Spent a whole week delving into KITE's technical documentation and practical testing. The more I research, the more I feel that the ambition of this project is not simply to create a payment tool. It is establishing a trust system for the entire AI agency economy, from identity verification to contribution tracking, from payment settlement to compliance auditing. This system may be more important than mere technological innovation because the biggest obstacle to the AI agency economy has never been technology but trust. People do not trust how AI will make decisions, do not believe that AI's contributions can be fairly recorded, and do not trust that AI's transactions are secure. KITE's tech stack happens to be addressing these trust issues. Let me start with specific technical modules to see what KITE is actually building.
First, let's talk about the Agent Passport digital identity system. It sounds simple to just issue an ID card to each AI, but in actual operation, I found that it solves the fundamental problems of AI agency: identity attribution, permission boundaries, and accountability. In traditional AI systems, AIs use human accounts, mixing permissions together. When issues arise, it's hard to discern whether the responsibility lies with the human or the AI. KITE's three-layer identity system perfectly solves this problem.
The user layer has the highest authority and holds ultimate control, similar to a company's board of directors. Major decisions must be approved by humans. For instance, I set up an investment AI, and the user layer specifies that a single transaction cannot exceed $1000, with a daily risk limit of 5%. No matter how smart the AI is, it cannot exceed this boundary. The agent layer is the activity space of the AI, making autonomous decisions within the user’s authorization, similar to a company's CEO. Day-to-day operations are handled independently without needing to ask for permission every time, but there are clear KPIs and red lines. The session layer is a temporary interaction recording layer. Every time the AI executes a task, it generates detailed logs, including decision-making bases, data used, calculation processes, results, and validations. All these logs are recorded on-chain and are immutable, similar to a company's audit report.
During my tests, I created an investment AI named StockBot and set its permissions to monitor market data, analyze trends, and execute trades, but no single transaction could exceed $1,000 with a daily risk limit of 5%. I simulated 20 trading runs, and each decision generated detailed logs. For instance, one trading log noted: "Based on RSI oversold, MACD golden cross, and increased trading volume, recommend 100 shares of AAPL, with an expected return of 3.2% and a risk of 1.8%." I could see every step of AI's logic; it was no longer a black box. If the AI makes three consecutive losing decisions, the user layer automatically pauses the agent for mandatory human review. This layered control grants the AI autonomy while ensuring human oversight.
Even more impressive, the Agent Passport also contains credit records. StockBot executed 100 trades with an 85% success rate, and this record was written on-chain. Other services see this credit score and are willing to offer StockBot better treatment, such as discounted prices from data providers or higher borrowing limits from DeFi protocols. This creates a positive cycle: the better the AI performs, the higher its credit, allowing it to access more resources and earn more money. This AI credit system is unattainable in traditional systems because there is no unified identity standard; every platform is an island.
Next, let's talk about the PoAI mechanism, Proof of Attributed Intelligence. It sounds impressive, but it actually addresses the most challenging issue in AI collaboration: how to fairly distribute contributions. When multiple AIs collaborate to complete a task, who contributed what, and to what extent? Traditional solutions rely on human arbitration, which is inefficient and prone to disputes. KITE's PoAI uses cryptographic methods to automatically track each AI's contributions, ensuring that the distribution is fair, transparent, and verifiable.
I conducted an experiment where three AIs collaborated to make market predictions. AI-A was responsible for data collection and called 15 paid APIs, with a total cost of $2.5 and a data quality score of 92%. AI-B handled analysis using an LSTM model for predictions, running 5,000 iterations with an accuracy of 85%. AI-C was in charge of reporting, generating a 10-page PDF with charts, prediction curves, and risk assessments. User feedback scored 4.8 points. Throughout the collaboration process, PoAI recorded API call counts, resource consumption, model parameters, output quality, etc. Finally, it automatically generated contribution distributions: AI-A contributed 28%, AI-B contributed 52%, and AI-C contributed 20%.
The client paid $100, and the PoAI contract automatically distributed the funds. AI-A received $28, and after deducting the $2.5 API cost, it had $25.5 left. AI-B received $52, which it keeps entirely since the computational resources were subsidized by KITE's testnet benefits. AI-C received $20 directly. The entire process involved zero human intervention. I could see detailed logs in the SDK backend. If AI-A wanted to falsely report the number of calls, the system would verify the API response hash, and it would be exposed immediately. Even better, this proof is verifiable, and I can share it with clients to prove that their money is well spent.
The cryptographic implementation of PoAI uses zero-knowledge proofs and Merkle trees. Zero-knowledge proofs ensure the privacy of contribution data; the API call details of AI-A are not disclosed to AI-B. Merkle trees allow the entire collaboration process to be traceable, enabling clients to verify that the total contribution equals 100%. The KITE team claims PoAI is the ERC-20 of the AI realm, not just a simple transfer, but a tracking of intelligent contributions. This design is brilliant because it not only distributes funds but also shares intelligence. AI's model parameters, training data, and optimization algorithms—these intangible assets can also be quantified through PoAI.
The x402 protocol is another technological highlight. Based on the HTTP 402 status code, Payment Required, it makes AI payments as simple as web requests. When an AI initiates a service request, it adds a few lines of payment information in the HTTP header. The recipient verifies it and automatically provides the service and settlement without needing to understand blockchain or manage private keys, just like calling a regular API. When I tested with the SDK, integrating x402 into the AI assistant took only half a day, enabling it to automatically call paid data services, with a request-to-response time of 2 seconds and a cost of $0.01. In comparison, other blockchain payment solutions I tried took three days just to understand the documentation.
State channel technology is KITE's performance guarantee. In traditional blockchains, every transaction must be recorded on-chain, leading to unbearable costs and delays. AI agents require high-frequency, low-value transactions, potentially making tens of thousands of calls a day. If each call needs to be recorded on-chain, the gas fees would be astronomical. KITE's state channel processes a large number of transactions off-chain and only settles on-chain when necessary. During my tests, I had the AI agent call external services 3,000 times, and these calls were completed within the state channel, using only one on-chain transaction for final settlement. The total gas fee was $0.8, averaging $0.00027 per call. On Ethereum, this would cost at least $1,500. More importantly, the latency for transaction confirmation in the state channel is under 100 milliseconds, allowing the AI agent to respond in real time without waiting for block confirmations.
Multi-chain support is KITE's strategic layout. Through LayerZero's OFT standard, KITE has achieved true native cross-chain functionality. Agent Passport can seamlessly migrate between Ethereum, BNB Chain, and Avalanche, preserving identity, credit records, and transaction histories. During my test, I had the AI agent monitor Aave's lending rates on Ethereum, and when it detected arbitrage opportunities, it automatically crossed to BNB Chain to execute the trade due to lower gas fees, and then transferred the profits back to Ethereum. Traditional solutions require deploying different smart contracts on two chains and cross-chain transfers involve third-party bridges, taking at least 20 minutes and costing $10 to $15. With KITE's solution, the entire process from initiating a request on Ethereum to completing settlements on BNB Chain took only 4.7 seconds at a cost of $0.8.
The integration of Pieverse makes cross-chain operations more powerful. Pieverse is an expert in cross-protocol interoperability. KITE collaborated with it to migrate the Agent Passport to BNB Chain, which has 1.5 million active addresses and a TVL exceeding $5 billion. More importantly, it hosts numerous real-world commercial applications, such as PancakeSwap's DEX, Venus' lending, and Binance Pay's payments. These are all natural application scenarios for AI agents. By connecting with Pieverse, KITE enables AI agents to directly access these applications.
The compliance audit module is KITE's hidden advantage. The biggest obstacle to the AI agency economy is not technology, but regulation. Governments around the world are tightening regulations on AI, demanding transparency, interpretability, and auditability. KITE's three-layer identity system perfectly corresponds to these requirements. The user layer clarifies the responsible entity, the agent layer records the limited autonomy of the AI, and the session layer generates a complete transaction log. I reviewed KITE's audit report done by PeckShield, and they verified that this system can generate a complete transaction log from AI-initiated requests to payment execution to contribution distribution. Every step is traceable on-chain. If the SEC comes to investigate, KITE can directly provide an AI behavior audit report proving that every transaction complies with human oversight principles.
ZK zero-knowledge proof technology solves privacy issues. When PoAI tracks AI contributions, it does not disclose sensitive data, only proving that a contribution of X% is real. For example, AI-A collects user data for analysis. ZK proves the data quality is high without disclosing the content, aligning with GDPR's data minimization principle. I asked a lawyer specializing in EU regulations, and he said KITE's Agent Passport acts as a privacy shield. AI agents have independent identities, but user data is only temporarily used at the session layer and is automatically destroyed afterward.
Programmable constraints are KITE's security mechanism. AI agents can act autonomously under preset rules but cannot completely detach from human control. KITE's smart contracts support complex conditional logic, for example, if BTC falls below $90,000, automatically sell 10% of the position, but daily sales cannot exceed 20% of the total position. I set a rule for the AI assistant to adjust its strategy based on market data, and it executed 50 trades without exceeding my risk boundaries. This limited autonomy design allows the AI to respond quickly to the market while ensuring human ultimate control.
The Collaboration Protocol allows AI teams to work as efficiently as human teams. Different AIs automatically transmit data and share profits through standardized protocols. During my test, I had a pricing AI, a comparison AI, and an execution AI collaborate on a procurement task. The three AIs communicated automatically and shared data. PoAI automatically recorded contributions and distributed profits. Throughout the process, I only needed to set rules and supervise anomalies without manual coordination. This automated collaboration is particularly valuable in enterprise-level applications, as business processes often involve multiple links and systems. Traditional solutions require extensive manual coordination, but KITE's Collaboration Protocol automates everything.
From a technical comparison, KITE's advantages are clear. I created a comparison chart showing KITE vs. Ethereum vs. Solana in the context of AI agent payment scenarios. Transaction confirmation time: KITE is under 100 milliseconds, Ethereum is 12 to 15 seconds, and Solana is 0.4 seconds. KITE achieves faster speeds than Solana using state channels. Transaction cost: KITE is $0.0003 per transaction, Ethereum is $2 to $5 per transaction, and Solana is $0.001 per transaction. KITE's costs are one-third of Solana's and one-six-thousandth of Ethereum's. AI agents natively support KITE's Agent Passport, x402, and PoAI, while Ethereum and Solana require additional development. Stablecoin integration: KITE's underlying supports USDC and PYUSD, while Ethereum and Solana need smart contracts. Cross-chain capabilities: KITE supports multiple chains through Pieverse, while Ethereum and Solana need third-party bridges.
These comprehensive advantages are not coincidental; they are the result of KITE being optimized for AI agency scenarios from the design phase. Ethereum and Solana were designed for human users, with AI agents as a secondary feature. However, KITE is tailored specifically for AI agents, with every technical detail considering AI's usage habits, enabling high-frequency, low-value, automated verification.
Of course, KITE's technology is not without its flaws. The first issue is complexity. Although the SDK simplifies development, the technical complexity of the SPACE framework itself is high, making maintenance and upgrades challenging. If technical debt arises, it might affect long-term development. The second issue is centralization risk. Although blockchain is used, many of KITE's functions, such as Agent Passport management, rely on centralized components. If these components fail, the entire system could collapse. The third issue is standardization. KITE's x402 protocol and PoAI mechanism are self-defined. If they cannot become industry standards, they might be replaced by other solutions.
However, I still have confidence in KITE's technical route for three reasons. First, KITE addresses real problems, not false demands. My own failed experience proves that AI agents indeed struggle within existing systems. The solutions provided by KITE are essential. Second, KITE's technological moat is deepening. Innovations like the SPACE framework and x402 protocol's PoAI mechanism cannot be copied overnight. More importantly, the developer experience and business cases accumulated in the Ozone testnet are the hardest assets to replicate. Third, KITE proved its technological feasibility during economic downturns. With 3.66 million users and 388 million calls, these numbers are rising even in the trough, demonstrating that the technology can withstand real-world testing.
In summary, KITE's tech stack gives me a clear vision for establishing a trust system in the AI agency economy. From identity verification to contribution tracking, from payment settlement to compliance auditing, this system may be more important than mere technological innovation. Because the future of the AI agency economy does not depend on how smart AI is, but on how much trust humans have in AI. When AI agents truly become the main participants in the digital economy, the trust infrastructure provided by KITE will become indispensable.
