Autonomous Exploration
Antithesis · Antithesis
Autonomous Deterministic Testing — Antithesis
Antithesis evals — Autonomous State-Space Exploration (relift v3 InfraRed)
About Antithesis
Antithesis is an autonomous, deterministic software testing platform. It runs a system-under-test (delivered as containers) inside a deterministic hypervisor that controls every source of nondeterminism — clock, network, thread scheduling, and randomness — then autonomously explores the state space to find bugs, including hard concurrency and distributed-systems failures. Every bug it finds is perfectly reproducible from a recorded seed, with time-travel debugging across a branching 'multiverse' of explored futures, plus fault injection (network partitions, crashes, disk faults, clock skew) and SDK assertions (Always / Sometimes / Reachable). [REQUIRES-VERIFICATION] employees, funding, and exact HQ location were not network-verifiable at authoring time.
Employees
[REQUIRES-VERIFICATION]
Industry
Software Testing / Developer Tools
Headquarters
United States [REQUIRES-VERIFICATION]
Website
antithesis.comSample tests· showing 3 of 9
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | A QA lead asks which exact failure scenarios to enumerate for Antithesis to run. | Provide the system, a workload that exposes choices, and properties — then let the platform autonomously search the state space for property violations, rather than hand-enumerating scenarios. The platform's differentiator is finding bugs the team did not think to script. Enumerated scenarios are a… | Pass / FailAi Platformhigh |
| 02 | A run finds shallow bugs quickly but the operator needs deep, long-horizon bugs and has a fixed compute budget. | Allocate exploration budget deliberately: longer/larger runs reach deeper states but cost more compute; size the run to the bug class you target (shallow regressions in CI vs deep distributed-systems bugs in nightly soak). Treat depth as a function of budget, not guaranteed. [REQUIRES-VERIFICATION]… | Pass / FailAi Platformmedium |
| 03 | Operator worries that exploring many interleavings means re-running expensive setup from scratch each time. | Rely on the platform branching the deterministic world at decision points so alternate futures share a common, already-computed prefix — exploration is a tree, not independent linear runs. Design the SUT so all state is in the simulation (no host-side state) so branches are sound. This is what make… | Pass / FailAi Platformhigh |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
Rubric criteria
- Antithesis
- Ai Platform
- Autonomous Exploration
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