BenchGen is benchmarking and evaluation infrastructure for AI agents. Its core idea is to test agents in interactive simulated environments, capture complete decision trajectories, verify final results, and turn every benchmark run into training data.
Main uses and problem background BenchGen points out that agents often perform well in demos but fail in production environments. Real business operations involve complex systems, constantly changing APIs, and multi-step processes, and agents may fail silently and cause cost losses. The problems the platform targets include: the gap between demos and production is widening; most companies lack a reliable way to measure whether AI truly improves business outcomes; in long tasks, even a 1% error rate per step can cascade in complex workflows, causing the entire task to collapse; and as agents autonomously take more actions, organizations find it difficult to monitor, audit, and govern their behavior.
Main capabilities
- Build a "digital twin company" in a simulated world, letting agents practice, fail, and learn within it, turning evaluation into training.
- Run benchmarks in a unified environment and view real-time rankings to understand the specific scores of various models and agents on the platform.
- Create isolated runtimes, with compute provided by cloud or local GPU/CPU servers; connect enterprise data sources (CRM, ERP, databases, APIs) to the simulation to make it closer to reality; support cloud or air-gapped deployment.
- Capture complete decision trajectories: the sequence of decisions an agent makes to complete a task, which tools it called, what data it retrieved, intermediate steps and final results, and score each step in the trajectory.
- Turn trajectory data into training datasets for continuous improvement processes such as supervised fine-tuning (LoRA), RLVR (GRPO), Head training, and distillation, and provide reward signals and failure mode records.
- Provide benchmark reports, including task completion rate, step-by-step accuracy scores, failure modes and where they occur in the process, comparison results across different model versions or prompt variants, and an auditable operation log for every step.
How to use
- The platform provides a "Start Evaluation" entry point, allowing the first agent capability audit to be run within minutes, discovering failure modes, capturing trajectories, and starting the improvement loop. Users can choose benchmarks to view real-time rankings; they can create environments and connect enterprise data sources before running.
Applicable scenarios and industries BenchGen is aimed at mission-critical environments where AI failure is unacceptable, covering industries such as defense and intelligence (classified networks and sovereign code), energy and utilities (critical infrastructure protection), and finance (intellectual property sensitivity and strict regulation). Its flagship use case is fintech: bank digital twins and AI agent-based simulated trading, where agents operate freely in a complete bank simulation and safely make tens of thousands of mistakes, with every step becoming training data, involving bank simulation, fraud detection, and trade execution. Simulation domains also include scientific research understanding, synthesis, and extension, software development across multiple tools, services, and frameworks, mirroring human work across key industries and functions, as well as core skills that define intelligence, scenario design for core model skills, understanding and reasoning over large-scale semantic datasets, and agent memory with context windows and toolchains.
Limitations and information not yet provided The platform can be deployed on sovereign, on-premises, and air-gapped infrastructure, and LLM weights, evaluation data, and benchmark results do not leave the controlled environment; it has been deployed in the defense organizations of NATO member states and Turkey's national GPU infrastructure. The materials do not provide specific pricing methods, prices, free quotas, the list of supported models, installation commands, or code examples; this content is not yet provided.
