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

marsbitPubblicato 2026-03-24Pubblicato ultima volta 2026-03-24

Introduzione

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.

Domande pertinenti

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.

Letture associate

Anthropic Cries Wolf: Is the AGI Threat Real, or Just an IPO Story?

Anthropic has published an article titled "When AI builds itself," discussing the emerging concept of "recursive self-improvement," where AI begins to actively participate in designing, training, testing, and optimizing its own subsequent versions. The company presents internal data showing that by May 2026, over 80% of code merged into its codebase was written by Claude, its AI model. Claude's capabilities have expanded to handling complex, open-ended engineering tasks, achieving a 76% success rate in such areas, and even contributing to research processes, such as optimizing code performance and conducting AI safety experiments. Anthropic outlines an evolution from human-driven development to AI-assisted workflows, culminating in the current stage where AI agents can autonomously write, run, and delegate code. The company cautions that the path toward a "closed loop," where AI continuously improves itself, is becoming visible. It calls for coordinated global mechanisms to potentially slow or pause frontier AI development to allow safety research and societal structures to catch up. However, the timing of this warning coincides with Anthropic's preparations for an IPO, framing the narrative not just as a safety concern but also as a demonstration of Claude's advanced capabilities and its integral role in accelerating Anthropic's own R&D—creating a potential "flywheel" effect for competitive advantage. This contrasts with OpenAI's recent, more policy-oriented discussion of the same risks, highlighting the competitive dynamics in the AI industry as companies position themselves in both the technological and regulatory landscape.

marsbit49 min fa

Anthropic Cries Wolf: Is the AGI Threat Real, or Just an IPO Story?

marsbit49 min fa

BIT Research: ETF Purchases Have Slowed, Strategy (MicroStrategy) Has Slowed, What Else Can Drive Bitcoin's Rise?

Market Refocus on Inflation and Rate Expectations Weighs on Bitcoin Currently, the market is in a phase of macro-repricing dominated by inflation and interest rate expectations. Bitcoin, which previously benefited from easy liquidity and low inflation, is seeing its core bullish drivers weaken. These drivers were market expectations for interest rate cuts and strong inflows from Bitcoin ETFs and institutions like MicroStrategy (referred to as "Strategy" in the text). The logic has shifted. Recent high inflation data (e.g., CPI hitting 3.8% in a May 2026 report) has caused the market to sharply reduce its rate cut expectations for 2025 and even price in potential hikes. This is a key constraint for Bitcoin, as it lacks cash flows and is highly sensitive to rate expectations. Concurrently, institutional capital flows have slowed significantly. Following the hot CPI data, Bitcoin ETFs saw accelerated outflows, with around $4.3 billion leaving over a period. MicroStrategy's ability to keep adding substantial Bitcoin to its balance sheet is also diminishing. Together, ETF and MicroStrategy holdings total roughly $110 billion, but their momentum as growth engines is cooling. In summary, Bitcoin's current pressure stems not from its own fundamentals but from a changing macro environment. As long as inflation stays elevated, Bitcoin is likely to remain in a consolidating phase. However, historically, inflation eventually peaks. Once it recedes and rate cut expectations rebuild, institutional capital could return, potentially fueling a new and more robust recovery phase for Bitcoin.

marsbit57 min fa

BIT Research: ETF Purchases Have Slowed, Strategy (MicroStrategy) Has Slowed, What Else Can Drive Bitcoin's Rise?

marsbit57 min fa

Earning 1000 Trillion in Half a Year, 'Pocketing' 20 Million per Capita: This Round of Wealth Creation in the Korean Stock Market is Unprecedented in Scale

The South Korean stock market is experiencing an unprecedented wealth surge in 2026, with household equity and fund asset values soaring by over 1,000 trillion KRW (~$730bn) year-to-date. This translates to an average per capita wealth increase of roughly 20 million KRW, fueled by a historic 109% rally in the KOSPI index. The boom is driven by three converging forces: an AI-driven semiconductor supercycle boosting giants like Samsung and SK Hynix; the government's "Value-Up" market reforms addressing long-standing corporate governance issues; and aggressive real estate regulations that have locked capital within financial markets, preventing profits from flowing back into property. This has triggered a wealth effect, boosting high-end consumption significantly. However, the gains are highly concentrated. The two semiconductor behemoths account for over half the index's value, but retail investors own relatively low stakes in them, systematically missing the biggest rallies. Wealth and consumption benefits are skewed towards luxury goods and imported cars, bypassing mainstream retail. Further risks stem from excessive leverage, with high trading volume in leveraged ETFs, and a market sentiment heavily reliant on the AI sector's fortunes and speculative rumors. While this cycle marks a potential shift from real estate to equities as a primary wealth generator for Koreans, its sustainability, amid structural imbalances and leverage, remains a critical test.

marsbit1 h fa

Earning 1000 Trillion in Half a Year, 'Pocketing' 20 Million per Capita: This Round of Wealth Creation in the Korean Stock Market is Unprecedented in Scale

marsbit1 h fa

Trading

Spot
Futures
活动图片