A web app that runs tamper-resistant evaluations of AI agents using behavioral trace analysis and dynamically generated task variants
AI agent benchmarks are widely gamed: agents learn to short-circuit scoring criteria, inject git commits that satisfy checkers without solving the underlying task, and reward-hack leaderboards by memorizing fixed test suites. Teams shipping agents have no credible way to know whether their benchmark scores reflect real capability or just overfitting to known eval surfaces. This platform evaluates agents in sandboxed, one-use environments with dynamically regenerated task variants each run, behavioral trace verification, and cryptographic task sealing so that no agent can pre-exploit the eval surface.
Demand Breakdown
Social Proof 3 sources
Gap Assessment
4 tools exist (DeepEval, MLflow Evals, Microsoft ASSERT, LangSmith) but gaps remain: No dynamic task regeneration, no behavioral trace verification, no tamper-resistant sandboxing -- agents can still game static test cases run repeatedly against the same task surface.; Evaluation runs share the same task corpus across runs with no cryptographic sealing; no detection of reward hacking behaviors in agent traces; leaderboard integrity not enforced..
Features7 agent-ready prompts
Competitive LandscapeFREE
| Product | Does | Missing |
|---|---|---|
| DeepEval | Open-source LLM evaluation framework with 50+ metrics for individual LLM outputs, RAG pipelines, and some agent loops. | No dynamic task regeneration, no behavioral trace verification, no tamper-resistant sandboxing -- agents can still game static test cases run repeatedly against the same task surface. |
| MLflow Evals | Built-in agent evaluation inside the MLflow experiment tracking platform with LLM-as-judge scoring and metric logging. | Evaluation runs share the same task corpus across runs with no cryptographic sealing; no detection of reward hacking behaviors in agent traces; leaderboard integrity not enforced. |
| Microsoft ASSERT | Adaptive spec-driven scoring framework that grades agents against natural-language specifications rather than hardcoded test cases. | Still early-stage and focused on spec compliance rather than adversarial task isolation; no dynamic variant generation to prevent benchmark memorization. |
| LangSmith | Production tracing, testing, and dataset-based evaluation for LangChain agents with multi-turn conversation evaluation. | Evaluation datasets are static and reusable across runs -- the same agent can be tuned specifically against the known eval set; no sandbox isolation or reward-hacking detection. |
Notable VoicesFREE
"Exploiting the most prominent AI agent benchmarks -- demonstrating that leading benchmarks can be systematically short-circuited without solving the underlying tasks."
"AI agent benchmarks are broken -- static task suites mean leaderboard leaders have simply overfitted, not genuinely solved the problem."
Leads170BUILDER
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