Depth Analysis of Intuition: How to Rebuild the Internet in the Era of AI Agents?

This report, written by Tiger Research, analyzes how Intuition achieves standard Consensus through atomically structured knowledge and Token Curated Registries (TCR), as well as a signal-based trust metric system, to rebuild network infrastructure in the era of AI agents.

Key Points Summary

• The era of AI agents has arrived. AI agents cannot fully realize their potential. The current network infrastructure is designed for humans. Websites use different data formats. Information is still unverified. This makes it difficult for agents to understand and process data.
• Intuition evolves the vision of the semantic web through Web3 methods. It addresses existing limitations. The system structures knowledge into Atoms. It uses Token Curated Registries (TCR) to reach consensus on data usage. Signals determine the level of trust in the data.
• Intuition will change the network. The current network is like an unpaved road. Intuition creates a highway where agents can operate safely. It will become the new infrastructure standard. This will realize the true potential of the agent AI era.

1. The era of intelligent entities begins: Is the network infrastructure sufficient?

The era of AI agents is thriving. We can envision a future where personal agents handle everything from travel planning to complex financial management. But in practice, it's not that simple. The issue is not with the performance of AI itself. The real limitation lies in the current network infrastructure.

The web is built for humans to read and interpret through browsers. Therefore, it is very unsuitable for agents that need to parse semantics and connect relationships across data sources. These limitations are evident in everyday services. An airline's website may list the departure time as "14:30", while a hotel website shows the check-in time as "2:30 PM". Humans immediately understand that both refer to the same time, but agents interpret them as completely different data formats.

Source: Tiger Research

The problem lies not only in the format differences. A key challenge is whether agents can trust the data itself. Humans can handle incomplete information by relying on context and prior experience. In contrast, agents lack clear standards for evaluating sources or reliability. This makes them vulnerable to erroneous inputs, flawed conclusions, and even hallucinations.

Ultimately, even the most advanced agents cannot thrive in such conditions. They are like F1 cars: no matter how powerful, they cannot drive at full speed on unpaved roads (unstructured data). If misleading signs (unreliable data) are scattered along the way, they may never reach the finish line.

2. Technical Debt of the Network: Rebuilding the Infrastructure

This issue was first raised over 20 years ago by Tim Berners-Lee, the founder of the World Wide Web, through his proposal for the Semantic Web.

The core idea of the Semantic Web is simple: to structure network information so that machines can understand it, rather than just human-readable text. For example, "Tiger Research was founded in 2021" is clear to humans, but for machines, it is merely a string. The Semantic Web structures it as "Tiger Research (subject) - was founded (predicate) - in 2021 (object)" so that machines can interpret the meaning.

This approach was ahead of its time, but ultimately failed to materialize. The biggest reason was the implementation challenges. Reaching consensus on data formats and usage standards proved to be difficult, and more importantly, it was almost impossible to build and maintain large datasets through voluntary user contributions. Contributors did not receive direct rewards or benefits. Moreover, whether the created data is trustworthy remains an unresolved issue.

Nevertheless, the vision of the semantic web remains valid. The principle that machines should understand and utilize data at a semantic level has not changed. In the age of AI, this demand has become even more critical.

3. Intuition: Reviving the Semantic Web in a Web3 Way

Intuition evolves the vision of the semantic web through Web3 methods to address the existing limitations. The core lies in creating a system that incentivizes users to voluntarily participate in the accumulation and verification of high-quality structured data. This systematically constructs a machine-readable, clearly sourced, and verifiable knowledge graph. Ultimately, this provides the foundation for reliable operation of agents and brings us closer to the future we envision.

3.1. Atom: Building Blocks of Knowledge

Intuition first divides all knowledge into the smallest units called Atoms. Atoms represent concepts such as people, dates, organizations, or attributes. Each Atom has a unique identifier (using technologies like Decentralized Identifiers DIDs) and exists independently. Each Atom records contributor information, allowing you to verify who added what information and when.

The reason for breaking down knowledge into atoms is clear. Information often appears in the form of complex sentences. Machines like agents have structural limitations when parsing and understanding such composite information. They also struggle to determine which parts are accurate and which are incorrect.

Subject: Tiger Research

Predicate: Established in

Object: Year 2021

Consider the sentence "Tiger Research was established in 2021." This may be true, or only partially incorrect. Whether the organization actually exists, whether the "establishment date" is an appropriate attribute, and whether 2021 is correct, each needs to be verified individually. However, treating the entire sentence as a unit makes it difficult to distinguish which elements are accurate and which are incorrect. Tracking the source of each piece of information also becomes complicated.

Atomic solves this problem. By defining each element as an independent atom, such as [Tiger Research], [established in], [2021], you can record the source and individually verify each element.

Subject: The establishment date of Tiger Research is 2021.

Predicate: Based on

Object: Official Record

Atoms are not just tools for splitting information—they can be combined like Lego blocks. For example, individual atoms [Tiger Research], [established in], and [2021] connect to form a triple. This creates meaningful information: "Tiger Research was established in 2021." This follows the same structure as the triples in the semantic web RDF (Resource Description Framework).

These triples can also become atoms themselves. The triple "Tiger Research was founded in 2021" can be expanded into a new triple, such as "The founding date of Tiger Research in 2021 is based on business records." Through this method, atoms and triples combine repeatedly, evolving from small units into larger structures.

The result is that Intuition has built a fractal knowledge graph that can expand infinitely from basic elements. Even complex knowledge can be decomposed for verification and then recombined.

3.2. TCRs: Market-driven Consensus

If Intuition provides a conceptual framework for structured knowledge through atoms, there are still three key questions: Who will contribute to creating these atoms? Which atoms can be trusted? When different atoms compete to represent the same concept, which one becomes the standard?

Source: Intuition Light Paper

Intuition solves this problem through TCRs. TCRs are based on community-valued content filtering entries. Token staking reflects these judgments. Users stake $TRUST (Intuition's native token) when proposing new atoms, triplets, or data structures. Other participants stake tokens on the supporting side if they find the proposal useful; if they find it useless, they stake tokens on the opposing side. They can also stake on competitive alternatives. If the data chosen by users is frequently used or receives high ratings, they will be rewarded. Otherwise, they will lose part of their stake.

TCRs validate individual proofs, but they also effectively address the ontology standardization issue. Ontology standardization means determining which method becomes the common standard when there are multiple ways to express the same concept. Distributed systems face the challenge of reaching this consensus without centralized coordination.

Consider the predicates of the evaluations of two competing products: [hasReview] and [customerFeedback]. If [hasReview] is introduced first and many users build upon it, early contributors have Token rights in that success. At the same time, supporters of [customerFeedback] receive economic incentives, gradually shifting toward a more widely adopted standard.

This mechanism reflects how the ERC-20 Token standard is naturally adopted. Developers who adopt ERC-20 gain clear compatibility benefits—direct integration into existing wallets, exchanges, and dApps. These advantages naturally attract developers to use ERC-20. This indicates that market-driven choices alone can address standardization issues in distributed environments. TCRs work on similar principles. They reduce the struggle of agents with fragmented data formats and provide an environment where information can be understood and processed more consistently.

3.3. Signal: Building a Trust-Based Knowledge Network

Intuition structures knowledge through atoms and triples, and reaches consensus on "what is actually used" using incentives.

The final challenge still remains: to what extent can we trust this information? Intuition introduces Signal to fill this gap. Signal expresses the user's trust or distrust in a specific atom or triplet. It goes beyond simply recording the existence of data—it captures how much support the data receives in different contexts. Signal systematizes the social verification processes we use in real life, such as when we judge information based on "a reliable person recommended this" or "an expert has verified it."

Signals accumulate in three ways. First, explicit signals involve intentional assessments made by users, such as Token staking. Second, implicit signals emerge naturally from usage patterns (such as repeated queries or applications). Finally, transitive signals create relational effects—when someone I trust supports information, I am also inclined to trust it more. The combination of these three creates a knowledge network that shows who trusts what, how much trust they have, and in what ways they trust.

Source: Intuition White Paper

Intuition provides this through Reality Tunnels. Reality Tunnels offer a personalized perspective for viewing data. Users can configure tunnels that prioritize expert group evaluations, value close friends' opinions, or reflect the wisdom of specific communities. Users can choose trusted tunnels or switch between multiple tunnels for comparison. Agents can also use specific interpretive methods for particular purposes. For example, choosing a tunnel that reflects Vitalik Buterin's trusted network will set the agent to interpret information and make decisions from "Vitalik's perspective."

All signals are recorded on the chain. Users can transparently verify why specific information seems credible, which servers serve as sources, who vouches for it, and how many Tokens are staked. This transparent process of forming trust allows users to directly verify the evidence rather than blindly accept information. Agents can also use this foundation to make judgments suitable for individual contexts and perspectives.

4. What if Intuition becomes the next generation of network infrastructure?

The infrastructure of Intuition is not just a conceptual idea, but a practical solution to the problems faced by agents in the current network environment.

The current network is filled with fragmented data and unverified information. Intuition transforms data into deterministic knowledge graphs, providing clear and consistent results for any query. Token-based signals and curation processes validate this data. Agents can make clear decisions without relying on guesswork. This simultaneously improves accuracy, speed, and efficiency.

Intuition also provides a foundation for agent collaboration. Standardized data structures allow different agents to understand and communicate information in the same way. Just as ERC-20 created token compatibility, Intuition's knowledge graph creates an environment where agents can collaborate based on consistent data.

Intuition goes beyond the infrastructure limited to agents, becoming a foundational layer that all digital services can share. It can replace the trust systems currently built separately by each platform with a unified foundation—Amazon's reviews, Uber's ratings, LinkedIn's endorsements. Just as HTTP provides a common communication standard for the web, Intuition provides standard protocols for data structures and trust verification.

The most important change is data portability. Users directly own the data they create and can use it anywhere. Data isolated on various platforms will be connected and reshape the entire digital ecosystem.

5. Rebuilding the foundation for the upcoming era of intelligent agents

The goal of Intuition is not simply a technical improvement. It aims to overcome the technical debt accumulated over the past 20 years and fundamentally redesign the network infrastructure. When the semantic web was first proposed, the vision was clear. But it lacked incentives to drive participation. Even if their vision is realized, the benefits remain unclear.

The situation has changed. Advances in AI are making the era of intelligent agents a reality. AI agents now go beyond simple tools. They represent us in executing complex tasks. They make autonomous decisions. They collaborate with other agents. These agents require fundamental innovations in existing network infrastructure to operate effectively.

Source: Balaji

As pointed out by former Coinbase CTO Balaji, we need to build the proper infrastructure for these agents to operate. The current network resembles an unpaved road rather than a highway where agents can securely move on trusted data. Each website has different structures and formats. Information is unreliable. Data remains unstructured, making it difficult for agents to comprehend. This creates significant obstacles for agents to perform tasks accurately and efficiently.

Intuition seeks to reconstruct networks to meet these needs. It aims to build standardized data structures that are easy for agents to understand and use. It requires a reliable information verification system. It needs protocols that allow for smooth interaction between agents. This is similar to how HTTP and HTML established network standards in the early days of the internet. It represents an attempt to establish new standards for the agent era.

Of course, challenges still exist. Without sufficient participation and network effects, the system cannot operate properly. Achieving critical mass requires quite a bit of time and effort. Overcoming the inertia of existing network ecosystems has never been easy. Establishing new standards is difficult. But this is a challenge that must be addressed. The rebase proposed by Intuition will overcome these challenges. It will open up new possibilities for the era of agents that is just beginning to be imagined.