Artificial Intelligence and blockchain technology represent two transformative forces reshaping our world. AI amplifies human cognitive capabilities through machine learning and neural networks, while blockchain technology introduces verifiable digital scarcity and enables novel forms of trustless coordination. As these technologies converge, they are laying the foundation for a new iteration of the internet—one where autonomous agents interact with decentralized systems. This “Agentic Web” introduces a new class of digital citizens: AI agents that can navigate, negotiate, and transact independently. This transformation redistributes power in the digital realm, enabling individuals to reclaim sovereignty over their data while fostering an ecosystem where human and artificial intelligence collaborate in unprecedented ways.

The Evolution of the Web

To understand where we’re heading, let’s first trace the evolution of the web through its major iterations, each marked by distinct capabilities and architectural paradigms:

While the first two generations of the web focused on information propagation, the latter two enable information augmentation. Web 3.0 introduced data ownership through tokens, and now Web 4.0 imbues intelligence through Large Language Models (LLMs).

From LLMs to Agents: A Natural Evolution

LLMs represent a quantum leap in machine intelligence, functioning as dynamic, pattern-matching systems that transform vast knowledge into contextual understanding through probabilistic computation. However, their true potential emerges when structured as agents—evolving from pure information processors into goal-directed entities that can perceive, reason, and act. This transformation creates an emergent intelligence capable of sustained, meaningful collaboration through both language and action.

The term “agent” introduces a new paradigm for human-AI interaction, moving beyond the limitations and negative associations of traditional chatbots. This shift isn’t merely semantic; it represents a fundamental reconceptualization of how AI systems can operate autonomously while maintaining meaningful collaboration with humans. Fundamentally, agentic workflows enable marketplaces to form around solving specific user intent.

Ultimately, the Agentic Web represents more than just a new layer of intelligence—it fundamentally transforms how we interact with digital systems. While previous web iterations relied on static interfaces and predefined user journeys, the Agentic Web introduces a dynamic runtime infrastructure where both computation and interfaces adapt in real-time to user context and intent.

Traditional websites serve as the atomic unit of today’s internet, providing fixed interfaces where users read, write, and interact with information through predetermined pathways. This model, while functional, constrains users to interfaces designed for general use cases rather than individual needs. The Agentic Web breaks free from these constraints through Context-Aware Computation, Adaptive Interface Generation, Predictive Action Flows unlocked through RAG and other innovations in real-time information retrieval.

Consider how TikTok revolutionized content consumption by creating highly personalized feeds that adapt to user preferences in real-time. The Agentic Web extends this concept beyond content recommendation to entire interface generation. Instead of navigating through fixed webpage layouts, users interact with dynamically generated interfaces that predict and facilitate their next actions. This shift from static websites to dynamic, agent-driven interfaces represents a fundamental evolution in how we interact with digital systems—moving from navigation-based to intent-based interaction models.

Anatomy of an Agent

Agentic architectures have been a huge exploration for researchers and builders alike. New methods are constantly being developed to enhance their reasoning and problem-solving capabilities. Techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT) are prime examples of innovations designed to improve how LLMs handle complex tasks by simulating more nuanced, human-like cognitive processes.

Chain-of-Thought (CoT) prompting encourages large language models (LLMs) to break down complex tasks into smaller, manageable steps. This approach is particularly effective for problems that require logical reasoning, such as writing short Python scripts or solving mathematical equations.

Tree-of-Thoughts (ToT) builds upon CoT by introducing a tree structure that allows for the exploration of multiple independent thought paths. This enhancement enables LLMs to tackle even more intricate tasks. In ToT, each “thought” (a text output from the LLM) is directly connected only to its immediately preceding or subsequent thought within a local chain (a tree branch). While this structure offers more flexibility than CoT, it still limits the potential for cross-pollination of ideas.

Graph-of-Thought (GoT) takes the concept further by fusing classic data structures with LLMs. This approach expands on ToT by allowing any “thought” to link to any other thought within a graph structure. This interconnected network of thoughts more closely mirrors human cognitive processes.

The graph structure of GoT likely provides a more accurate representation of human thinking compared to CoT or ToT in most scenarios. While there are instances where our thought patterns may resemble chains or trees (such as when developing contingency plans or standard operating procedures), these are exceptions rather than the norm. This model better mirrors human thinking, which often jumps across various thoughts rather than following a strict sequential order. While some scenarios, like developing contingency plans or standard procedures, might still follow a chain or tree-like structure, our minds typically create complex, interconnected webs of ideas that align more with the graph structure.

This graph-like approach in GoT allows for a more dynamic and flexible exploration of ideas, potentially leading to more creative and comprehensive problem-solving capabilities in LLMs.

These recursive graph based operations are only a step towards agentic workflows. The obvious next evolution is multiple agents with their own specialization being orchestrated towards specific goals. The beauty of agents is in their composition.

Agents let you to Modularize and Parallelize LLMs through multi-agent coordination.

Multi-Agent Systems

The concept of multi-agent systems is not a new one. Its roots trace back to Marvin Minsky’s “Society of Mind,” which proposed that multiple, modular minds working in collaboration can outperform a single, monolithic mind. ChatGPT and Claude are single agents. Mistral popularized Mixture of Experts. Extending this idea further, we believe a Network of Agents architecture to be the end state of this intelligence topology.

From a biomimicry point of view, unlike AI models, where billions of identical neurons are connected in uniform, predictable ways, the human brain (essentially a conscious machine) is incredibly heterogeneous—both at the organ and cellular level. Neurons communicate through intricate signals, involving neurotransmitter gradients, intracellular cascades, and various modulatory systems, making their function far more nuanced than simple binary states.

This suggests that in biology, intelligence doesn’t just stem from the sheer number of components or the size of a training dataset. Rather, it arises from the complex interplay between diverse, specialized units—an inherently analogue process.

For this reason, the notion of developing millions of smaller models rather than just a few large ones, and enabling orchestration among all these actors, more likely leads to innovations in cognitive architectures, something akin to a multi-agent systems.

Multi-agent system design offers several advantages over single-agent systems: it is more maintainable, easier to understand, and more flexible to extend. Even in cases where only a single-agent interface is needed, implementing it within a multi-agent framework can make the system more modular, simplifying the process for developers to add or remove components as needed. It’s essential to recognize that multi-agent architecture can be a highly effective way to build even a single-agent system.

While large language models (LLMs) have shown extraordinary capabilities—such as generating human-like text, solving complex problems, and handling a wide array of tasks—individual LLM agents face limitations that can hamper their effectiveness in real-world applications.

Below, we examine five key challenges associated with agentic systems and explore how multi-agent collaboration can overcome these hurdles, unlocking the full potential of LLMs.

• Overcoming Hallucinations through Cross-VerificationIndividual LLM agents often hallucinate, generating incorrect or nonsensical information. This happens despite their vast training, as outputs may appear plausible but lack factual accuracy. A multi-agent system allows agents to cross-verify information, reducing the risk of errors. By specializing in different areas, agents ensure more reliable and accurate responses.
• Extending Context Windows through Distributed ProcessingLLMs have limited context windows, making it difficult to manage lengthy documents or conversations. In a multi-agent framework, agents can divide the processing load, each handling a portion of the context. Through inter-agent communication, they can maintain coherence across the entire text, effectively extending the context window.
• Enhancing Efficiency through Parallel ProcessingIndividual LLMs typically process tasks one at a time, resulting in slower response times. Multi-agent systems support parallel processing, allowing multiple agents to work on different tasks simultaneously. This improves efficiency and speeds up response times, enabling businesses to handle multiple queries without delays.
• Fostering Collaboration for Complex Problem-SolvingLLMs alone struggle to solve complex problems that require diverse expertise. Multi-agent systems foster collaboration, with each agent contributing unique skills and perspectives. By working together, agents can tackle complex challenges more effectively, offering more comprehensive and innovative solutions.
• Increasing Accessibility through Resource OptimizationAdvanced LLMs demand significant computational resources, making them expensive and less accessible. Multi-agent frameworks optimize resource usage by distributing tasks among agents, lowering overall computational costs. This makes AI technologies more affordable and accessible to a wider range of organizations.

While multi-agent systems offer compelling advantages in distributed problem-solving and resource optimization, their true potential emerges when we consider their implementation at the network’s edge. As AI continues to evolve, the convergence of multi-agent architectures with edge computing creates a powerful synergy – enabling not just collaborative intelligence, but also localized, efficient processing across countless devices. This distributed approach to AI deployment naturally extends the benefits of multi-agent systems, bringing specialized, cooperative intelligence closer to where it’s needed most: the end user.

Intelligence at the Edge

The proliferation of AI across the digital landscape is driving a fundamental restructuring of computational architectures. As intelligence becomes woven into the fabric of our daily digital interactions, we’re witnessing a natural bifurcation of compute: specialized data centers handle complex reasoning and domain-specific tasks, while edge devices process personalized, context-sensitive queries locally. This shift toward edge inference isn’t merely an architectural preference—it’s a necessity driven by multiple critical factors.

First, the sheer volume of AI-driven interactions would overwhelm centralized inference providers, creating unsustainable bandwidth demands and latency issues.

Second, edge processing enables real-time responsiveness critical for applications like autonomous vehicles, augmented reality, and IoT devices.

Third, local inference preserves user privacy by keeping sensitive data on personal devices. Fourth, edge computing dramatically reduces energy consumption and carbon footprint by minimizing data movement across networks.

Finally, edge inference enables offline functionality and resilience, ensuring AI capabilities persist even when network connectivity is compromised.

This distributed intelligence paradigm represents not just an optimization of our current systems, but a fundamental reimagining of how we deploy and interact with AI in our increasingly connected world.

Furthermore, we’re witnessing a fundamental shift in the computational demands of LLMs. While the past decade has been dominated by the massive computational requirements of training large language models, we’re now entering an era where inference-time compute takes center stage. This transition is particularly evident in the emergence of agentic AI systems, as exemplified by OpenAI’s Q* breakthrough, which demonstrated how dynamic reasoning requires substantial real-time computational resources.

Unlike training-time compute, which is a one-time investment in model development, inference-time compute represents the ongoing computational dialogue necessary for autonomous agents to reason, plan, and adapt to novel situations. This shift from static model training to dynamic agent reasoning necessitates a radical rethinking of our computational infrastructure—one where edge computing becomes not just advantageous but essential.

As this transformation unfolds, we’re witnessing the emergence of peer-to-peer edge inference markets, where billions of connected devices—from smartphones to smart home systems—form dynamic computational meshes. These devices can seamlessly trade inference capacity, creating an organic marketplace where computational resources flow to where they’re needed most. The excess computational capacity of idle devices becomes a valuable resource, tradable in real-time, enabling a more efficient and resilient infrastructure than traditional centralized systems.

This democratization of inference compute not only optimizes resource utilization but also creates new economic opportunities within the digital ecosystem, where every connected device becomes a potential micro-provider of AI capabilities. The future of AI will thus be characterized not just by the power of individual models, but by the collective intelligence of interconnected edge devices forming a global, democratized inference marketplace, something akin to a spot market for verifiable inference based on supply and demand.

Agent Centric Interaction

LLMs now allow us to access vast amounts of information via conversation, instead of traditional browsing. This conversational approach will soon become more personalized and localized, as the internet transforms into a platform for AI agents rather than human users.

From the user’s perspective, the focus will shift from identifying the “best model” to getting the most personalized answers. The key to better answers lies in incorporating the user’s own data alongside general internet knowledge. Initially, larger context windows and retrieval-augmented generation (RAG) will help integrate personal data, but eventually, individual data will surpass general internet data in importance.

This leads to a future where we each have personal AI models interacting with the wider internet’s expert models. Initially, personalization will happen alongside remote models, but concerns over privacy and response speed will push more interaction onto local devices. This will create a new boundary—not between human and machine, but between our personal models and the internet’s expert models.

The traditional internet model of accessing raw data will become outdated. Instead, your local model will communicate with remote expert models to gather information, which it will process and present to you in the most personalized, high-bandwidth way possible. These personal models will become increasingly indispensable as they learn more about your preferences and habits.

The internet will transform into an ecosystem of interconnected models: local, high-context personal models and remote, high-knowledge expert models. This will involve new technologies like federated learning to update information between these models. As the machine economy evolves, we’ll have to reimagine the computational substate upon which this occurs, primarily in regards to compute, scalability, and payments. This leads to a reorganization of information space that is agent centric, sovereign, highly composable, self learning, and evolving.

Architectures for Agentic Protocols

In the Agentic Web, human-agent interaction evolves into a complex network of agent-to-agent communications. This architecture presents a fundamental reimagining of the internet’s structure, where sovereign agents become the primary interfaces for digital interaction. Below, we highlight core primitives required for Agentic Protocols.

Sovereign Identity

• Digital identity transitions from traditional IP addresses to cryptographic public-key pairs owned by agentic actors
• Blockchain-based namespace systems replace traditional DNS, eliminating central points of control
• Reputation systems track agent reliability and capability metrics
• Zero-knowledge proofs enable privacy-preserving identity verification
• Identity composability allows agents to manage multiple contexts and roles

Autonomous Agents

Self-directed entities capable of:Natural language understanding and intent resolution

Multi-step planning and task decomposition

Resource management and optimization

Learning from interactions and feedback

• Autonomous decision-making within defined parameters
• Agent specialization and marketplaces for specific capabilities
• Built-in safety mechanisms and alignment protocols

Data Infrastructure

• Real-time data ingestion and processing capabilities
• Distributed data verification and validation mechanisms

Hybrid systems combining:zkTLS

Traditional training datasets

Real-time web scraping and data synthesis

• Collaborative learning networks

RLHF (Reinforcement Learning from Human Feedback) networksDistributed feedback collection

Quality-weighted consensus mechanisms

• Dynamic model adjustment protocols

Compute Layer

Verifiable inference protocols ensuring:Computation integrity

Result reproducibility

Resource efficiency

• Decentralized compute infrastructure featuring:Peer-to-peer compute markets

Proof of computation systems

Dynamic resource allocation

• Edge computing integration

Model Ecosystem

Hierarchical model architecture:Task-specific SLMs (Small Language Models)

General-purpose LLMs

Specialized multi-modal models

• Multi-Modal LAMs (Large Action Models)
• Model composition and orchestration
• Continuous learning and adaptation capabilities
• Standardized model interfaces and protocols

Coordination Frameworks

• Cryptographic protocols for secure agent interactions
• Digital property rights management systems
• Economic incentive structures

Governance mechanisms for:Dispute resolution

Resource allocation

• Protocol updates

Parallel execution environments enabling:Concurrent task processing

Resource isolation

State management

• Conflict resolution

Agentic Markets

• Onchain primitives for Identity (Gnosis, Squad multisigs)
• Inter-agent economics and trade

Agent Owned LiquidityAgents own a portion of their token supply at genesis

• Aggregated inference markets paid via liquidity
• Onchain keys that control offchain accounts

Agents become yield bearing assetsAgentic DAOs

• Governance and dividends

Creating a Hyperstructure for Intelligence

Modern distributed systems design offer unique inspiration and primitives to enable an Agentic Protocols, specifically event driven architectures and more directly, the Actor Model of Compute.

The Actor Model provides an elegant theoretical foundation for implementing agentic systems. This computational model treats “actors” as the universal primitives of computation, where each actor can:

1. Process messages
2. Make local decisions
3. Create more actors
4. Send messages to other actors
5. Determine how to respond to the next message received

Key advantages of the Actor Model for agentic systems include:

• Isolation: Each actor operates independently, maintaining its own state and control flow
• Asynchronous Communication: Messages between actors are non-blocking, enabling efficient parallel processing
• Location Transparency: Actors can communicate regardless of their physical location in the network
• Fault Tolerance: System resilience through actor isolation and supervision hierarchies
• Scalability: Natural support for distributed systems and parallel computation

We propose Neuron, a practical implementation of this theoretical agentic protocol through a multi-layered distributed architecture combining blockchain namespaces, federated networks, CRDTs, and DHTs, with each layer serving distinct purposes in the protocol stack. We take inspiration from Urbit and Holochain, early pioneers in p2p OS design.

In Neuron, the blockchain layer provides verifiable namespaces and identity, enabling deterministic addressing and discovery of agents while maintaining cryptographic proofs of capabilities and reputation. Above this, a DHT layer facilitates efficient agent and node discovery alongside content routing with O(log n) lookup times, reducing on-chain operations while enabling locality-aware peer finding. State synchronization between federated nodes is handled through CRDTs, allowing agents and nodes to maintain consistent views of shared state without requiring global consensus for every interaction.

This architecture maps naturally to a federated network where autonomous agents operate as sovereign nodes living on devices with local edge inference implementing the Actor Model pattern. Federation domains can be organized by agent capabilities, with the DHT providing efficient routing and discovery within and across domains. Each agent functions as an independent actor with its own state, while the CRDT layer ensures eventual consistency across the federation. This multi-layered approach enables several key capabilities:

Decentralized Coordination

• Blockchain for verifiable identity and sovereign global namespace
• DHT for efficient peer and node discovery and content routing O(log n) lookup
• CRDTs for concurrent state synchronization and multi-agent coordination

Scalable Operations

• Zone-based federation topology
• Tiered storage strategy (hot/warm/cold)
• Locality-aware request routing
• Capability-based load distribution

System Resilience

• No single point of failure
• Continued operation during partitions
• Automatic state reconciliation
• Supervision hierarchies for fault tolerance

This implementation approach provides a robust foundation for building complex agentic systems while maintaining the key properties of sovereignty, scalability, and resilience required for effective agent-to-agent interactions.

Final Thoughts

The Agentic Web marks a pivotal evolution in human-computer interaction, transcending the sequential developments of previous eras to establish a fundamentally new paradigm of digital existence. Unlike previous iterations that simply changed how we consume or own information, the Agentic Web transforms the internet from a human-centric platform into an intelligent substrate where autonomous agents become the primary actors. This transformation is powered by the convergence of edge computing, large language models, and decentralized protocols, creating an ecosystem where personal AI models seamlessly interface with specialized expert systems.

As we move toward this agent-centric future, the boundaries between human and machine intelligence begin to blur, replaced by a symbiotic relationship where personalized AI agents serve as our digital extensions, understanding our context, anticipating our needs, and autonomously navigating the vast landscape of distributed intelligence. The Agentic Web thus represents not merely a technological advancement, but a fundamental reimagining of human potential in the digital age, where every interaction becomes an opportunity for augmented intelligence and every device becomes a node in a global network of collaborative AI systems.

Just as humanity navigates the physical dimensions of space and time, autonomous agents inhabit their own fundamental dimensions: blockspace for existence and inference-time for thought. This digital ontology mirrors our physical reality—where humans traverse distances and experience temporal flow, agents move through cryptographic proofs and computational cycles, creating a parallel universe of algorithmic existence.

It is inevitable that entities in latent space will operate on decentralized blockspace.

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