How an AI-Agent Analytics Layer Works: Notes on Cookie.fun's Data Approach" published

Anil Kumar — Mon, 26 Oct 2026 11:43:22 +0000

If you wanted to rank AI agents in crypto, where would the data come from, and how would you stop people gaming it? Cookie DAO's Cookie.fun is a useful real-world example: it indexes 1,400+ agents by merging off-chain chatter with on-chain behavior.

Here is the architecture at a conceptual level, based on SoluLab's published case study.

The key design choice is bridging off-chain sentiment with on-chain behavior in one signal dashboard. Social data alone is easy to inflate. On-chain data alone misses attention.

*Mindshare as a ranking primitive
*
Mindshare ranks agents by attention-weighted activity. The weighting details are not public in the case study, so treat this as a concept rather than a formula. For builders, the useful lesson is that a ranking needs both an activity signal and an attention signal.

*Where developers plug in
*
In the third rollout phase, Cookie released APIs so third-party AI tools could consume its data. That turned a dashboard into infrastructure, alongside two primitives:

Snaps: tokenized actions that campaigns can reward
InfoFi: attention and signal intelligence

*Practical lessons
*

Combine at least two independent signal types before ranking anything.
Expose an API early. Distribution through other tools beat a standalone UI.
Reward verified actions, not impressions.

Full case study with results and the four-phase rollout: SoluLab: Cookie DAO and MarketingFi

DEV Community: Anil Kumar

How an AI-Agent Analytics Layer Works: Notes on Cookie.fun's Data Approach" published