🚫Problem Definition

What's Happening in the Market?

We're on the cusp of an unprecedented economic transformation, driven by the rise of Multi-Agent Systems (MAS) powered by Artificial Intelligence. AI is evolving from isolated tools into networks of autonomous, specialized agents capable of collaborating and transacting at speeds and scales impossible for humans. This is the "start of the AI maturity phase," with market projections indicating exponential growth from billions to hundreds of billions of dollars in the next decade, and a "trillion-dollar opportunity" for the global economy.

However, despite this colossal potential, the AI agent ecosystem is still nascent and faces significant bottlenecks hindering its full realization:

Proto-Persona

Proto-Persona 1: The Agent Creator

For the innovators building these specialized agents (whether for marketing analysis, legal writing, software development, risk assessment, or project management), the pain points are palpable:

• Don't know how to monetize their agent: The absence of a native payment infrastructure for AI means agents are "limited to pre-funded accounts with usage limits" and "cannot directly transact with other AI agents." This stifles the ability to monetize granular, high-volume services.

• Lack a reliable distribution channel: There isn't a centralized, transparent hub where creators can efficiently list, promote, and distribute their agents to a broad and relevant audience.

• Difficulty proving their agent's quality: In a trustless environment, it's challenging for creators to demonstrate their agents' effectiveness and reliability in a verifiable and auditable way.

• No real market visibility (usage, clients, feedback): The lack of transparent metrics and public feedback prevents creators from understanding their agents' market performance and identifying improvement opportunities.

Proto-Persona 2: The Agent Buyer

For organizations and professionals looking to leverage the power of AI agents to optimize their operations, the challenges are equally frustrating:

• Don't know where to find reliable agents: Market fragmentation and the absence of a centralized repository make it difficult to discover specialized, high-quality agents for specific needs.

• Difficulty understanding the true value of agents: Without verifiable proofs of work and transparent performance metrics, it's hard for buyers to assess an AI agent's potential ROI.

• Need proofs of work, traceability, trust: The autonomous nature of agents demands a high degree of trust and auditability, which traditional systems can't provide.

• Need efficient micropayments (can't pay US$ 50 for a US$ 0.05 test): Traditional payment systems impose "disproportionately high fees on microtransactions," making the "pay-per-use" model, essential for the AI agent economy, unfeasible.

• Insecurity about paying for something that doesn't deliver results: The lack of "pay-per-contribution" mechanisms and the inability to audit agent performance in real-time create insecurity and financial risk.

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Global Landscape

The proliferation of AI agents, each performing specialized tasks and interacting frequently, creates an unprecedented demand for a payment infrastructure capable of handling high-frequency, low-value transactions. Traditional payment systems are fundamentally inadequate for this emerging economic reality, acting as a "hidden bottleneck" that hinders the advancement of the AI economy.

• Prohibitive Fees for Microtransactions: Traditional payment systems, like credit cards and banks, impose disproportionately high fees on microtransactions, making payments as low as US$0.01 economically unviable. These systems "crumble under the weight of micropayments," rendering them unsuitable for the granular, high-volume transactions characteristic of AI-to-AI interactions. For instance, Company A, which developed a software testing agent, can't monetize thousands of tiny checks costing fractions of a cent. It's forced to sell its agent through expensive monthly subscriptions or annual licenses, which limits access and flexibility for buyers.

• Slow Settlement Times: Transactions between AI agents demand millisecond-level payment speeds, but traditional banks and clearing systems can take hours or even days to settle payments. This substantial delay is compared to a "horse and buggy in a world racing toward AI-powered Teslas," severely impeding the real-time operations of autonomous agents.

• Need for Human Intermediation: Traditional financial systems require human authorization and the establishment of institutional accounts, something autonomous AI agents cannot achieve independently. This means AI agents are currently "forced to wait for the ability to exchange value," which restricts their autonomy and prevents end-to-end automation. Company B's internal agents (Business, Design, Developer) cannot autonomously interact and transact with an external testing agent, creating an automation bottleneck.

• Rigid Commercial Models: Traditional payment structures often force users into expensive monthly subscriptions or large upfront payments, which stifles the flexibility and accessibility of granular AI services. AI agents are currently "limited to pre-funded accounts with usage limits" and "cannot directly transact with other AI agents." For Company B, paying US$ 50 for an agent that performs a US$ 0.05 test is economically unfeasible and doesn't align with the granular nature of AI interactions.

• Inefficient Cash Flow Management: Providers of AI services frequently need to pay upfront for significant computational resources or server costs. Traditional systems, with their slow settlement times, exacerbate cash flow challenges by delaying cost recovery.

• Global Accessibility Challenges: Many potential users and AI services, especially in emerging markets, lack access to traditional bank accounts or credit cards, hindering the global scale of AI services.