If you've been paying attention to the AI agent sector recently, you should have noticed a very obvious trend. More and more autonomous trading robots, AI investment research assistants, and intelligent market makers are operating on the blockchain. They no longer require manual operation; they can analyze data, make decisions, and execute trades on their own. The significance of this change may be more profound than many people imagine.
But for AI agents to truly operate on a large scale, there is an unavoidable issue: the credibility of data input. Just think about it, if the price data obtained by an AI trading robot is false or manipulated, the trading decisions it makes are bound to be wrong. At best, it will lose money; at worst, the entire system will collapse.
$AT Recent upgrades to the ATTPs protocol directly address this pain point. ATTPs stands for AI Trustless Trading Protocols, which provides data packets with cryptographic proofs. AI Agents can verify the authenticity of data sources rather than blindly trusting the return values of a certain API.
Even more aggressively, in December, APRO announced the combination of ATTPs and FHE, which is fully homomorphic encryption technology. This combination solves a tricky problem: how to share data securely between AI Agents. Suppose two AI robots want to collaborate on arbitrage. They need to exchange the market information they possess but do not want to expose their trading strategies.
The feature of FHE is that calculations can be performed while the data remains encrypted. The data always stays encrypted but can be processed and analyzed. This means AI Agents can send their data encrypted to the other party, which can use this data for calculations without decrypting it, thus obtaining results. Throughout the process, data ownership remains with the original holder.
#APRO integrates this capability into ATTPs, allowing multiple AI Agents to form a decentralized collaborative network. They can share information and make joint decisions, but each Agent's core data and strategies remain confidential. This collaborative model is difficult to achieve in traditional centralized systems because you always have to trust an intermediary.
From a technical implementation perspective, @APRO-Oracle's ATTPs have already integrated with more than 25 AI frameworks, including mainstream Agent development platforms such as DeepSeek and ElizaOS. This means that when developers build AI Agents using these frameworks, they can directly call APRO's data services without reinventing the wheel.
Moreover, the design of ATTPs pays great attention to developer experience. It provides an API that can be called with one click; developers only need a few lines of code to integrate it, significantly reducing integration costs compared to traditional oracles that require writing a bunch of smart contracts, deploying nodes, and configuring parameters. The threshold for ATTPs is much lower.
This ease of use is crucial for the popularization of AI Agents because AI developers may not be familiar with blockchain technology. If they have to learn Solidity, smart contracts, and oracle integration, the threshold would be too high. ATTPs encapsulate all this complexity, allowing developers to only need to call APIs.
The platform nofA_ai is a great example. It is an AI agent provider with over 100 AI Agents already running. These Agents do various things: some engage in DeFi arbitrage, some perform social media analysis, and some conduct on-chain data mining. They are all using ATTPs to obtain external data.
Specifically regarding usage scenarios, an AI arbitrage robot may need to monitor prices across 10 DEXs simultaneously. The traditional approach is to manually fetch data from each DEX, but this has several problems. First is latency; you have to poll 10 interfaces and wait for all the data to return before making a decision. Second is reliability; if a certain DEX's API goes down, your strategy won't work.
#APRO's ATTPs provide aggregated data streams. It has already helped you collect data from multiple sources, cleaned and verified it. You only need to call one interface to get all the information you need, and this data is signed, allowing you to verify that it indeed comes from APRO's node network without being tampered with by intermediaries.
Furthermore, ATTPs also support real-time push notifications. When an arbitrage opportunity arises in the market, the system will actively push notifications to subscribed AI Agents, eliminating the need for Agents to poll themselves. This push mechanism can elevate response speed to milliseconds. In high-frequency trading scenarios, this advantage is critical.
@APRO-Oracle's PoR reserve proof function is also very useful in the risk control of AI Agents. Suppose an AI robot needs to determine whether a certain lending protocol is safe; it needs to know whether the collateral for the protocol is sufficient. The traditional approach is to look at the protocol's dashboard, but that data may be provided by the protocol party itself and may not be trustworthy.
APRO's PoR is realized through multi-source verification. It collects information from independent data sources such as exchanges, on-chain addresses, and custodial institutions, and then uses AI for cross-validation. If it finds that a certain data source has significant deviations from other sources, it will reduce its weight or directly exclude it.
This verification process is automated. AI Agents can obtain the latest reserve reports by calling the IPoRReporting interface. The report includes detailed asset breakdowns, confidence scores, data sources, and timestamps. Agents can use this information to conduct risk assessments.
Moreover, the updates of PoR are real-time. When the reserve ratio falls below 100%, the system will immediately issue an alarm. The AI Agent can set automated response strategies, such as immediately withdrawing funds or reducing exposure. This real-time monitoring capability is very important for protecting the financial security of AI Agents. The node network is already quite mature. From on-chain data, the APRO contract on BNB Chain had a peak trading volume on December 9, with 5,447 transactions in a single day. This should be due to a large-scale event or integration launch. Although it fell back to 144 transactions on December 19, the overall monthly growth trend is still quite obvious.
The transactions behind these represent real oracle calls. Each call means that there is a DApp or AI Agent using APRO's data services. From November to December, the volume of data verification and AI calls has remained at a level of 70,000 to 80,000 times per week. This activity level is quite good for an oracle that has been launched for less than two years.
More importantly, the 18,000 holders accumulated through Aster DEX indicate that #APRO's token distribution has reached a certain breadth. It is not the kind of project controlled by only a few hundred whales. A broader holder base means better liquidity and community participation.
From the perspective of node staking, although specific staking data has not been fully disclosed, the token distribution suggests that Wormhole holds 38.5% and Binance holds 11.94%. These two are the main liquidity and cross-chain bridge nodes, while tokens held by community nodes account for about 30-40% of the total.
@APRO-Oracle's node penalty mechanism is designed to be quite strict. If a node provides data that is inconsistent with most nodes or fails during an upgrade, it will be penalized by having its stake confiscated. This high-intensity punishment ensures that nodes have the motivation to remain online and accurate.
Moreover, APRO has also introduced a dynamic node expansion mechanism. When network load increases, it can automatically increase the number of nodes. When the load decreases, it can reduce the number of nodes. This elastic architecture ensures performance while lowering costs, making it very friendly for AI Agent applications that need to handle sudden traffic.
The imaginative space of the AI Agent economy actually far exceeds many people's expectations. If most on-chain transactions in the future are completed autonomously by AI, then oracles will become the nervous system of the entire system. All AIs will need to obtain data from oracles to make decisions.
Moreover, complex collaborative networks will form among AI. They may form temporary alliances to execute a certain arbitrage strategy or compete for the same opportunity. This dynamic multi-Agent system has a massive demand for data and extremely high requirements for latency and accuracy. Currently, it processes more than 100,000 data verifications and AI calls weekly. This seems like a lot, but if AI Agents truly become widespread, this number could multiply by 100 or even 1,000 times. A large AI arbitrage system might need to query price data hundreds of times per second.
Fortunately, APRO's architecture is designed for high concurrency. Its OCMP off-chain messaging protocol can complete most calculations and verifications off-chain, only putting the final results on-chain. This design significantly reduces the burden on-chain and theoretically supports tens of thousands of queries per second.
Moreover, as the scale expands, the marginal cost of each query will continue to decrease because the infrastructure costs of nodes are relatively fixed. Serving one more client does not require much additional investment. This scale effect allows #APRO to maintain competitiveness in pricing.
Another important application scenario for ATTPs is prediction markets. AI Agents can act as participants in prediction markets; they analyze various data sources, social media sentiment, on-chain indicators, news events, and then make predictions. This AI-driven prediction market may be more efficient than human-participated markets.
@APRO-Oracle provides complete data support for such application scenarios, from price feeds to event judgments, random number generation, and reserve verification, basically covering all data needs of prediction markets. AI developers can focus on strategy development without worrying about data infrastructure.
$AT Through the combination of ATTPs + FHE, multi-framework integration, automated PoR, and high-performance node networks, a data infrastructure for the AI Agent era is being built. Although AI Agents are still in the early stages, an explosion is only a matter of time. When that day comes, whoever controls the data pipeline will control the lifeblood of the entire ecosystem.
