML for agent descriptions
ACP may use HSTP for secure graph transactions
ANP may function as a discovery layer for Spatial Web agents

Redundancy (Long-Term):
Eventually, many of the functions handled by these protocols — discovery, negotiation, task management — will likely be natively supported by the Spatial Web. When every entity is addressable, semantically described, and governed by a common protocol, many of today’s interoperability challenges disappear.

The historical precedent is clear:
Before HTTP and HTML, there were dozens of web protocols. But when a unified, open, and semantically rich system emerged, the rest either evolved — or faded away.

The Takeaway: Adapt or Be Outmoded

Agent protocols are currently instrumental. They’re carrying us through this transition. But their longevity will depend on how well they align with the deeper architecture of the Spatial Web and the deeper intelligence of Active Inference.

In the future, agents will not merely communicate. They will model, reason, and act — in real time, across networks, grounded in the same shared context as the rest of the intelligent infrastructure.

The protocols that recognize this shift and adapt accordingly will find their place.
The ones that don’t will, inevitably, be left behind.

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