BenchGen
Simulated environments for benchmarking AI agents on multi-step tasks, with trajectory-level scoring
BenchGen evaluates AI agents inside simulated operational environments rather than on single prompts. Agents run complete multi-step workflows against digital twins of real systems (CRM, ERP, databases, APIs) in a sandboxed runtime, and every step of the decision path is scored.
The output is trajectory data: which tools were called, what was retrieved, where the run failed. Reports cover task completion rates, per-step accuracy and failure modes by workflow stage, and the same trajectories can be exported as reinforcement learning datasets for PPO, GRPO and PRM-style training.
It supports on-premise and air-gapped deployment, which is the reason it appears in defense, energy and financial services. Pricing is not public; the site routes to sales.
BenchGen Alternatives
Explore 45 products in the Observability & Analytics category. View all BenchGen alternatives.
RAGAS
Open-source evaluation and testing framework for LLM and RAG applications
Guardrails AI
Open-source framework for adding input and output validators around LLM calls
NeMo Guardrails
NVIDIA toolkit for adding programmable guardrails to LLM conversational apps
Presidio
Microsoft open-source SDK for detecting and anonymizing PII in text and images
Also listed in
Work on BenchGen? Feature it at the top of Observability & Analytics.
Is your product missing?