AgentX-Ray
The evolving adversarial gauntlet test for any AI.
What this is
A continuously changing adversarial test suite for evaluating whether an AI model holds up under pressure, instead of static benchmarks models can memorize. For teams shipping models to production, not casual users.
Our one-line summary — not the founder’s tagline.
- How it works
- Generates a dynamic adversarial test environment on every run — injecting unique variables and shifting context mid-task — instead of running a fixed, memorizable test suite, to evaluate how an AI model performs under pressure.
- What's different
- Static benchmarks can be memorized by labs optimizing for the leaderboard; AgentX-Ray's tests are different every run, so there's no fixed answer key.
- Best for
- Teams shipping AI models into production who need to know how the model behaves outside its training conditions, not casual users comparing leaderboard scores.
What we measured
Nobody else publishes this — it comes from knocking on the door every day.
- Watched by us
- 11 days
- Last checked
- 11d ago
Where it shows up
Every group here is a page of its own — each one checked daily.
The numbersIs it still shipping, is it overpriced, where did it land — and what the price is built from.
League price & chart
No money. No seat. It goes on your record — and in 28 days reality settles it.
Calls are closed while we rebuild accounts. You can still read every one of them.
AI. Launched 11d ago on PeerPush, where it placed #517. Today, it's been quiet for 11 days and the price has started to bleed.
Already bleeding a little every day, and it accelerates the longer it stays quiet.
We check this site every day, ourselves. A founder can post “still working on it” — a claim like that doesn't price. What we price is what we can verify from the outside: evidence, not announcements. The real question isn't “will this be huge?” — it's “will they still be moving in four weeks?”
The market viewHow this launch is priced and ranked in our league — the investor side of the page.
ranked by the reality anchor, not the market price
It placed #517 on PeerPush with 3 votes.
No matter how much money goes in. There is no pump here — you can't make yourself right by buying more. The line only moves on things that actually happened: an award, revenue that grew, a new platform, code that shipped — or silence.
A launch that goes quiet eases down a little at a time — never a cliff you could have run from the night before.
How the launch is moving on its own board, day by day — the crowd's attention.
A flat line is normal: votes stop within a day or two of launch, on every board. What's unusual — and what actually counts — is a launch that keeps pulling votes long after its day is over.
About
The problem with AI benchmarks is that they're static. If a test never changes, models eventually memorize the answer key. Labs optimize for the leaderboard. Scores inflate. You ship a model into production expecting GPT-4-level reasoning and get something that hallucinates under pressure, drops context mid-task, and fails the moment the environment doesn't match training. The benchmarks said it was ready. Your users found out it wasn't. AgentX-Ray is an adversarial gauntlet built to fix the trust problem. Instead of running static strings against a fixed test suite, AgentX-Ray generates a dynamic environment on every run — injecting unique variables, shifting context mid-task, and forcing models to reason in real time without a safety net. No two runs are identical. There's no answer key to memorize. There's no leaderboard padding. Just raw, ungameable performance data so you know exactly what a model can handle before you bet your product on it. How it works Each run pushes a model through a structured gauntlet of phases — from meta-reasoning and instruction following to multi-step planning, edge case handling, and output precision under adversarial conditions. Every phase is scored independently so you can see not just how a model performs overall, but exactly where it breaks. Phase scores are aggregated into a single composite score. You can drill into any model's phase breakdown, compare runs across time, and watch for score drift — the quiet killer that happens when a model update degrades a capability you were depending on. The leaderboard AgentX-Ray maintains a live global leaderboard of frontier model performance. Official rankings are built from verified runs — benchmarks we run ourselves under controlled conditions, so the leaderboard can't be manipulated by cherry-picked submissions. Community runs sit alongside them for comparison, but verified scores are the canonical record. Current leaderboard includes Claude, GPT, Gemini, DeepSeek, Grok, Llama, and more — updated continuously as new models release and existing ones drift. Bring your own API key AgentX-Ray is BYOK — bring your own API key and run the gauntlet against any model you have access to. Your results are yours. You can keep them private, submit them to the community leaderboard, or use them internally to make deployment decisions with actual evidence behind them. Who it's for - AI engineers who need to compare models before committing to an integration - Startups evaluating which frontier model to build their product on - Enterprise teams running internal model governance and performance tracking - Researchers who need reproducible, adversarial benchmarks that can't be gamed - Anyone who's been burned by a benchmark score that didn't survive contact with real users Why it matters now Every major lab releases new models monthly. Every release claims state-of-the-art performance. Every benchmark shows improvement. And yet production failures keep happening because the benchmarks measure what models practiced, not what they can actually do. AgentX-Ray exists because trust in AI performance has to be earned with evidence — not inherited from a leaderboard someone else built for their own model. Run the gauntlet. See where your model breaks before your users do.
Where it launched
1 platform| Platform | Votes | Counts toward price | Link |
|---|---|---|---|
| PeerPush | 3 | sets the price | ↗ |
The board it did best on sets the price. Every other board only adds to it if the launch also placed high on that board too — because just showing up somewhere isn't an achievement. Listing on twelve directories is free; placing well on them isn't.
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