BenchGen

What is BenchGen?

BenchGen is a platform for testing AI agents before they go live in production. It builds digital twins of business systems, runs agents through multi-step tasks, and scores every decision along the way. Each run then becomes training data for fine-tuning or reinforcement learning, so teams can measure, fix, and retrain in one loop.

Top Features:

• Simulated environments: digital twin companies with CRM, ERP, database, and API connections.
• Trajectory scoring: every tool call and decision step is recorded and scored.
• Built-in agent: one chat message can benchmark, train, and retest a model.

Use Cases:

• Pre-launch testing: check that a support or finance agent finishes full workflows.
• Model selection: compare several LLMs on one benchmark to pick the best fit.
• Retraining: turn failed runs into LoRA or GRPO training data and measure gains.

Who Can Use BenchGen?

• AI engineers: developers shipping agents who need proof they handle real workflows.
• Regulated industries: defense, energy, and fintech teams needing audit trails and air-gapped setups.
• ML researchers: people building benchmarks, RL environments, and datasets for agent training.

Pricing

• Starter (free): 50 benchmark runs monthly, five environments, trajectory export, and community support.
• Pro ($49 per month): 2,000 runs monthly, unlimited environments, training data generation, and priority support.
• Enterprise (contact sales): unlimited runs, custom environments, SSO, dedicated infrastructure, and 24/7 SLA support.

Pros and Cons

Pros:

• Tests real behavior: scores full multi-step workflows instead of single prompt and answer pairs.
• Flexible deployment: runs on cloud, on-premise, or fully air-gapped infrastructure for sensitive data.
• Closed loop: evaluation results feed straight into fine-tuning without extra data work.

Cons:

• Learning curve: benchmarks, environments, and training settings take time for newcomers to learn.
• Run limits: the free Starter plan allows only 50 benchmark runs each month.
• Developer focused: most value comes through the API, which suits technical teams.

FAQs:

1) How is it different from prompt testing?

It tests whole agent workflows, including tool calls, rather than single prompts.

2) Can it run offline?

Yes, on-premise and air-gapped setups keep all data inside your environment.

3) Which agents can it evaluate?

Any OpenAI-compatible endpoint, plus OpenClaw and Hermes agents through dedicated connectors.

4) Is there a free plan?

Yes, the Starter plan is free and includes 50 benchmark runs every month.

5) Does it help train models too?

Yes, it can fine-tune with LoRA and run GRPO reinforcement learning on benchmark results.