Dragonfly Partner: Most Agents Will Not Conduct Autonomous Transactions, How Will Crypto Payments Win?

marsbitPublicado em 2026-03-24Última atualização em 2026-03-24

Resumo

Dragonfly partner Robbie Petersen argues that the prevailing narrative about AI agents driving massive adoption of crypto payments is flawed. He contends that most agents—whether enterprise or consumer-facing—will not engage in autonomous transactions. Enterprise agents, which will constitute the majority of agent deployments, are an evolution of SaaS and will operate within closed organizational structures. They automate internal tasks (e.g., sales, accounting, legal review) without spending autonomously. Costs for API calls or data are abstracted into bulk, pre-negotiated invoices from platform providers, not paid per transaction. Consumer agents will act more as research assistants than independent economic actors. While they will excel at coordination and discovery (e.g., finding travel options), humans will retain final decision-making and payment authorization for all but the most repetitive purchases due to the qualitative, situational nature of consumer choice. Petersen identifies a narrow third category where crypto could win: permissionless, bottom-up agents (e.g., those inspired by OpenClaw) that operate truly autonomously and require high-frequency, granular payments. For these, blockchain's key advantage is not just technical efficiency but its open, permissionless nature, allowing experimental development without regulatory hurdles. However, he concludes that the larger bottleneck to a full autonomous agent economy is not payment infrastructure but human-cent...

Author: Robbie Petersen, Junior Partner at Dragonfly

Compiled by: Guyu, ChainCatcher

Whenever an emerging narrative enters public discussion, the mainstream argument tends to be simplified into a form most easily accepted by the general public. Intuitively, when no one can empirically prove what will happen, provocation is more rewarding than nuanced analysis.

The recent discussion around the "Agent Economy" is no exception. A market consensus has formed: the number of agents will surge; agents need to conduct transactions; agents cannot hold bank accounts but can hold crypto wallets; card networks charge 2-3% fees; therefore, stablecoins win.

This chain of logic is flawed on many levels. Agents can hold bank accounts under an FBO (Financial Business Operator) structure. Furthermore, the 2-3% fee reflects credit risk and fraud risk, which blockchain does not solve.

However, the debate over "which payment method wins?" actually stems from a preliminary question that is largely overlooked in the discussion:

Will most agents actually conduct transactions?

The agent economy will be massive, but the proportion of agents that actually transact will not be.

The Agent Economy Resembles an Org Chart More Than a Market

Fundamentally, AI is an automation technology. It can perform certain tasks—such as searching, aggregating, and synthesizing—more efficiently than humans. Agents are the actionable derivatives of AI. They don't simply return outputs; they perform actual tasks.

The implicit assumption in the entire agent commerce theory is that execution comes at a cost. In other words, for most agent tasks, they need to spend money to autonomously acquire external resources, pay for compute and data on a usage basis, and interact with other agents as independent economic actors.

This is fundamentally at odds with how agents will actually be used.

Broadly, agent deployment falls into two categories: commercial agents deployed on behalf of businesses, and consumer agents that augment our personal lives. For different reasons, neither category is likely to transact autonomously.

Commercial Agents Are the Inevitable Evolution of SaaS

A plausible concept of a commercial agent is the inevitable evolution of SaaS. Rather than augmenting workflows, they replace existing workflows. Just as over 95% of software spending comes from businesses and governments, over 95% of large-scale agent applications will likely be deployed within similar organizations.

This is the first nuance the current mainstream agent commerce theory misses: the vast majority of agent demand won't be agents booking flights for consumers, but top-down deployments within businesses. More importantly, agents automating tasks within a closed organization are fundamentally different from agents operating as independent economic actors.

Take a sales agent. It plugs into the CRM, researches leads, writes personalized outreach, and schedules follow-ups. It doesn't spend autonomously, nor does it interact with external agents from other organizations. It simply performs a task—sales outreach—and automates it within a closed environment.

Intuitively, this applies to almost every organizational function. Finance agents review and reconcile expenses; accounting agents record journal entries, reconcile accounts, and prepare statements; legal agents review contracts and flag exceptions; coding agents write code.

In almost every use case, the agent itself does not spend, nor is it granted spending authority. It is deployed top-down in a controlled organizational environment with permission controls.

Even if it does require cross-organizational interaction and pays for its API calls or data, the cost likely won't manifest as the agent paying autonomously. Any pay-per-use costs would likely be abstracted away by the software vendor. This is precisely how the enterprise software stack works. Platform providers negotiate custom partnerships with data vendors, compute providers, and other infrastructure partners, bundle access into the platform cost, and pass it through as a single aggregated line item.

Furthermore, they can achieve unit economics that no single agent could replicate autonomously. Compute is acquired through reserved capacity agreements with AWS, Azure, or GCP. Model inference is priced based on volume agreements with Anthropic, OpenAI, or Google. Data enrichment is done through vendors like Bombora or Clearbit. All of this is pre-negotiated and abstracted.

In other words, an agent's 40,000 API calls, model inferences, and data queries don't generate 40,000 payments; they generate an invoice. The granularity of consumption has never been the same as the granularity of settlement, and businesses may prefer to keep it that way.

Consumer Agents Will Coordinate, Not Consume

While commercial agents might not transact autonomously because businesses won't allow it, consumer agents won't transact autonomously because people don't want them to.

Take an example proponents of agent commerce love to cite: you tell your agent to book a trip to Tokyo. It searches hundreds of hotels, cross-references reviews, checks your calendar, applies your preferences. Then, it books the room automatically. You do nothing. Proponents of the agent commerce model would extrapolate this user experience to almost every consumption category, from groceries to household goods to clothing and beyond.

The problem is that preferences aren't static. They are revealed in the act of choice itself. When you book a hotel, you're not just looking for the cheapest accommodation. The judgments you make reflect your mood, context, risk tolerance, and other qualitative factors you might not even be aware of before seeing the options.

In practice, the agent will search, ask follow-up questions, and return options. You'll look at pictures, ask about the neighborhood, maybe read a few reviews. Then you'll make a choice and authorize the agent to pay using the credit card information it already has. In other words, the agent is a research assistant, not an independent economic actor.

Beyond certain predictable, repeat purchases, this user experience will likely hold true for almost every consumption category precisely because consumer decisions are rarely based on price alone. The entire consumer economy is built on product differentiation. Whether it's clothing, hotels, household goods, or groceries, the decision-making process involves countless qualitative factors that are not only impossible for an agent to capture—more importantly, they are discovered by the user during the process itself.

Agents will play a powerful coordinating role in the discovery phase, but at the moment of truth, they will cede decision-making back to the human. Semantically, this isn't agent commerce, and it doesn't require building new payment rails.

Where Crypto Payments Truly Win: Bottom-Up Agents

While these two categories combined will likely account for over 95% of agent deployment in the next five years, there is a third category worth considering.

Over the past few months, a new type of bottom-up agent has begun to emerge. Driven by phenomena like OpenClaw, these agents belong to a qualitatively different category. Unlike the commercial and consumer agents described earlier, these are truly autonomous actors operating independently of any organizational principal. These agents need to actually pay, and they need to do so with a granularity and frequency that makes manual authorization impractical. Although the bottom-up agent economy is currently极小, it is likely to grow rapidly as new, unforeseen use cases emerge.

It is only in this extremely narrow context that the debate over whether crypto payments or card networks are the better underlying infrastructure becomes compelling. While the technical arguments for why crypto payments are superior are being enumerated, the reason they might ultimately win, in my view, is something else—permissionlessness.

Today, the reality is that neither payment method is technically optimized for agent commerce. While blockchains theoretically offer better unit economics for micropayments, they lack authentication and risk scoring—which may prove important in the future agent era. Also, while instant settlement is often cited, it simply means fraudulent transactions settle immediately on-chain. Conversely, card networks have sophisticated fraud graphs and tokenized credentials that agents could inherit, but these tools are trained on human behavior patterns and don't map cleanly to autonomous agent transactions. Furthermore, for cross-border transactions, agents would be subject to the settlement times of card networks.

Perhaps counterintuitively, crypto payment rails might become the default infrastructure for this class of agents because blockchains are open, permissionless, and unregulated.

This is their ultimate structural advantage. While I believe incumbent card networks like Visa and Mastercard will continue to adapt through initiatives like Visa Intelligence Commerce and Mastercard's AgentPay, they are, after all, public companies that must comply with regulatory obligations, meet customer onboarding requirements, and work with institutional counterparties. Blockchains have none of these constraints. Anyone can build on them, any agent can transact, and no approval is required.

Intuitively,新兴的, experimental categories develop where friction is lowest.

The Bottleneck Isn't the Infrastructure, It's Us

The longer-term question, however, is how fast this experimental development can ultimately scale to matter. The bottom-up agent economy only truly flourishes when autonomous agent organizations are demonstrably better than human organizations augmented by agents; not marginally better, but sufficiently better that the top-down human constraints on agents become a competitive disadvantage. At that point, agents cease to be mere automators of human tasks in closed environments and become the organization itself.

However, we are likely far from this future. The bottleneck won't be the technology itself. And what is truly "not built for machines" might not be the payment rails themselves, but everything else not designed for an autonomous agent economy: regulatory frameworks, institutional bureaucracy, legal structures, and the social inertia around human decision-making. These constraints are far more profound than any technical detail in the payment stack. Unfortunately, protocol upgrades can't solve for them.

The agent economy will be massive, and most of it will be billed monthly.

Perguntas relacionadas

QAccording to the article, why won't most agents conduct autonomous transactions?

AMost agents, whether deployed by businesses or for consumers, are not designed to be independent economic actors. Business agents operate within closed organizational environments with controlled permissions, and their costs are abstracted into platform fees. Consumer agents act more as research assistants that require human authorization for final decisions, rather than making autonomous purchases.

QWhat is the fundamental difference between agents operating within organizations and those acting as independent economic entities?

AAgents within organizations are deployed top-down in controlled environments to automate specific tasks without spending authority, while independent economic entities operate autonomously outside any organizational structure and require the ability to make frequent, granular payments.

QHow does the article describe the role of consumer agents in the purchasing process?

AConsumer agents primarily serve as coordinators or research assistants. They search, aggregate information, and present options, but the final decision and payment authorization are typically made by the human user, except for predictable, repetitive purchases.

QIn which specific scenario does the article suggest crypto payments could become the dominant infrastructure, and why?

ACrypto payments could become the default infrastructure for a nascent category of bottom-up, autonomous agents that operate independently. The key advantage is that blockchains are open, permissionless, and unregulated, offering minimal friction for experimental development and transactions, unlike traditional card networks that have compliance obligations and customer onboarding requirements.

QWhat does the article identify as the main bottleneck for the growth of a large-scale, autonomous agent economy, beyond technical infrastructure?

AThe main bottlenecks are non-technical constraints: regulatory frameworks, institutional bureaucracy, legal structures, and the social inertia surrounding human decision-making. These limitations are far more impactful than any technical details in the payment stack and cannot be solved by protocol upgrades.

Leituras Relacionadas

As Consensus Accelerates, What Are Young Investors Betting On?

Title: As Consensus Forms Faster, What Are Young Investors Betting On? In the rapid evolution of tech investment, a new generation of young investors is navigating a landscape where AI, robotics, commercial aerospace, and quantum computing are advancing simultaneously. Traditional investment logic based on financial models is giving way to a need for deep technical understanding and the ability to act before industry consensus forms. An analysis of trends from the "WAIC FUTURE TECH" list of young investment leaders reveals key shifts in focus. The first major trend is the movement of AI from the digital screen into the physical world. Investment is shifting from large language models and chatbots towards embodied AI, robotics, AI hardware, and edge computing. While demonstrations generate excitement, the real challenge lies in achieving scalable, reliable, and cost-effective delivery in complex real-world environments like factories and logistics. Success depends not just on algorithms but on the integration of sensors, actuators, and control systems. Second, the competitive focus for large models is moving beyond raw capability toward building an "intelligence flywheel." The goal is to create self-reinforcing systems where user interaction generates data, improving the model, which in turn enhances the user experience and attracts more engagement. Companies that successfully embed AI into workflows to create these closed-loop systems can build lasting value that isn't easily erased by the next model upgrade. Third, facing a potential bottleneck in high-quality human-generated data, investors are looking at new underlying technologies. Reinforcement learning and self-play, as demonstrated by AlphaGo Zero, offer paths for AI to generate its own experience. Scientific foundation models, which aim to build general AI capabilities for fields like life sciences and materials discovery, represent a non-consensus direction that could unlock new frontiers of knowledge and data. Finally, in deep-tech areas like quantum computing, commercial aerospace, and space-based infrastructure, patient capital is essential. These fields have long, uncertain development and validation cycles involving complex engineering, supply chains, and regulations. Investment here requires a long-term view, focusing on foundational team capabilities and the eventual emergence of market demand, even if commercial returns are distant. Collectively, these trends illustrate how young investors are adapting to a new era. They are learning to make earlier, technically-informed judgments, balance hype with real-world viability, and provide the patient capital needed to build the deep-tech foundations of the future.

marsbitHá 25m

As Consensus Accelerates, What Are Young Investors Betting On?

marsbitHá 25m

Can Japan Buy Growth with AI? Will the Bond Market Believe It?

Japan's cabinet has introduced the 2026 Basic Policy on Economic and Fiscal Management and Reform, shifting its primary fiscal target. The new framework moves away from the traditional annual primary balance goal and instead prioritizes a stable reduction of the debt-to-GDP ratio. This change is tied to a strategy of increased "responsible proactive fiscal" spending, aiming to boost long-term growth through investments in strategic sectors like AI, semiconductors, energy, and robotics. The government estimates total public and private investment in 62 key technologies could exceed 370 trillion yen by 2040. The market reaction has been mixed and cautious. While equity markets may respond to policy signals, bond markets are focused on fiscal credibility. Concerns center on whether the weakening of the clear primary balance anchor could lead to looser fiscal discipline. If investors doubt that these strategic investments will generate sufficient productivity gains, tax revenue, and nominal growth to outpace rising interest costs, they may demand higher yields on Japanese Government Bonds (JGBs). Recent volatility in the yen and JGB yields, with the 10-year yield briefly reaching 2.9%, reflects this skepticism. The success of this new framework hinges on two factors: whether Japan can achieve a nominal growth rate consistently higher than its long-term interest rates, and whether future budgets demonstrate disciplined control over bond issuance. The government's narrative is that strategic investment is essential to break Japan's cycle of low growth, aging, and labor shortages. However, the bond market will continuously assess the credibility of this plan, pricing the risk that it may represent fiscal expansion rather than a viable growth strategy.

marsbitHá 1h

Can Japan Buy Growth with AI? Will the Bond Market Believe It?

marsbitHá 1h

Misjudged A-Shares: Resilience, Expectations, and Confidence

China's A-share market recently faced selling pressure, especially in tech sectors, initially triggered by a global tech sell-off that began in South Korea. However, the article argues this is a case of "mistaken injury" and highlights the market's underlying resilience. This resilience stems from three main pillars: **1) Tech Sector Fundamentals:** Unlike Korea's market dominated by a few memory chip stocks, China's tech sector is diversified across computing, communications, electronics, and semiconductors, supported by dual narratives of global AI supply chains and domestic substitution. Core areas like optical modules and fiber optics continue to show strong earnings growth. **2) "National Team" Support:** State-backed institutions and large corporations have made significant market purchases and announced buybacks, providing liquidity and signaling confidence. This is seen as a stabilizing policy signal, often associated with market bottoms. **3) Broader Market Pillars:** Other major sectors are showing endogenous recovery momentum. Consumer stocks benefit from stabilizing CPI and signs of sector recovery (e.g., liquor price hikes). Cyclical sectors like aluminum have high earnings, potential price increases due to tight supply, and low valuations. The financial sector offers stable dividends and low valuations. The conclusion is that the sell-off was driven by external contagion, not a collapse in fundamentals. With strong policy support and recovering momentum across key sectors, the A-share market possesses the toughness to regain stability.

marsbitHá 1h

Misjudged A-Shares: Resilience, Expectations, and Confidence

marsbitHá 1h

Trading

Spot
活动图片