# BenchGen

> BenchGen is benchmarking infrastructure for AI agents — a simulation platform that evaluates agents inside interactive environments, captures full decision trajectories, verifies outcomes, and turns every benchmark run into training data.

BenchGen tests agents, not prompts. It simulates the real systems your agents interact with, records every decision step as a trajectory, scores behavior deterministically, and generates RL-ready datasets — closing the loop between evaluation and continuous improvement.

## Key Facts

- Company: BenchGen
- Category: AI agent benchmarking and evaluation infrastructure — simulation environments, trajectory capture, verifiable rewards, and training data generation.
- Core product: Evaluation platform that runs AI agents through multi-step simulated workflows, scores every decision in the trajectory, and outputs audit-ready benchmark reports.
- Pricing: Starter (free, 50 runs/mo), Pro ($49/mo, 2,000 runs/mo), Enterprise (custom, unlimited runs).
- Deployment: Cloud, on-premise, air-gapped, and sovereign environments supported.
- Customers: Government, defense, fintech, energy, education, and enterprise AI teams.
- Deployments include NATO-member defense organizations and national GPU infrastructure in Türkiye.
- Co-founders: Andrii Bidochko (PhD in AI Systems; published research on long-horizon LLM agents in Elsevier's Journal of Computational Science; 90+ software projects, 13+ years in software products), Tolga Dincer (AI-native cloud architecture, digital sovereignty specialist), Ruslan Synytsky (Serial entrepreneur, Java Champion, founder of Jelastic PaaS acquired by Virtuozzo in 2021).

## Core

- [BenchGen](https://benchgen.com/): Benchmarking infrastructure for AI agents — simulate, evaluate, and train in one platform.
- [About BenchGen](https://benchgen.com/about): Team, mission, and why BenchGen was built.
- [For Agents](https://benchgen.com/for-agents): Agent-native access — benchmarks directly consumable by AI agents via REST API and skill.md, no browser or login required.
- [llms-full.txt](https://benchgen.com/llms-full.txt): Full-text reference for single-fetch ingestion by AI answer engines.

## Platform

- [Home — Features Overview](https://benchgen.com/): Simulation environments, trajectory capture, verifiable rewards, and the agent learning loop.
- [Hermes Agent](https://benchgen.com/hermes): BenchGen's native agent runtime — multi-step task execution, tool use, and evaluation harness.
- [Skill Checker Tool](https://benchgen.com/tools/skill-checker): Free tool to evaluate AI agent skill coverage across task categories.
- [Leaderboard](https://benchgen.com/leaderboard): Public benchmark leaderboard ranking AI agents across evaluation categories.

## Use Cases

- [Case Studies](https://benchgen.com/case-studies): Real-world deployments and customer outcomes across defense, fintech, energy, and enterprise AI.

## Pricing

- Starter: Free — 50 benchmark runs/mo, 5 evaluation environments, trajectory capture & export, community support, basic failure analysis.
- Pro: $49/mo — 2,000 benchmark runs/mo, unlimited environments, full trajectory datasets, verifiable reward configs, auto-generated training data, priority support.
- Enterprise: Custom — unlimited benchmark runs, custom environment builds, full audit trail & compliance, dedicated infrastructure, SSO/SAML, 24/7 SLA support.

## Guides

- [AI Agent Benchmarking Guide](https://benchgen.com/guides/ai-agent-benchmarking-guide): End-to-end guide to benchmarking AI agents in real operational environments.
- [AI Agent Reliability Guide](https://benchgen.com/guides/ai-agent-reliability): How to evaluate and improve AI agent reliability before production deployment.
- [RL Environments for Agent Evaluation](https://benchgen.com/guides/rl-environments-agent-evaluation): How reinforcement learning environments are used to evaluate and train agents.

## Glossary

- [AI Agent Glossary](https://benchgen.com/glossary): Definitions of key terms in AI agent evaluation, benchmarking, trajectory capture, and reinforcement learning.

## Blog

- [Blog](https://benchgen.com/blog): Insights on AI agent evaluation, benchmarking methodology, trajectory-based training, and the agentic economy.

## FAQ

### What is AI agent benchmarking?
Benchmarking is a structured evaluation process that measures how well an AI agent performs on real tasks, under real conditions, against defined success criteria. Unlike simple prompt tests, benchmarking runs agents through complete multi-step workflows — tool calls, data retrieval, decision sequences — and scores every step. Think of it as QA for intelligent systems.

### Why isn't standard LLM evaluation enough?
Standard LLM benchmarks like MMLU or HumanEval measure isolated prompt-response quality. When a model operates as an autonomous agent, it must call tools, retrieve context, make sequential decisions, and complete multi-step workflows. A model that scores 90% on academic benchmarks can still hallucinate tool calls or fail midway through a workflow. Trajectory-based benchmarking captures the full decision path.

### What is trajectory-based evaluation?
A trajectory is the complete sequence of decisions an AI agent makes while completing a task: which tools it called, what data it retrieved, what intermediate steps it took, and what the final outcome was. BenchGen captures full decision trajectories and makes every step auditable — not just the final answer.

### What kinds of agents can BenchGen benchmark?
Any LLM-powered agent that must complete multi-step tasks in an operational environment: DevOps and infrastructure automation, customer service and conversational agents, financial and compliance agents, energy and industrial operations, education and workflow assistants, and sovereign or air-gapped AI systems.

### Can BenchGen be deployed in air-gapped or sovereign environments?
Yes. BenchGen is designed for sovereign, on-premise, and air-gapped infrastructure. LLM weights, evaluation data, and benchmark results never leave your controlled environment. Deployed in NATO-member defense organizations and on national GPU infrastructure in Türkiye.

### How does BenchGen support reinforcement learning?
Every agent execution produces structured trajectory data compatible with PPO, GRPO, and PRM-style training methods — reward signals, preference pairs, and failure-mode records. BenchGen is both an evaluation tool and a data generation engine for continuous agent improvement.

### How is BenchGen different from other evaluation tools?
Most evaluation tools test prompts. BenchGen tests agents. The difference is scope: prompt-level tools measure output quality on a single turn; BenchGen measures behavioral reliability across complete operational workflows, integrates simulation environments, produces RL-ready trajectory data, and supports sovereign deployment.

## Optional

- [Changelog](https://benchgen.com/changelog): Product updates and release history.
- [Privacy Policy](https://benchgen.com/privacy)
- [Terms of Use](https://benchgen.com/terms)

_Last updated: 2026-06-29._