I was tracking AI agents to see which one would recover fast and make an ATH after that bloody correction post-$TRUMP launch, and I found @AcolytAI which led me to research more about it. I went through docs, found them very clean and precise, and they’re actually solving a major pain point of AI agents by building a Data Oracle that could be used by AI agents through API to get access to a quality data pipeline based on their domain-specific purpose. Think about having an onchain data pipeline being opened for every agent to use at less than 1% of the cost they might pay if they decided to integrate APIs from the direct source, and Acolyt doesn’t only provide data, but it also provides training on it. $ACOLYT is the work token for the B2B use case where devs need to pay in $ACOLYT to access APIs. On the B2C side, Acolyt is also building their own terminal, and they’re launching staking soon, which could also offer additional benefits like airdrops from their partner agent tokens. They’ve added 10+ agents to their ecosystem so far who are going to use their APIs for data scraping and insight generation. @DTRXBT is a notable one from their ecosystem. They will buy back 20% of tokens after 70% of tokens are staked, activating a decent flywheel model that aligns all stakeholders — stakers, businesses, and the broader ecosystem — while enhancing $ACOLYT token value through a cycle of incentives, data services, and buybacks. Disclaimer: Its not a sponsored post, and I bought some $ACOLYT at 50M market ca. The sole purpose of this post to share something interesting i came across. You should access project docs to get more understanding the project, understand the risk before making any financial decision. AI Agents are very risk investment in nature so you could even loss 100% of the capital at the worst case scenraio, and you should keep that risk in mind before taking a call.