2026 AI Agent Economic Outlook: Reshaping AI Identity and the Flow of Network Value

Author: @BlazingKevin_, the Researcher at Movemaker

Introduction: A Structural Leap from Generative AI to "Agent Behavior"

In 2026, the field of artificial intelligence will undergo a structural leap from "generative capabilities" to "Agent agency." If 2023-2024 is about the stunning language generation capabilities of large language models, then 2026 will mark the formal establishment of the "AI Agent economy."

Based on predictions and analyses from the a16z Crypto research team, further research indicates that 2026 will be a year of deep integration between AI as a productivity tool and Crypto as a value distribution layer.

AI will no longer be a passive tool that responds to human commands but will become an active participant with reasoning, planning, trading, and autonomous discovery capabilities.

According to a16z Crypto's outlook report, the three core trends reshaping the AI+Crypto landscape in 2026 are:

1. New Paradigm in Research: Transitioning from single Agents to "Agent-Wrapping-Agent."
2. Revolution in Financial Infrastructure: From KYC to KYA (Know Your Agent).
3. Reconstruction of Economic Models: Addressing the "invisible tax" crisis faced by open networks through payment integration and programmable IP.

These three trends are not isolated: the shift in research paradigms relies on advanced collaboration between Agents; advanced collaboration requires Agents to have verifiable identities (KYA); and Agents with identities must adhere to new value exchange protocols when acquiring data.

1. New Era of Polymaths: The "Agent-Wrapping-Agent" Architecture in Advanced Research

Starting this year, the definition of "AI-assisted research" will undergo a qualitative leap.

We will no longer discuss simple literature retrieval or text summarization but will witness AI systems capable of substantial reasoning, hypothesis generation, and even autonomously solving PhD-level problems.

The core driving force behind this transformation is the shift from linear prompt engineering of single models to complex, recursive AWA workflows.

1.1 Breakthrough in Reasoning Capabilities: Crossing the Boundaries of Pattern Matching

Scott Kominers from a16z points out that AI models are evolving from merely understanding instructions to being able to receive abstract instructions (like guiding PhD students) and returning novel and correctly executed answers. Recent technological advancements indicate that AI models are breaking through the "random parrot" ceiling, demonstrating slow, deliberate reasoning capabilities akin to human "system" thinking.

1.1.1 "Useful Hallucinations"

With the enhancement of reasoning capabilities, a new "polymath" research style is emerging. Scott describes this style as: "Using AI to cross disciplinary boundaries, speculating on the deep connections that may exist between topology and economics, biology and materials science."

The "hallucination" characteristic often criticized in large models is being recontextualized as a mechanism for "generative exploration" in the context of scientific discovery:

• Protein Design Case: Researchers at the University of Washington utilized "family hallucinations" (concepts) to generate over 1 million unique protein structures that do not exist in nature. Among them, a newly identified luciferase exhibits catalytic activity comparable to natural enzymes but with higher substrate specificity.
• Fluid Dynamics Discovery: Through Physics-Informed Neural Networks (PINNs), researchers discovered new unstable singularities in the Navier-Stokes equations, revealing previously unknown patterns in fluid motion.

The core of this research style is: allowing models to "think outside the box" in abstract spaces to generate high-entropy hypotheses, which are then filtered using rigorous logical validators.

1.2 Detailed Explanation of AWA Architecture

To harness this powerful reasoning and generative capability, research workflows are shifting from flat to hierarchical structures. AWA refers not only to the dialogue between multiple Agents but also to a recursive, layered control structure.

1.2.1 Orchestrator-Executor Model

This is currently the most mainstream implementation of AWA. A "Chief Researcher" Agent is responsible for maintaining the global context and research goals, breaking down tasks, and distributing them to a group of specialized "Executor" Agents.

• Architectural Advantage: Data from Anthropic shows that a multi-agent system composed of Claude Opus as the lead Agent and Claude Sonnet as a sub-Agent performs 90.2% better on complex research tasks than a single Claude Opus Agent.

  This performance improvement is primarily due to the isolation of context—the lead Agent does not need to handle redundant information from each sub-task, thus maintaining clarity in reasoning.

1.2.2 Recursive Self-Improvement and the MOSAIC Framework

Another key feature of the AWA architecture is the introduction of a Reflexion (reflection) loop. When a lower-level Agent fails to execute a task, error information is fed back to a "critic" Agent for analysis and correction.

The MOSAIC framework (Multi-Agent System for AI-driven Code generation) significantly improves the accuracy of scientific code generation by introducing specialized "self-reflecting Agents" and "principle-generating Agents," without relying on validated test cases. This "trial-error-reflection-retry" closed loop simulates the thought process of human scientists when facing experimental failures.

1.3 Case Study: Sakana AI's "AI Scientist"

One of the most notable AWA application cases in 2025 is the "The AI Scientist" system released by Sakana AI. This is a system designed to fully automate the entire lifecycle of scientific discovery.

1.3.1 Fully Automated Research Closed Loop Process

1. Idea Generation: The system uses an initial code template (like NanoGPT), leveraging LLM as a "mutation operator" to brainstorm diverse research directions and calls the Semantic Scholar API to retrieve literature to ensure novelty.
2. Experimental Iteration: The "Experimenter" Agent writes and executes code. If the experiment fails, the system captures error logs through the Aider tool and autonomously fixes the code until a visual graph is obtained.
3. Paper Writing: The "Writer" Agent uses LaTeX to write a complete scientific paper, covering the abstract, methods, experimental results, and autonomously searches for references to generate BibTeX.
4. Automated Peer Review: The generated paper is submitted to a simulated "Reviewer" Agent, which scores it according to the standards of top conferences (like NeurIPS). The system can even make multiple rounds of revisions based on reviewer feedback.

1.3.2 Economic Efficiency and Quality

The economic efficiency of the "AI Scientist" system is astonishing: the computational cost of generating a complete research paper is only about $15. The paper generated by this system, titled "Compositional Regularization," even successfully passed peer review at the ICLR workshop. Although there are still limitations such as citation hallucinations and logical flaws, this case demonstrates that AI has the capability not only to assist in research but also to execute the entire research process.

2. The Command of Identity: From KYC to KYA

As Agents are granted the authority to execute tasks and conduct transactions, the digital economy faces an unprecedented identity crisis. Sean Neville (CEO of Catena Labs) warns that the number of "non-human identities" in the financial services sector has reached 96 times that of human employees, and in some statistics, it is as high as 100:1. These Agents—operating at machine speed without bank accounts or real-name verification—represent a massive compliance black hole. The industry is urgently shifting from traditional KYC to KYA (Know Your Agent).

2.1 The Explosion and Risks of Non-Human Identities (NHI)

2.1.1 "Shadow AI" and the 96:1 Imbalance

45% of financial service institutions admit to having unauthorized "shadow AI Agents" internally. These Agents have created "identity islands" outside formal governance frameworks.

• Risk Scenarios: A test Agent used for cloud resource optimization might autonomously purchase expensive reserved instances without human intervention; or a trading bot might trigger erroneous sell orders during market fluctuations.
• Attribution Dilemma: When an Agent violates regulations, who is responsible? The engineer who developed it? The manager who deployed it? Or the vendor providing the underlying model? Without KYA, these responsibilities cannot be defined.

2.2 KYA Framework: The Trust Cornerstone of Machine Economy

KYA is not just about issuing identity cards; it is about establishing a complete digital identity system that includes subjects, credentials, permissions, and reputation.

2.2.1 Three Pillars of KYA

1. Subject: The entity legally responsible for the Agent. The Agent must be cryptographically linked to a human or corporate account that has undergone KYC/KYB verification.
2. Agent Identity: A unique digital identity based on decentralized identifiers. DIDs are cryptographically generated, immutable, and portable across platforms.
3. Authorization Delegation: Permission statements issued through Verifiable Credentials (VCs). For example, a VC might state: "This Agent is authorized to spend up to $500 on Amazon on behalf of Alice."

2.2.2 Cryptographic Binding and Trust Chain

When an Agent initiates a transaction, it presents a VC. The verifier does not need to trust the Agent itself; they only need to verify that the digital signature on the VC comes from a trusted issuer. This mechanism creates a "trust chain": banks trust enterprises -> enterprises issue VCs to Agents -> merchants verify VCs -> transaction is approved.

2.3 Protocol Stack Battle: Standardization of Agent Identity

2.3.1 Skyfire and KYAPay Protocol

Skyfire has launched the KYAPay open standard, with its core innovation being composite tokens:

• kya token: Contains identity information (e.g., "verified enterprise Agent").
• pay token: Contains payment capabilities (e.g., "pre-authorized 10 USDC").
• kya+pay token: Bundles identity and payment, allowing Agents to complete "guest checkout" without manual form filling.

2.3.2 Catena Labs and ACK (Agent Commerce Kit)

Founded by USDC architect Sean, Catena Labs has launched ACK, aiming to create the "HTTP of agent commerce." ACK emphasizes using W3C DID standards and account abstraction to allow Agents to directly control on-chain smart contract wallets, achieving stronger security than API keys.

2.3.3 Google AP2 and x402 Extension

Google's Agent Payments Protocol (AP2) manages permissions using "authorization letters" and has collaborated with Coinbase to develop the AP2 x402 extension, directly integrating cryptocurrency payment standards into the protocol.

2.4 Agent Credit Scoring and Risk Control

KYA is also the beginning of a reputation system.

• On-chain Reputation (ERC-7007): Through ERC-7007 (the standard for verifiable AI-generated content tokens), every successful interaction of an Agent (such as timely payments or generating high-quality code) can be recorded on-chain, forming a verifiable history.
• Real-time Circuit Breaker: Financial institutions are deploying AI gateways that can immediately revoke an Agent's VC if its behavior deviates from the baseline (such as high-frequency anomalous trading), triggering "digital suppression."

3. Economic Reconstruction: Addressing the "Invisible Tax" of Open Networks

Liz from a16z points out that AI Agents are imposing an "invisible tax" on open networks: Agents are extracting data from content websites (context layer) on a large scale to serve users while systematically bypassing the advertising and subscription models that support content production. If this parasitic relationship is not addressed, it will lead to the depletion of the content ecosystem.

3.1 "Great Decoupling": The Full Arrival of the Zero-Click Economy

In 2025, the digital publishing industry witnessed the "Great Decoupling": search volume increased, but the click-through rates to websites plummeted.

3.1.1 The Harsh Data of Traffic Erosion

• Surge in Zero Click Rates: a16z predicts that by 2026, traffic from traditional search engines will decline by 25%. Data from Similarweb shows that the zero-click search rate had already risen to 65% in 2025.
• Collapse of Click-Through Rates (CTR): DMG Media reports that when AI Overview appears at the top of search results, the click-through rate of its content plummets by 89%. Even the top-ranking search results lose 34.5% of clicks in the face of AI summaries.

3.2 Breaking Free from Static Licensing: A New Pay-Per-Use Model

To address this crisis, the industry is shifting from static annual data licenses (like the deal between Reddit and OpenAI) to usage-based compensation.

3.2.1 Perplexity's Comet Plus Model

Perplexity AI's Comet Plus plan is a typical early attempt:

• Mechanism: Establishing an initial $42.5 million revenue pool. When an AI Agent cites publisher content in its answers or accesses pages on behalf of users, revenue distribution is triggered.
• Revenue Share: Publishers can receive up to 80% of the relevant revenue pool. This acknowledges the commercial value of "machine access."

3.3 Technical Standards: Nano Payments and Micro Attribution

To extend compensation across the web, a series of open technical standards are being implemented.

3.3.1 Nano Payments and x402 Protocol

The HTTP 402 status code has finally been activated. The x402 protocol establishes standards for "machine-native payments."

• Workflow: Agent requests resources -> server returns 402 Payment Required and price (e.g., 0.001 USDC) -> Agent automatically signs payment via L2 blockchain (like Base, Solana) or Lightning Network -> server verifies and releases data.
• Economics: Traditional payment gateways cannot handle transactions of a few cents, while x402, combined with low-fee chains, reduces costs to negligible amounts, making nano payments possible.

3.3.2 Machine-Readable Rights: TDMRep and C2PA

• TDMRep (Text Data Mining Reservation Protocol): A W3C community standard that allows websites to declare in robots.txt or HTTP headers: "TDM rights reserved, payment/license required." This provides Agents with a clear binary signal.
• C2PA (Coalition for Content Provenance and Authenticity): By embedding tamper-proof "content credentials," it proves the original source of content. Even if content is ingested by AI, the cryptographic signatures provided by C2PA ensure that attribution links remain intact, providing a basis for royalty distribution.

3.4 On-Chain IP Ownership: Story Protocol

A more radical change is the tokenization of intellectual property itself. Story Protocol aims to build a "programmable IP" layer.

• Mechanism: Creators register their works as "IP assets" on the Story Network.
• Automated Licensing: Assets come with "programmable IP licenses." When AI Agents use the data, smart contracts automatically execute licensing terms (e.g., "5% royalty for commercial use") and automatically distribute revenue. This creates a highly liquid IP market without the need for lawyer intervention.

3.5 Outlook: From SEO to AEO

By 2026, the marketing focus will shift from SEO to AEO or GEO.

• Goal: No longer pursuing "first in search ranking," but aiming to be "cited" by AI or become the "preferred data source" in its reasoning process.
• Context Sponsorship: Future advertising models will involve "context injection." Brands will bid to enter the reasoning chain of Agents, for example, prompting travel Agents to "remember" a certain hotel as the best option when planning a trip.

4. Conclusion

The technological landscape of 2026 clearly indicates that the friction between human-centered internet infrastructure and machine-centered demands is forcing a complete reconstruction of the digital world.

1. Research Paradigm: AI is moving from assistance to autonomy, and the AWA architecture enables AI to produce scientific discoveries at low cost, transforming "hallucinations" into creativity.
2. Identity System: KYA becomes the new frontier of financial compliance, granting billions of AI Agents legitimate economic identities, allowing them to safely navigate value networks.
3. Economic Models: The network economy is shifting from attention-based advertising models to value-based nano payments and programmable IP models. x402, TDMRep, and Story Protocol form the tracks of the new economy, addressing the "invisible tax" issue and ensuring that data producers remain profitable in the zero-click era.

We are witnessing the birth of the Agent economy—in this economy, software not only helps us work but also acts as producers, consumers, and traders themselves.

About Movemaker

Movemaker is the first official community organization authorized by the Aptos Foundation and jointly initiated by Ankaa and BlockBooster, focusing on promoting the construction and development of the Aptos ecosystem in the Chinese-speaking region. As the official representative of Aptos in the Chinese-speaking area, Movemaker is dedicated to building a diverse, open, and prosperous Aptos ecosystem by connecting developers, users, capital, and numerous ecological partners.

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