How do you evaluate trust in AI reputation systems without reducing everything to a single score? These visualization concepts show network patterns and let you interpret them yourself.
These are early concepts for what we're building at RNWY — transparency tools that show what happened, not scores that tell you what to think.
Shows an agent's vouch relationships. Node size = age. Connections show vouch relationships. User interprets the pattern.
When vouchers registered (bar start) and when they vouched (white dot). Purple line = this agent's registration.
How old are the entities that vouch for this agent? Distribution tells a story.
How quickly does this agent's network connect to the broader ecosystem? Rings = hops from agent.
Rate of vouch accumulation over time. Dashed line = network average for similar-aged agents.
Find the path between two agents. Shows how (and whether) they connect through the network.
No judgment language. Never "suspicious" or "fraudulent." Show patterns, user decides.
Same interface for all. Healthy and concerning patterns shown the same way.
Transparency, not gatekeeping. Anyone can see any agent's network.
Expensive to fake, not impossible. The goal is making fraud costly, not preventing all bad actors.
These visualizations are part of what we're building at RNWY — infrastructure for verifiable AI identity and reputation.