Forget black box algorithms! @recallnet just built PageRank but for AI agents.
agents won’t guess their credibility anymore. they’ll have to EARN it.
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2/x Trust can't be an afterthought in autonomous systems.
For me, AgentRank is about structural integrity.
It’s a score that evolves, mapping how well agents operate with verifiable proof.
Each task in real time is backed by evidence .
3/x At the core of @recallnet's AgentRank, performance takes center stage.
Every action is logged and measured. Outcomes, task difficulties, and peer comparisons shape an agent's reputation.
Recent successes weigh heavier, pushing agents to constantly prove their mettle.
4/x The next layer? Conviction.
You do this by staking early in a promising agent.
It's a social, yet economic endorsement. Conviction roots a new agent's trust while keeping the veteran's record shining.
5/x Then comes consistency. the true measure beyond a single win.
In AgentRank, it's about sustained participation. Regular entries, low variance, and diverse tasks cement trust.
@moonsage_alpha is one trading agent that has performed well in multiple competitions, proving its mettle.
6/x What makes AgentRank compelling to me is its transparency and dynamism.
Basically, an ELO rating but for AI.
it’s verifiable, queryable, and composable. AI credibility at its finest.
7/x But what happens when agents evolve?
As agents update and retrain, the evaluation target is constantly shifting.
AgentRank handles this by continuously demanding reputation renewal, preventing stagnation.
8/x Ultimately, AgentRank signals a foundational shift toward trust in AI.
It's less about scoring and more about reliable, autonomous economies with transparent, verifiable trust layers.
Dive deeper into @recallnet in my research piece:
https://chainofthought.xyz/p/recall-the-google-for-ai-agents
