Dataline provides clean web3 data to AI agents. One call returns every market, spot, perps, prediction markets, and onchain, structured and confidence-scored, so an agent acts on signal instead of noise. Instead of stitching together a dozen brittle feeds and hoping the numbers agree, an agent gets a single normalized read across venues, with a confidence score attached to every data point.

That last part matters more than it sounds. An agent making a decision does not just need data. It needs to know how much to trust each number. A confidence score is the difference between an agent that acts and an agent that acts recklessly.

Cluster is where those agents get built and run. CodeXero scaffolds a dApp or agent from a single prompt. Cluster runs it on serverless inference and GPU compute. Dataline is the live cross-venue read those agents call before they price, bid, or settle.

CodeXero lets anyone deploy an onchain app or agent from natural language, with no manual wiring. With Dataline available inside that build surface, a builder can prompt an agent into existence and have clean, multi-market data flowing into it from the start. “Build me an agent that watches perps funding and prediction-market odds” stops being a research project and becomes a prompt, because the data layer is already there to call.

Dataline is the data source, CodeXero is the build surface. No custom pipeline to assemble, the data is one call away from the moment the agent ships.

An agent is only as good as what it knows when it acts. Cluster runs agents on inference and compute, while keeping that inference private and verifiable. Dataline gives those agents the read they need before they move: normalized prices, funding, odds, and onchain state across venues, each one confidence-scored. The agent decides on clean signal, and can prove the inference behind that decision ran as claimed.

Cluster runs and verifies the agent, Dataline supplies the market read. Clean separation, each side does what it is best at.

The point is what this removes from a builder’s plate.

For builders, market data stops being the hard part. The thing that used to take weeks of feed integration becomes a call inside a prompt-built app. For agents, decisions are grounded in normalized, confidence-scored data instead of a patchwork of raw feeds that may or may not agree. For the ecosystem, this lowers the bar for a whole class of data-driven agents, trading bots, prediction-market agents, onchain monitors, that were previously too much plumbing to be worth building. 

This partnership starts with a shared thesis and a clear direction. The technical integration, wiring Dataline’s API and SDK into CodeXero so its data is callable from any prompt-built app, follows Dataline’s launch. We would rather be straight that the deep integration is ahead of us than pretend it is already behind us.

What is already true is the shape of it. Agents built on Cluster, running on private and verifiable compute, calling Dataline for the read before they act.

Dataline provides clean web3 data to AI agents. A single call returns structured, confidence-scored data across spot, perps, prediction markets, and onchain, so agents act on signal rather than noise.

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Cluster Protocol is the orchestration layer for autonomous AI on Base: serverless inference across 500+ models, GPU compute, and a tokenized data marketplace under a single API, with private, verifiable inference. CodeXero, its application layer, lets anyone deploy on-chain apps and agents from a single natural-language prompt, with x402 payment rails for autonomous per-call payments.

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