Artificial intelligence is approaching a critical threshold in finance. AI systems are moving beyond analyzing information and recommending decisions toward holding value, initiating payments and executing transactions under delegated authority. Yet today’s financial market infrastructure was largely designed for human-paced activity, not autonomous agents transacting continuously in fractions of a second.

Alexis Sirkia, Chairman and Co-Founder of Yellow, argues that intelligence is no longer the principal constraint. The more pressing challenge is building financial rails that combine machine-speed settlement with verifiable identity, tightly scoped authorization and enforceable economic safeguards. In his view, payments and machine-to-machine micropayments will emerge first, followed by narrowly defined applications in treasury management and liquidity provision.

In this written interview with Korea IT Times, Sirkia explains how collateral-backed escrow and decentralized clearing could reduce counterparty risk without placing assets in the custody of a central intermediary. He also examines who should bear responsibility when an autonomous agent causes harm and why South Korea’s strengths in fintech, AI and digital asset participation could give the country an early advantage—provided its regulatory framework keeps pace with the technology.

Q1: AI agents are beginning to move beyond information analysis toward independently executing transactions. How do you define an AI-driven financial agent, and how close are we to seeing such agents operate meaningfully in real-world markets?

Alexis Sirkia: I'd define an AI-driven financial agent as software that can hold value, decide on a transaction and execute it end to end without a human approving each step. Plenty of tools today can analyze markets; far fewer can actually hold and move funds autonomously—the infrastructure to do that safely is still maturing.

We're closer than most assume, but not yet at scale. The building blocks—programmable wallets, on-chain identity and escrow—are maturing fast. What's missing isn't intelligence; it's financial rails built for machine speed rather than human pace. I expect the first deployments to be narrow and constrained, rather than involving general autonomous market participation.

Q2: Which areas—trading, payments, treasury management or liquidity provision—are likely to see the earliest practical adoption of autonomous financial agents, and why?

Alexis Sirkia: Payments and machine-to-machine micropayments will move first—the use case is narrow and the risk is bounded. An agent paying fractions of a cent for compute or data presents a smaller trust problem than one making discretionary trades with real capital.

Treasury management and liquidity provision will follow under narrow, rules-based mandates. Fully open-ended autonomous trading requires the most trust and regulatory clarity, so I'd expect that to come later rather than sooner.

Q3: Traditional financial infrastructure was designed primarily for transactions initiated and supervised by humans or institutions. What technological changes are needed to support markets in which AI agents can transact continuously and at machine speed?

Alexis Sirkia: The problem is economic as much as it is technical: card and bank rails carry fixed processing costs that make sense at human scale but break down at machine scale—a fraction-of-a-cent payment can't absorb a much larger processing fee.

What's needed is settlement built for continuous, high-frequency, low-value transactions: near-instant finality, fees that scale down to fractions of a cent and enforcement built into the protocol rather than handled by humans after the fact.

Q4: As AI agents begin trading with one another, how could decentralized clearing and settlement systems help manage counterparty risk, transaction speed and capital efficiency?

Alexis Sirkia: Counterparty risk is the crux of agent-to-agent trading. Strip away human relationships and reputation, and you need something that enforces good behavior mathematically. Collateral-backed escrow does that: both sides post collateral upfront, and the protocol enforces the agreed terms economically rather than relying on trust or legal recourse afterward.

That's the architecture we've been building at Yellow. Clearing happens off-chain through signed, version-numbered state updates between the two parties, and the blockchain is used only to settle the net position or arbitrate a dispute. This means two agents can clear at high frequency without every transaction touching a base layer, and neither side ever hands custody of funds to an intermediary that could freeze them, fail or be compromised. The capital efficiency follows from the same design: no overcollateralization through a trusted middleman and no multiday settlement cycle.

Q5: Autonomous agents may need to hold funds, make payments and settle transactions without constant human intervention. What kind of identity, authorization and payment infrastructure would be required to make this model secure and commercially viable?

Alexis Sirkia: Three layers: verifiable identity, so an agent can prove what it's authorized to do without a human vouching for it each time; scoped authorization—session-based access with hard limits enforced at the protocol level, not just through app policy; and programmable settlement, with funds held conditionally and released only when the agreed conditions are verifiably met.

Commercial viability matters as much as security: none of this works if fees or latency make routine agent transactions uneconomical.

Q6: AI-driven transactions also raise questions about accountability, cybersecurity and market manipulation. If an autonomous agent makes an erroneous or harmful transaction, where should responsibility lie, and what safeguards should be built into the system?

Alexis Sirkia: Responsibility rests with whoever deployed and authorized the agent—similar to how liability works for a human employee acting within their role. The agent isn't a legal actor; the party that gave it capital and authority is accountable.

But accountability shouldn't rely purely on legal recourse after the fact. Harmful behavior should carry an immediate economic consequence—collateral that can be forfeited, spending limits enforced cryptographically and rapid revocation if an agent is compromised. The goal is economic enforcement at the protocol level, not a promise that nothing will go wrong.

Q7: Looking ahead three to five years, how do you expect AI agents to change the structure of digital asset markets, and what opportunities could this shift create for technology and financial markets in Asia, including South Korea?

Alexis Sirkia: The biggest structural shift will be from periodic, human-paced settlement to continuous, machine-speed settlement, with value moving constantly in small increments rather than scheduled batches. That changes how liquidity is priced and how quickly markets respond.

For South Korea specifically, this is a significant opportunity, given its strengths in both fintech infrastructure and AI development. Markets that establish clear regulatory frameworks for agent-held funds early will be better positioned to host this activity rather than seeing it developed elsewhere first. South Korea's sophisticated retail crypto market and strong technology sector give it a genuine head start—if regulation keeps pace.

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