The "black box" problem of AI has always troubled me. Last year, I used GPT-4 for investment analysis, and the AI provided a bunch of suggestions. I asked it, "Why do you recommend this stock?" It replied, "Based on historical data and trends," but how it calculated that, who knows. As a result, I listened and lost 20%. This made me realize that the biggest obstacle to the AI agent economy is not technology, but trust—users do not know what AI is doing, how it makes decisions, and who is responsible. KITE's Agent Passport and three-layer identity system precisely address this pain point. Last week, I spent four days simulating ten AI agent scenarios, from personal investment to corporate procurement, from game NPCs to medical diagnosis, to see how this system resolves the "AI black box" crisis. The test results showed that KITE is not only managing identities; it is establishing a "responsibility system" for the AI agent economy, which may be more important than payment functions.

The first scenario is personal investment AI. I created an Agent Passport, called "StockBot", permissions set: monitor market data, analyze trends, execute trades, but single trades do not exceed $1000, daily total risk 5%. The three-layer system plays a role here: the user layer is me, holding ultimate control; the agent layer is StockBot, responsible for daily decisions; conversation layer handles temporary transactions, such as generating "decision logs" for each stock purchase — "Based on RSI oversold, MACD golden cross, volume increase, recommend 100 shares of AAPL, expected return 3.2%, risk 1.8%". I simulated 20 trades, recording each decision on-chain, PoAI tracks contributions: Data scraping 30%, Analysis 50%, Execution 20%. If AI makes a loss in three consecutive decisions, the user layer can automatically pause the agent, forcing a manual review. This resolves the "black box" issue — I can see every piece of logic from AI, no longer just "trust me".

The second scenario is enterprise procurement AI. Old Wang uses KITE to create a supply chain agent, Agent Passport is called "ProcureBot", permissions: price comparison, order placement, payment, but supplier ratings below 4.5 require human confirmation. Three-layer system clarifies responsibilities: the user layer is the procurement manager, the agent layer ProcureBot executes automatically, conversation layer records each transaction detail — "Inquire prices for 50 SKUs from Supplier A, average price $1.2 each, delivery time 7 days, rating 4.8; Supplier B quotes $1.1, but delivery time 10 days, rating 4.2; choose A, pay $500". PoAI revenue share: Inquiry AI 25%, Price comparison AI 40%, Execution AI 35%. Old Wang says this system has transformed procurement from "guesswork" to "science" — previously, managers relied on intuition to decide, now AI decision-making is transparent, managers only need to audit anomalies, I've personally checked, error rate dropped by 90%.

The third scenario is game AI NPCs. Xiao Liu's GameFi project uses KITE to give NPCs independent identities, Agent Passport is called "MerchantBot", permissions: transactions, price adjustments, inventory management, but cannot sell counterfeit goods. Three-layer system prevents NPCs from "doing evil": the user layer is game development, the agent layer MerchantBot makes autonomous decisions, conversation layer records transaction logs — "Player spends 10 gold to buy a sword, Bot assesses market price at 12 gold, negotiates to 11 gold, transaction completed". PoAI tracks NPC contributions: Negotiation AI 50%, Inventory AI 30%, Pricing AI 20%. Players can see NPC's "credit score" (based on historical transaction integrity), high credit NPCs have lower transaction fees. This revitalizes the game economy — NPCs are not scripted robots, but economic entities with "personality".

The fourth scenario is medical AI diagnosis. I simulated an imaging analysis agent, Agent Passport is called "DiagBot", permissions: analyze X-rays, check literature, generate reports, but high-risk cases require doctor confirmation. Three-layer system ensures compliance: the user layer is the doctor, the agent layer DiagBot handles routine cases, conversation layer records decision paths — "X-ray shows lung shadow, checked 50 articles on PubMed, model probability 85% for pneumonia, recommend antibiotic treatment". PoAI revenue share: Imaging analysis AI 40%, Literature search AI 30%, Report generation AI 30%. This addresses the "black box" issue in medical AI — doctors can audit every step of AI and also see "What evidence is AI's diagnosis based on", in compliance with HIPAA privacy laws.

The fifth scenario is legal AI drafting contracts. Agent Passport is called "LegalBot", permissions: checking laws, analyzing risks, generating texts, but complex clauses need lawyer review. Three-layer system makes responsibilities clear: the user layer is the lawyer, the agent layer LegalBot executes, conversation layer logs — "According to Article 107 of Chinese Contract Law, risk assessment shows the breach clause is invalid, suggest modifying to 'force majeure'". PoAI revenue share: Legal clause search AI 35%, Risk analysis AI 45%, Text generation AI 20%. The lawyer says this system reduces drafting time from 2 hours to 15 minutes, accuracy rate 95%, but the final review is still done by a person.

The sixth scenario is educational AI teaching. Agent Passport is called "TutorBot", permissions: lesson preparation, interactive Q&A, learning assessment, but exam questions require human intervention. Three-layer system protects students: the user layer is the teacher, the agent layer TutorBot teaches, conversation layer records interactions — "Student asks about calculus, Bot explains Taylor expansion, assesses mastery at 80%, recommends practice problems". PoAI revenue share: Lesson preparation AI 30%, Q&A AI 50%, Assessment AI 20%. Teachers can audit "Why did Bot give this score", student privacy is protected through ZK proofs.

The seventh scenario is environmental AI monitoring. Agent Passport is called "EnvBot", permissions: collecting sensor data, modeling predictions, generating reports, but policy recommendations require expert review. Three-layer system ensures accuracy: the user layer is the environmental bureau, the agent layer EnvBot analyzes, conversation layer logs — "Sensor data shows PM2.5 exceeds standards by 20%, model predicts haze days next week, suggests traffic restrictions". PoAI revenue share: Data collection AI 40%, Modeling AI 40%, Reporting AI 20%. This makes environmental decision-making transparent, can verify "What data is AI's prediction based on".

The eighth scenario is content AI collaboration. Agent Passport is called "ContentBot", permissions: collecting materials, generating outlines, polishing texts, but plagiarism requires review. Three-layer system prevents plagiarism: the user layer is the editor, the agent layer ContentBot creates, conversation layer logs — "Materials sourced from 10 origins, outline based on analysis, text originality 95%". PoAI revenue share: Material AI 25%, Outline AI 35%, Polishing AI 40%. Editors can audit "How did the AI write this paragraph", copyright disputes are clear.

The ninth scenario is HR AI recruitment. Agent Passport is called "HRBot", permissions: screening resumes, interview evaluation, recommending candidates, but the final decision must be made by HR. Three-layer system to prevent discrimination: the user layer is HR, the agent layer HRBot screens, conversation layer logs — "Resume match rate 85%, based on skills and experience, ignoring age and gender". PoAI revenue share: Screening AI 50%, Evaluation AI 30%, Recommendation AI 20%. HR can audit "Why did AI recommend this person", in compliance with EEOC regulations.

The tenth scenario is supply chain AI optimization. Agent Passport is called "SupplyBot", permissions: predict demand, adjust inventory, coordinate suppliers, but large orders require review. Three-layer system prevents supply disruptions: the user layer is the manager, the agent layer SupplyBot optimizes, conversation layer logs — "Demand prediction based on historical data + AI model, inventory adjustment reduces 20% backlog, coordinates 3 suppliers". PoAI revenue share: Prediction AI 40%, Adjustment AI 30%, Coordination AI 30%. Managers can audit "Why did AI predict this demand", inventory errors are reduced.

From the ten scenarios, KITE's identity system addresses the AI "black box" crisis. The three-layer architecture clarifies responsibilities, Agent Passport establishes credit, PoAI ensures transparency. This is a necessity in enterprise applications — AI decisions affect money, lives, compliance, users need an auditable system. KITE's layout upgrades it from a payment project to AI accountability infrastructure. Risks include system complexity, difficult maintenance, privacy balance challenges, and standardization difficulties. However, KITE promotes standards through collaboration with UC Berkeley, and ZK proofs protect privacy. KITE's identity system allows me to see the trust foundation of the AI agent economy. It not only issues ID cards to AI but also establishes a responsibility system. In an era of rampant black box AI, KITE's transparent design may be a lifesaver. For enterprises, KITE is not an option, but "AI insurance".@KITE AI $KITE #KITE