Forecasting
Better long-horizon reasoning means fewer missed signals in planning, finance, logistics, and operations.
Benchmarks for the path to ASI
RL Gym
Testing the failures between today's agents and tomorrow's autonomy.
ASI will need trust before it earns responsibility. We turn real agent failures into focused tests that show where models break, recover, and improve.
∞Frontier agents to evaluate
9High-impact datasets
169Observed failures turned into tests
36%Average score today
Why test cases
Autonomy only matters if it helps people in the real world.
The same failure that looks small in a demo can cost hours in a warehouse, delay a clinician, mislead a researcher, or break trust with a customer.
Use Cases
Where better agent testing changes outcomes
Imbalanced Classification
Better judgment helps agents flag rare but important cases without flooding people with false alarms.
Data Quality
Better data handling keeps teams from making decisions on stale facts, hidden conflicts, and invented context.
Probabilistic Classification
Better uncertainty handling lets agents verify, recover, and escalate before a guess becomes a real-world mistake.
Workflow
From real-world risk to readiness score
Fast to run. Easy to compare. Built to show whether an agent is ready for responsibility.
01
Choose the scenario
Start with a workflow that matters.
02
Configure the benchmark
Set the agent, limits, tools, and validators.
03
Watch it under pressure
See retries, traces, latency, and decisions as they happen.
04
Read the report card
Turn the run into scores, clusters, traces, and next steps.
Next step
Found the kind of failure ASI must outgrow?
Send it in.
Today's miss can become tomorrow's safety test.