I have never had high hopes for this thing called the test network. In the cryptocurrency world over the years, how many projects have boasted about test networks with 'millions of daily active users' and 'billions of calls', only to reveal their true nature once they hit the mainnet—either turning out to be empty bots or fake data, discovering that the SDK is half-broken within a month, making it feel like stepping on a landmine. But last week, having nothing to do, I downloaded the KITE Ozone test network SDK and decided to give it a try myself. As a result, I couldn't stop once I started; I ran AI calls over a hundred times, simulating various scenarios, from simple data scraping to complex multi-agent collaboration, taking a full four days. The more I tried, the more I felt something was off—this test network wasn't just 'showing off its muscles,' but quietly accumulating real demands. Let me start with my first failed test to see what secrets the KITE Ozone test network is hiding and why it might become the 'low-key king' in the AI agent economy.
My first test was to create an AI arbitrage monitoring assistant. The idea was simple: let the AI agent fetch yield rate data from DeFi protocols every minute and automatically execute trades upon discovering arbitrage opportunities. It sounds like a small task, but I encountered numerous pitfalls during actual operation. Traditional solutions use Ethereum's Web3.js, and the first step to fetch data hit a snag—the API call required paying gas fees of $0.5 each time, so fetching data 100 times would cost $50. Even worse, the AI agent had no independent identity and could only use my wallet, mixing permissions together and posing huge risks. Collaboration was even more challenging; I wanted one AI to fetch data while another analyzed it, but data transfer had to be done manually, which was exhausting. As a result, this "small project" was abandoned after 10 attempts, costing $20 in gas fees with nothing accomplished.
This time using KITE's Ozone testnet, I started from scratch. After downloading the SDK, I felt different when I first looked at the documentation—it wasn't just a pile of technical jargon, but a "scenario-oriented" tutorial: "Want AI to automatically fetch data? Three steps: create an agent, set permissions, call the API." I used the Python SDK and wrote less than 50 lines of code to run the first demo. agent = kite.create_agent(name='MarketMonitor', capabilities=['data_fetch', 'analysis']) creates the AI agent; agent.authorize(budget=10, risk_level='low') sets the budget and risk; agent.fetch_data(api='defi_llama', interval='1min') starts fetching data. The entire process took 5 minutes and cost $0.01. The AI agent automatically calls the DeFi Llama API and fetches yield data every minute, with a state channel allowing payments to be settled off-chain, only being recorded on-chain during aggregation.
After running it 10 times, I realized the authenticity of the Ozone testnet—it is not just idling. KITE's Ozone is a test environment specifically designed for AI agents, and the claimed 388 million calls are not exaggerated; I checked the on-chain logs and found that most calls come from real interactions: 40% are API service calls (data fetching, model invocation), 30% are AI collaborations (one AI invoking another's capabilities), 20% are micropayments (small settlements), and 10% are testing and development. Among the 3.66 million users, 60% are developer accounts, 30% are corporate tests, and 10% are regular users. This structure indicates that Ozone is attracting real builders, not just faking activity.
I upgraded the tests and let AI agents collaboratively execute arbitrage. I created two agents: Monitor AI for data gathering and Executor AI for executing trades. Monitor AI calls DeFi Llama and Dune Analytics every minute to fetch yield rates from Uniswap and PancakeSwap. If it detects a price difference greater than 0.5%, it notifies Executor AI to execute cross-chain. Using KITE's Collaboration Protocol, the two AIs automatically transmit data and split profits. The PoAI mechanism records contributions: Monitor AI, due to its high data quality, receives 40% of the profits; Executor AI, for executing trades, receives 60%. I ran 50 simulated trades with an 85% success rate, and total costs were $0.45. Traditional solutions cost at least $50 and require manual coordination.
This collaboration showed me the depth of Ozone. KITE's SDK uses state channels to allow AI agents to handle high-frequency interactions off-chain, settling only on-chain. During testing, I discovered that the channel supports batch payments—100 calls in one transaction, with a gas fee of $0.0003 per call. Agent Passport gives each AI an independent identity, and the credit record of Monitor AI (85% accuracy) allows it to call paid APIs without needing extra deposits. Ozone's logs indicate that similar collaborative calls account for 30%, demonstrating real testing scenarios.
I interviewed several developers on Ozone and found their application scenarios to be diverse. One who works in cross-border e-commerce said he uses Ozone to optimize procurement AI, automating price comparison and ordering, with 100,000 calls per month, reducing costs by 90%. Another involved in GameFi allows NPC AI to trade autonomously, with 50,000 calls per day, and Ozone's low latency brings the game economy to life. There's also a medical AI team that collaborates on imaging diagnostics, with 30,000 calls per week, and PoAI ensures data contributions are traceable.
The growth trend of Ozone shocked me. When it launched in June, it had an average of 500,000 calls per day and 200,000 users; now it's 1.3 million calls and 3.66 million users, a six-month growth of 2.6 times and 18 times. This kind of growth in a bear market is too unusual and can only indicate that the demand is real. KITE's SDK is simple, the documentation is scenario-oriented, the community is active, and official engineers provide 24/7 support. This is rare in the crypto space, where most project documents are like ancient texts and communities are filled with spam.
From my perspective, Ozone is KITE's "ecosystem incubator." It is not just a testnet but is sowing seeds: funding over 60 projects, attracting talent with low-cost hackathons, and offering cheap talent during the bear market. Ozone's modular design—Payment, Agent Passport, Collaboration—has high reusability; I tested three modules and achieved complete AI arbitrage with 200 lines of code. Ozone also resolves the "trust issue" of AI agents. Traditional testnets have opaque data, and it’s unclear whether calls are genuine. Ozone records everything on-chain, PoAI tracks contributions, and the x402 protocol ensures transparent payments. Corporate test accounts constitute 30%, verifying ROI, which is real demand in a bear market. Risks? The challenge is whether Ozone’s data can scale to mainnet size. The modular beta stage has many bugs, high risks from centralized components, and standardization difficulties. But KITE is accumulating a network during the bear market, which serves as a moat. The Ozone testnet has shown me KITE's real strength. It is not just about inflating data but is building an "operating system" for AI agents. 388 million calls, 3.66 million users—these are countless developers exploring the AI economy. When AI agents are widely applied, KITE's Ozone experience will be an advantage. For developers, now is the right time to position themselves—at the bottom of the bear market, KITE is laying its foundation.
