Top 10 AI tools to evaluate ai systems

Describe your AI flow in plain English ("user asks for a refund and the agent processes it through the support tool") and define what "good" looks like: tool calls, refusals, latency budget, semantic match. We verify…

path to safe superintelligence runs through offensive AI. Every failure surfaced is intelligence we didn't have yesterday. A new failure mode found leads to a new guardrail trained. The faster we break AI, the faster AI gets…

Your agent runs with test versions of the LLM and the services it calls, and Opslane checks what it actually did: what it stored, what it sent.

Evaluator: pass@k evaluation is performed on the task. The evaluator also combines the environments, and verifies this process by matching the final state of the model with the expected state using the oracle trajectory on…

environments grounded in real enterprise data that push your models beyond synthetic benchmarks. Enterprise AI Teams Digital twins that let you evaluate agent performance against realistic company environments before going…

Break down LLM spend by user, feature, model, and environment. Know which parts of your app drive 80% of the bill. Run automated evals LLM-as-judge, custom Python evaluators, and rule-based checks. Run on production traffic…

Run automated evals on every output. Establish quality baselines before you have enough users to notice problems manually. Iterate on prompts fast Edit prompts from the dashboard. A/B test variants. See quality impact…

Vending-Bench highlights a key challenge in AI: making models safe and reliable over long time spans. While models can perform well in short, constrained scenarios, their behavior becomes increasingly unpredictable as time…