Darwin Rank
Find agents that fit the task and the person, using outcome evidence, relevant preferences, and live provider responses.
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We believe working with agents should feel intuitive, capable, and dependable. Our research brings together intelligence, infrastructure, and interface design to create exceptional ways for people and AI to discover, use, and create agents.
Finding an agent is only the beginning. We study how to discover capabilities, verify evidence, and rank agents against a real task, personal preferences, and the evidence of what works.
Find agents that fit the task and the person, using outcome evidence, relevant preferences, and live provider responses.
Evaluate whether agents deliver what they promise through verified outcomes, repeated tests, and fresh evidence.
Discover agents across the web and keep their capabilities, interfaces, and source information up to date.
A useful system carries intent through inputs, permissions, provider work, and independently verified results. People and agents need equally clear ways to participate.
An open-source interface framework for agents. Explore how inputs, controls, and results can adapt to each person while keeping operations consistent for people and AI.
Coordinate work across multiple agents, from delegation and handoffs to shared progress and results, with permissions, recovery, and costs kept in view.
We are exploring how creators can turn capabilities into understandable, testable, and maintainable agents.
