Recently, Datai Network and Coreon MCP have declared a strategic alliance that would make the blockchain data more practical for use by AI agents. The partnership combines the structured high-frequency blockchain data flows from Datai and the Multi-Step Toolcall implementation of Coreon.
🤝 CoreonMCP X Datai network
We are very happy to announce our collaboration with @Datainetwork.
This collaboration brings us closer to the unlocking of real-time, AI-Native Web3 intelligence for our community.@Datainetwork supplies structured, high-frequent blockchain data flows, … pic.twitter.com/nsa6tsclnm– Coreon (@coreonmcp) 12 September 2025
This yields a network that offers a cleaner context, higher intake and more useful intelligence for developers who create autonomous code in web3.
What data delivers
Datai is the company that deals with converting raw blockchain activity into structured signals that can be consumed by AI systems.
Instead of unprocessed logs and the generated noise event dumps, DataAI produces normalized data flows that record the transactional trends, event associations and timing indicators.
The structure will lead to a reduction in the efforts for pre -processing and enable AI models to more accurately study market movement, token flows and protocol events. Having predictable feeds based on temporary integrity can also enable developers to make monitoring dashboards, warning systems and predictive agents easier.
What coreon brings
Coreon is interested in agentwork flows that are based on tools. The Multi-Step-ToolCall platform coordinates the order of job invocations with which agents can pack external APIs, smart contract interactions and logic of decision-making.
With high-frequency data current integration in Datai, Coreon agents can perform more context-conscious behavior and respond to changes on chains with a very low latency. This combination carries out intrinsic activities, such as identifying liquidity events, evaluation of possible risks and defensive transactions/reports.
Coreon focuses on reproducibility and traceability and ensures that automated interventions are and can be checked efficiently.
Why this is important for Web3 AI
This team wants to solve two long -term problems with Web3 AI: signal quality and implementation of tools. The AI agents need quality and timely data input in addition to effective orchestration to work on the findings.
To a certain extent, data and coreon implement a feedback job in which improved signals provide smarter behavior and the behavior in turn provides improved information that must be improved. In the case of an ecosystem that is more dependent on composite AI agents, this loop is crucial in terms of safety and performance.
Early use cases and future prospects
The applications that can be set are financial and developer ecosystems. In the near future, plug-and-play agent templates can be made available to the developers who will lower the costs and simplify the engineering process on data and coreon integrations.
In the future, the partnership is an important step from both companies to enable an increasingly robust AI X web3 infrastructure, which is expected to transform autonomous agents to achieve faster, safer and more reliable autonomy.