The biggest loophole in the AI economy! Without an Oracle Machine, it cannot determine truth from falsehood, facing the risk of collapse.

LLM cannot reliably determine the state of the real world, and AI Agents face high vulnerabilities in execution. The Agent Oracle we lack is the cornerstone of the entire Agent ecosystem, yet it has long been overlooked. The essence of LLM capabilities is to generate the most probabilistically optimal text, rather than being a system for inferring the truth of the world. It does not verify the truthfulness of news, nor does it identify phishing links; these belong to “fact verification” rather than “language prediction.”

The Fatal Blind Spot of LLM: Unable to Verify the Truth of the World

Why isn't LLM enough? Because the essence of LLM's capability is to generate the most probable text, rather than to infer the truth of the world. This distinction is crucial, yet often overlooked. When you ask ChatGPT “What is the price of Bitcoin today?”, the answer it gives is not derived from real-time queries, but is generated based on training data and statistical patterns as the “most likely correct” response. If the training data is outdated or contains erroneous information, LLM will unhesitatingly generate incorrect answers, and do so with great confidence.

This characteristic has limited impact in everyday conversations, but it is fatal when the AI Agent is performing actual tasks. When the Agent needs to open accounts, trade, access websites, or submit forms, it is exposed to extremely high vulnerability. It does not verify the authenticity of news, does not identify phishing links, does not determine whether an API is compromised, does not understand whether a regulation is truly in effect, and cannot accurately grasp the real bias behind Powell's speech.

A specific example: Suppose you let an AI Agent help you buy a new coin. The Agent may: (1) search for information about the coin but cannot determine if the sources are trustworthy; (2) find a seemingly legitimate trading website but cannot identify if it is a phishing site; (3) execute the transaction but cannot verify if the smart contract contains backdoors; (4) confirm the transaction was successful, but in reality, the funds have been stolen. Throughout this process, the LLM is “doing due diligence,” but the lack of verification capability in the real world means that each step could go wrong.

These all belong to “verification” rather than “prediction.” Therefore, the LLM itself can never become the “source of truth” for an Agent. Regardless of how powerful GPT-5 or GPT-6 becomes, this inherent limitation will not change, as it is a structural issue rather than a matter of capability.

Five Major Blind Spots of LLM in Real-World Judgments

News Authenticity Verification: Unable to distinguish between real reports and fake news or AI-generated content

Phishing Identification Ability: Unable to determine whether the website, email, or link is a scam.

API Pollution Detection: Unable to verify if the data source has been tampered with or subjected to a man-in-the-middle attack.

Regulatory Effect Judgment: Unable to confirm whether a certain law is truly in effect or how it is enforced.

Intent Behind the Semantics: Unable to understand the true meaning of officials' speeches and company announcements.

Limitations of Traditional Oracle Machines: Price Truth vs Event Truth

Traditional Oracle Machines cannot solve this problem either. Traditional Oracles like Chainlink and Band Protocol excel at price truths: ETH/USD, BTC/BNB, indices, foreign exchange, on-chain TVL, and other structured, quantifiable, observable data. These data have clear data sources (exchange APIs), standardized formats (numbers), and objective verification methods (multi-node consensus).

But the reality faced by AI Agents is completely different: unstructured events, multi-source conflicts, semantic judgments, real-time changes, and fuzzy boundaries — this is the truth of events, which is an order of magnitude more complex than the truth of prices. For example: “Is a certain news true?” requires verifying multiple sources, analyzing language style, checking the authenticity of images, and tracing the source of the information; “Is a certain project trustworthy?” requires assessing team background, auditing reports, community feedback, and historical performance; “Does a certain tweet imply a positive outlook?” requires understanding the context, analyzing sentiment, and judging the speaker's intent.

The truth of the event ≠ the truth of the price; the mechanisms of the two are completely different. The truth of the price can be derived from the weighted average of prices from multiple exchanges, with clear data sources that are easy to verify. The truth of the event involves semantic understanding, context analysis, and cross-verification from multiple sources, which cannot be handled by the traditional Oracle Machine's node voting mechanism. Nodes can verify that “the BTC price returned by a certain exchange's API is 87,000 USD,” but they cannot verify whether “a certain news article is credible” or “a certain smart contract is secure.”

Sora's Revolutionary Attempt: Event Verification Market

The event verification market proposed by Sora is currently the closest attempt in the right direction. The core transformation of Sora is: the truth is no longer generated by node voting, but by agents executing real verification tasks. A query will undergo data fetching (TLS, Hash, IPFS), outlier filtering (MAD), LLM semantic verification, multi-agent reputation weighted aggregation, reputation updates, and challenge penalties.

Sora's key insight is that Earn = Reputation: income comes from credibility, and credibility comes from long-term real work, rather than stake or self-declaration. This direction is very revolutionary, as it transforms the Oracle Machine from “passive quoting” to “active validation.” Agents do not simply pull data from APIs and report it; they are required to perform actual validation work: accessing multiple websites, comparing different sources, analyzing content authenticity, and providing confidence scores.

However, Sora is still not open enough. The verification experts for real-world events are extremely diverse, ranging from finance, regulations, healthcare, multilingualism, to security audits, fraud detection, on-chain monitoring, and industry experience. No single team can build an Agent cluster that covers all fields. It's like trying to establish a company that includes experts from all fields, which is practically impossible to achieve. True expertise is dispersed among millions of experts worldwide, and closed platforms cannot effectively integrate this knowledge.

ERC8004 + x402: Open Truth Gaming Market

What we need is an open, multi-stakeholder “truth game market.” Why? Because the way humans obtain the truth is not by asking a single expert, but by checking multiple sources, asking several friends, listening to various KOLs, and then extracting a stable understanding from the conflicts. The AI Agent world must also evolve along this mechanism.

The combination of ERC8004 + x402 provides a technical framework. ERC8004 is responsible for establishing a programmable reputation layer that records each Agent's historical performance, call frequency, success cases, challenge records, areas of expertise, stability, etc., allowing the “verifiable career” to naturally determine the Agent's eligibility for participation. This is similar to the resume and recommendation letter systems in human society, but it is completely transparent and immutable.

x402 Responsible for the payment layer, through which we can dynamically gather multiple high-reputation Agents in a single event verification, allowing them to verify in parallel, cross-check, and aggregate output results based on contribution weighting. It’s not about finding one expert, but about convening a committee—that’s the “Truth Committee” of the machine world. When there's a need to verify a piece of news, the system may summon 10 Agents specialized in that field, each executing verification, providing scores and evidence, and ultimately reaching a conclusion through reputation weighting.

The advantage of this mechanism lies in its self-evolution capability. Well-performing Agents accumulate reputation, gaining more job opportunities and higher income; poorly performing Agents lose reputation and are gradually eliminated from the market. There is no need for centralized review or admission mechanisms, as the market naturally filters out the most reliable verifiers. This openness also allows anyone to deploy professional Agents into the market; as long as they can provide high-quality verification, they can earn income.

An open, multi-entity, reputation-weighted, challenge-incentivized, and self-evolving truth market may be the true form of the future Oracle Machine in a meaningful sense. This not only serves AI Agents but may also reshape the way humans acquire information.

The Social Semantic Truth Layer of Intuition

At the same time, Intuition is building another layer: Semantic Truth. Not all truths can be derived through event verification, such as “Is a certain project trustworthy?” “Is the quality of governance good?” “Does the community like a certain product?” “Is a certain developer reliable?” “Is a certain viewpoint recognized by the mainstream?” These are not Yes/No, but rather social consensus, suitable to be expressed using the TRUST triplet (Atom — Predicate — Object), and consensus strength is accumulated through stake support or opposition.

It is applicable to long-term facts such as reputation, preference, risk level, labels, etc. This social consensus mechanism complements the areas that event verification cannot cover. Event verification is suitable for answering “Did a certain event happen?”, while social consensus is suitable for answering “What does a certain event mean?” or “How is a certain entity evaluated?”

However, the product experience of Intuition is indeed very poor at the moment. For instance, to establish “V is the founder of Ethereum”, all related terms must have an identity within the system, and the process is very awkward. The pain points are clear, but their solutions are currently not good enough. This kind of user experience issue may limit its adoption, but the core concept direction is correct.

Future Three-Layer Reality Stack Architecture

Thus, the structure of future truths will present two complementary layers: event truth (Agent Oracle) responsible for real-time world, and semantic truth (TRUST) responsible for long-term consensus, which together form the truth foundation of AI. The reality stack will be clearly divided into three layers:

Reality Stack Three-Layer Architecture

Event Truth Layer: Sora / ERC8004 + x402, responsible for real-time event verification and real-world status assessment.

Semantic Truth Layer: TRUST / Intuition, responsible for social consensus, reputation assessment, and long-term facts.

Settlement Layer: L1/L2 blockchain, providing immutable records and economic incentives.

This structure is likely to become the real foundation of AI × Web3. Without the Agent Oracle, AI Agents cannot verify authenticity, determine sources, avoid fraud, prevent data contamination, undertake high-risk actions, or cross-check like humans do. Without it, the Agent economy cannot be established; but with it, we can finally create a verifiable reality layer for AI.

The future Oracle Machine will not be a node network, but will consist of countless professional Agents: they accumulate reputation through income, participate in verification through reputation, obtain new jobs and challenges through verification, collaborate automatically, divide work automatically, and self-evolve, ultimately expanding to all fields of knowledge. That will be a true machine society truth market.

Blockchain provides us with a trusted ledger, while the era of AI Agents requires trusted realities, trusted events, trusted semantics, trusted judgments, and trusted executions. Agent Oracle = the real foundation of AI. The future belongs to those protocols that can help machines understand the real world.