Recently, Azu has been increasingly able to sense a change: after AI agents officially take office, what they need to solve is not "whether they can do the job," but "whether they can utilize resources within the rules." It's easy to have them write a report, but getting them to complete a real task, such as setting up a temporary computing power and bandwidth for a cross-border event, prepaying advertising budgets, while simultaneously parking idle funds in more stable low-risk assets, and finally explaining every expenditure clearly, being able to retract authority at any time, and being auditable—this is the toughest threshold of the agency era.
Looking at DePIN, on-chain dollars, and RWA (such as tokenized government bonds) together, it is essentially a very realistic 'machine balance sheet.' DePIN represents the real-world resources you can rent on demand, computing power, bandwidth, storage, and sensor data can all be broken down into measurable service units; on-chain dollars represent the smoothest settlement assets for machines, suitable for high-frequency, small-value, automated payments; tokenized government bonds and similar RWAs resemble 'parking lots' in corporate cash management, providing a relatively stable destination for idle funds, avoiding wasting funding efficiency while running business. The challenge lies not in the individual existence of the three, but in orchestrating them into a controllable workflow: who can spend money, how much they can spend, where they can spend it, why they spend, how to stop when issues arise, how to reconcile, and how to explain to regulators or finance.
I prefer to understand what KITE aims to do as an 'agent scheduling layer': it does not invent new assets for you, but allows agents to use these assets and resources on the chain in a more programmable and accountable manner. The essence of DePIN is service provision, the essence of RWA is fund management, on-chain dollars serve as a settlement medium, and platforms like KITE that focus on agent payments and identities provide the 'connecting line'—binding authorization, budget, usage, settlement proof, and audit trails to the same path, allowing agents not just to buy resources, but to buy resources within organizational rules.
Take a specific but common enterprise-level task: you need to launch a wave of marketing and data collection in multiple regions simultaneously, requiring temporary computing power to run models, calling external data and risk control interfaces, and also needing to recharge the advertising platform as planned. The traditional approach is manual operations, swiping cards one by one, reconciling accounts piece by piece, and finally piecing together reports each month. After decentralization, you can break this task down into three roles: a resource agent specifically to 'rent' DePIN's computing power and bandwidth, a procurement agent who specializes in purchasing data/API services on a per-use basis, and a financial agent responsible for distributing funds from the on-chain US dollar treasury to the first two and executing settlements. The key is not in 'nice division of labor,' but in the boundaries of each agent's authority being clearly defined: limits on amounts, whitelists of payees, usage restrictions, session validity, and trigger thresholds all become hard rules, and agents can only operate within the framework; crossing the boundary results in automatic failure or manual review. As a result, DePIN resources are scheduled on demand, on-chain dollars are paid per use, and RWA assets can serve as 'low-risk destinations for idle funds,' allowing the treasury to be more efficient without sacrificing liquidity.
When you run this process, the changes in rules will be very obvious: in the past, the scheduling of resources and funds in enterprises was 'humans make decisions, systems keep records,' and when issues arose, accountability relied on emails, screenshots, and meeting minutes; the agency era is more like 'humans define boundaries, agents execute, and chains provide evidence.' What regulatory or auditing concerns will shift from 'whose address is this' to 'which organization authorized which agent, what payments did the agent complete under what rules, were there any limits exceeded, and were there payments made to unauthorized parties?' For enterprises, this represents a shift from post-event reconciliation to pre-event risk control; for the DePIN and RWA ecosystem, this is pulling demand from 'retail speculation' towards 'repeatable enterprise orders'; for ordinary developers, this is the first time they can write 'resource procurement—payment—attribution—reconciliation' as a machine-executable pipeline, rather than a vision on a PPT.
Of course, this combination will also bring new tensions, the most typical being the balance between privacy and transparency. Companies do not want competitors to see their supply chain, marketing rhythm, or resource consumption patterns from the chain, but compliance and risk control must have audit trails. A more realistic extension direction is to move “details” as much as possible out of plain text transactions, retaining necessary settlement and responsibility chains, while using stronger cryptography or proof mechanisms for selective disclosure when needed, allowing for both “compliance verifiable” and “trade secrets concealable” to coexist. For the agency economy to scale, privacy is not a decoration, but a prerequisite for continuous order generation.
Azu finally provides a feasible action guide: if you are a project party or startup team, don’t start from the most complex supply chain applications, first select the easiest standardized B-end link for a pilot, such as purchasing data/API per use, short-term renting of computing power, or small-scale ad testing, breaking it down into two roles: 'resource agent + financial agent,' first getting the limits, whitelists, and revocation paths running smoothly, then gradually incorporating the treasury's idle fund management into more stable asset positions. You will find that the value of systems like KITE lies not in putting the real world onto the chain with one click, but in giving agents the first opportunity to schedule real assets within the rules to complete complex tasks, while also clearly explaining every penny.
