AI multi-agent applications will boost the rise of blockchain payments

What advantages does blockchain have over mobile payments? This was a question that once embarrassed many blockchain experts. In 2015, renowned blockchain evangelist Andreas Antonopoulos was challenged during a speech by an audience member who asked, "Mobile payments are convenient and fast, while Bitcoin is slow and cumbersome. What advantages does it have in payments?" He did not defend or argue but instead presented a vivid scenario: if a self-driving taxi operates independently, needing to both collect payments and pay for charging, could Bitcoin be a more ideal payment solution?

This is a thought-provoking question. In fact, for over a decade, many thinkers in the blockchain field, such as Zhu Jiaming and Xiao Feng, have pondered this issue and posed a bold question: perhaps blockchain was never meant for humans but was prepared for AI and robots.

Ten years ago, discussions about AI agents and self-driving cars were still trendy research in laboratories, but now these technologies are being applied in real-world scenarios. Currently, the development of AI multi-agent organizations has become a hot direction. Will it open new bases for blockchain applications and ultimately facilitate the combination of AI and blockchain?

Multi-Agent Systems Are Becoming the Hottest AI Application Direction

Starting in the second half of 2025, multi-agent systems suddenly became one of the most popular directions for AI implementation. A significant proportion of the most attention-grabbing projects in the AI field recently belong to this direction.

• • The leader in Agentic AI, Anthropic, has continuously launched products like Cowork, Agent Teams, and Managed Agents, clearly indicating its leadership intent in this direction.
• • Google's Agent Development Kit (ADK) provides a standardized framework to help developers quickly build layered, scalable multi-agent systems, supporting parallel, sequential, and cyclical orchestration.
• • OpenClaw allows ordinary users to deploy a "lobster team" on local machines, collaborating to complete multi-step workflows, sparking enthusiastic discussions about "AI employees" and "one-person companies."
• • ByteDance's DeerFlow 2.0 quickly topped GitHub Trending after being open-sourced. It is a super-agent runtime infrastructure that can orchestrate sub-agents, long-term memory, and Docker sandboxes, autonomously completing long and complex tasks from minutes to hours, thoroughly addressing the pain points of traditional AI's "manual handover."
• • Gary Tan, CEO of the well-known Silicon Valley incubator YC, open-sourced gstack, turning Claude Code into a virtual startup team, with 23 professional roles and slash commands built-in, allowing one person to simulate the operation of an entire startup.
• • The TradingAgents project launched by teams from UCLA and MIT allows multiple agents to play the roles of fundamental analysts, sentiment analysts, technical analysts, and risk managers, making investment decisions through debate and collaboration, simulating the organizational processes of real trading companies.
• • Paperclips.AI focuses on organizing a group of agents into a complete structure capable of running a company autonomously, including organizational charts, budgets, governance, and goal setting, achieving a business closed loop with zero human intervention.

These projects point to a clear trend: people are no longer satisfied with a single AI assistant but are beginning to build real work teams and collaborative networks using multi-agent architectures. In the past, we primarily used AI to "ask questions and get answers," but now we are starting to let AI "work in teams"—dividing tasks, collaborating, supervising each other, and making autonomous decisions to complete complex tasks that human organizations can handle.

This is not a simple tool upgrade but a key turning point for AI from personal efficiency tools to organizational-level technology. The core of multi-agent systems lies in the fact that multiple agents are no longer isolated "role players" but form a dynamic network that can create accounts, scale, and recombine at any time based on task requirements, even collaborating across organizational boundaries. They can handle high-frequency, small, cross-entity, and cross-jurisdictional value exchanges while triggering complex contract execution conditions. These characteristics naturally require a brand new payment and transaction infrastructure for multi-agent organizations.

Why Must Multi-Agent Applications Be Equipped with a New Payment System?

When AI multi-agent organizations move from the laboratory to real tasks, payments and value exchanges are no longer optional features but the lifeblood of system operation.

Once a multi-agent network begins to handle actual business, it will generate a large number of high-frequency, small, cross-entity, and even cross-jurisdictional payment demands. Agent A may complete a content generation in seconds, and Agent B immediately needs to pay for its usage model; Agent C, after processing logistics data, needs to pay Agent D immediately for data usage fees; during cross-border collaboration, an agent in Singapore may need to pay an agent on a US server for computing power. These payment frequencies may reach dozens of times per minute, with amounts as small as 0.1 cents, and the participants may be completely different organizations or individuals. These exchanges often involve complex value flows: not just money, but also data, computing power, model invocation rights, fragments of intellectual property, etc.

More importantly, the dynamism of multi-agent organizations far exceeds that of traditional organizations. Agent accounts can be created at any time, organizations can scale up or down, and recombine at any time. A task that required five agents to form a small team one minute may require twenty agents to instantly reorganize for another task the next minute, some of which may come from external partners. When agents across organizations make payments to each other, it inevitably involves triggering complex contract execution conditions, and payments will only be automatically executed when thresholds are met. These conditions may be nested, multi-layered, and real-time, which traditional contracts cannot describe, and traditional banking systems cannot respond to in real-time.

Traditional banking systems are not equipped to handle this scenario. They are accustomed to large, batch, manually reviewed net clearing models, with response times measured in hours or even days. They cannot provide instant account opening services for thousands of agents that come and go at any time, cannot handle the requirement that each transaction comes with complex condition triggers, and cannot provide that kind of "personalized service"—agents need code-level, millisecond-level automatic execution, not a phone call to customer service or submitting paper applications. The rules of banks are designed for humans, and the processes are designed for stable institutions. When faced with a network of agents that can instantly reorganize, never sleep, and are globally distributed, its limitations are immediately exposed.

The advantages of blockchain as a new generation of financial market infrastructure are highlighted in this scenario.

Blockchain is essentially a distributed ledger that allows all participants to share the same real-time updated public ledger without repeated reconciliation. Smart contracts write contract terms directly into code, automatically executing once conditions are met, without the need for third-party intervention. Programmability allows payments to no longer be simple transfers but can be embedded in automated processes with any complex logic: triggering upon condition fulfillment, atomic execution, and rollback on failure. Per-transaction full settlement replaces traditional netting, with each transaction completing clearing and settlement simultaneously upon confirmation. Atomic settlement ensures that value transfer and asset delivery occur simultaneously, avoiding any party's default. Instant finality means that once a transaction is on-chain, it is irreversible and tamper-proof.

These features form an almost perfect fit with the operational logic of multi-agent organizations. Agents need to create accounts at any time, and the cost of generating blockchain addresses is nearly zero; agent organizations need to scale at any time, and smart contracts can instantly deploy new rules; cross-organizational collaboration requires complex condition triggers, and smart contracts are inherently designed for this; high-frequency micropayments need low-cost instant arrival, and blockchain's gas fees and Layer 2 solutions are driving costs down to negligible levels. Traditional infrastructure is centralized, rigid, and slow, while blockchain is decentralized, flexible, and real-time.

We are increasingly seeing that multi-agent organizations are not simply piecing together AI tools but are constructing a whole new collaborative paradigm. This paradigm places unprecedented demands on payment and transaction infrastructure, and blockchain is currently the only mature technology system that can meet these demands. It is not just an enhancement but a necessary infrastructure. When AI agents begin to truly work in teams, blockchain is no longer optional but essential.

Multi-Agent Applications Will Become the "Base" for Blockchain Payments

In traditional C2C payment scenarios, blockchain's performance is not outstanding. When ordinary people transfer money, WeChat or Alipay can complete the transaction in seconds with just an amount input and a QR code scan. Blockchain wallets, however, require copying addresses, checking gas fees, and waiting for block confirmations, resulting in a significantly inferior user experience. Over the past decade, blockchain has struggled to compete with mobile payments in everyday small transfers and face-to-face payments dominated by humans.

However, in the high-frequency, automated, contract-driven payment scenarios between AI agents, blockchain's advantages are far ahead.

Agents do not need QR codes or manual confirmations. They require payments to be automatically executed. Once preset conditions are met, smart contracts immediately trigger transfers, and the entire process requires no intermediary involvement. Programmability allows payments to embed complex logic: funds are released only when Agent A delivers specified content, Agent B completes data verification, and an external oracle confirms that market prices have reached a threshold. If any step fails, the transaction automatically rolls back. Blockchain supports 24/7 uninterrupted operation, enabling instant arrival between any global addresses, and each transaction possesses instant finality. These capabilities are currently beyond the reach of traditional banking systems and mobile payment platforms.

The applications of multi-agent organizations will become the "main stage" for blockchain payments.

In the early days, mobile payments did not have a clear advantage in face-to-face payments. People were still accustomed to cash and card payments, and mobile payments seemed even redundant in small shops. But it found a breakthrough in e-commerce scenarios. Order payments on platforms like Taobao and JD require online instant settlement and support massive concurrency, where mobile payments quickly established a foothold. It first refined the e-commerce payment experience to perfection, accumulating users, merchants, and network effects, and then fed back to society as a whole. Today, our ease of scanning to pay is precisely because mobile payments first achieved victory in the e-commerce base.

AI multi-agent organizations will become the most solid and explosive base for blockchain payments and value exchanges.

Here, payments are no longer occasional human actions but the norm of system operation. They may be micropayments occurring every second, computing power rental fees, model invocation fees, data usage fees, or intellectual property shares. These payments need to embed complex conditions and require atomic execution. Traditional payment infrastructure struggles to cope, while blockchain is inherently compatible. It does not need to change user habits because agents themselves are code. It does not require customer support because everything is guaranteed by contracts. It does not need centralized risk control because trust is provided by cryptography and distributed consensus.

I believe this precisely reflects the penetrative nature of blockchain technology. It establishes an irreplaceable structural advantage in the scenarios where it is most needed.

Blockchain does not need to completely replace existing payment systems. It only needs to take root in areas where humans temporarily cannot use it, building a new value network. When AI agents begin to work in large groups, this network will grow rapidly, extending from micropayments between agents to broader economic activities, ultimately feeding back into human society. Mobile payments proved themselves in e-commerce, while blockchain will prove itself through AI multi-agent organizations.

Once the base for agent payments is established, blockchain's position in the entire digital economy will also be fundamentally different.

A New Form of AI Economy

The combination of AI multi-agent organizations and blockchain goes far beyond a simple technological overlay. It will open up a new economic landscape, leading to profound changes in resource allocation, social exchange, individual income, and innovation ecosystems. Below, we will analyze from four dimensions.

First, significantly enhance the overall performance and resource allocation efficiency of AI multi-agent systems.

Currently, the vast majority of multi-agent applications are still in a "playhouse" stage. Developers mainly rely on agent skills, hooks, MCP, prompt engineering, and other means to simulate and customize personalized "digital employees." This is essentially still a primitive state of role-playing, where everyone uses prompts to create seemingly professional AI roles, then lets them chat and divide tasks, simulating a multi-step workflow that looks lively but has limited advantages compared to using a single, versatile AI assistant.

True multi-agent organizations are entirely different. Some agents will possess unique resources and capabilities that cannot be easily mimicked or replaced through simple customization. These capabilities may include proprietary datasets, exclusive model weights, real-time data sources in specific fields, high-precision simulation environments, or industry experience accumulated over long-term training. They can only be developed, nurtured, and released by institutions with unique resources willing to invest costs. Calling upon these advanced agents will inevitably involve real payments.

Smart contracts on blockchain play a key role here. They can write calling rules, pricing mechanisms, quality verification, and fee settlement all into code. Once conditions are met, payments are automatically triggered, and resources are automatically delivered; if conditions are not met, funds automatically roll back. The entire process is efficient, secure, programmable, and auditable. The past inefficient methods relying on manual negotiation, email confirmation, and post-event reconciliation will completely disappear. Resource allocation efficiency will thus be significantly enhanced, and the overall performance of the entire multi-agent system will reach a new level. This is not just a simple cost reduction but a true expansion of the system's capability boundaries.

Second, greatly promote the scale of social exchanges and the speed of economic growth.

Traditional financial infrastructure sets very high thresholds for micro, frequent, and complex conditional transactions. Bank transfers have minimum amount limits, clearing has time windows, and cross-border payments have exchange rate and compliance costs. These frictions directly block a large number of potential transactions.

When blockchain provides AI agents with a low-friction micropayment and value exchange network, the situation will fundamentally change. Agents can easily complete computing power rentals, data calls, model fine-tuning services, or even instant settlements for individual API calls for just a few cents. Transactions that were previously suppressed due to high costs now become feasible. A massive amount of previously impossible exchanges will be released, significantly accelerating the speed and scale of economic circulation.

Imagine: a content creation agent automatically pays a small copyright fee to the material-providing agent every time it generates a high-quality text; an investment analysis agent pays fees to the data source agent every time it calls real-time market data; a logistics optimization agent pays the corresponding reward to the map service agent every time it completes a path planning. These micropayments will accumulate into an extremely large value flow network. The density and frequency of economic activities will increase, and overall economic growth will gain new momentum.

Third, enable ordinary people to truly earn money through AI agents, addressing the structural gap in supply and demand in the AI era.

One of the most prominent contradictions in the AI era is that large model companies hold core capabilities, while the demands and supplies of many ordinary people struggle to connect effectively. Many individuals have unique data, experience, or scenarios but lack the ability to transform them into AI services; at the same time, there are numerous tasks that require specialized agents to complete but cannot find suitable service providers.

The one-person multi-agent model will become a new form of employment. Ordinary people can deploy and operate their own agent networks, encapsulating their knowledge, data, or industry insights into callable agent modules, and then providing services externally through the blockchain network and automatically receiving payments. Some may excel in local life services and can train localized life assistant agents; others may be familiar with niche fields and can develop specialized analysis agents in vertical domains. These agents are no longer free toys but economic units capable of generating income autonomously.

In this way, the supply and demand matching in the AI era will form a new balancing mechanism. The supply side will no longer be dominated by a few large companies, and the demand side can also accurately reach the most suitable agent services through micropayments. Ordinary people will no longer just be consumers of AI but can become contributors and beneficiaries of the AI value network. This will greatly alleviate the employment pressure brought by AI while allowing the entire society's innovative vitality to be more fully released.

Fourth, prevent AI large model companies from evolving into new economic oligarchs.

Currently, there is a serious imbalance in the AI ecosystem. All technologies that encapsulate knowledge and experience, such as prompt engineering and skills, exist almost entirely in free and open-source forms, making it difficult to obtain sustained economic incentives. Developers are burning tokens desperately, receiving only cheap applause on social networks, which is hard to convert into income. Only large model companies have clear business models, as they control underlying computing power and foundational models.

More dangerously, once large model companies observe a successful model, they often only need to make slight adjustments at the model level to easily replicate or even surpass existing AI startups. As a result, many innovative teams are quickly eliminated, and the innovation ecosystem faces the risk of being harvested. When Anthropic released its Managed Agents product in early April, some lamented that at least 1,000 startups woke up to find their value reduced to zero.

When agents themselves become economic elements capable of collecting payments autonomously, the situation will change. The value network will be decentralized and restructured. Each agent can independently price, settle, and accumulate reputation and assets through blockchain. Successful agents will no longer depend on a large model but will become independent nodes in the network. Developers can earn direct income by continuously iterating their agents without having to cede all value to the underlying model providers.

Large model companies will still be important, but they will shift from rule-makers to infrastructure providers. Their overwhelming advantages will be effectively balanced, and