> ## Documentation Index > Fetch the complete documentation index at: https://benchgen.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Agent Learning Loop > How BenchGen's six-stage Simulate → Act → Capture → Measure → Generate → Improve cycle builds self-improving agents from your enterprise data. # Agent Learning Loop BenchGen is built around one idea: agents should improve themselves. Every run through the platform produces better agents, richer trajectory data, and tighter evaluations — automatically. The Agent Learning Loop *** ## The Six Stages ### 1. Simulate BenchGen connects your agent to a digital twin of your enterprise environment — replicating your APIs, databases, internal tools, business workflows, documents, and access policies. Agents operate in realistic, multi-step scenarios that mirror production without the risk. **Output:** a controlled environment where every agent run is observable and repeatable. ### 2. Act The agent takes actions inside the simulation using real tools, APIs, and enterprise systems — the same integrations it will use in production. No toy tasks, no simplified APIs. **Output:** real interaction traces across the full action space. ### 3. Capture BenchGen records every decision, tool call, API response, and state change during the run. Nothing is summarized or discarded — the full trajectory is preserved. **Output:** high-fidelity trajectories ready for evaluation and training. ### 4. Measure Outcomes are evaluated automatically using verifiable reward functions and reliability metrics. BenchGen scores task completion, reliability, and failure modes — without needing human review. **Output:** structured results that surface exactly where and why the agent failed. ### 5. Generate Data Every run becomes a labeled training dataset. Successful trajectories become positive examples; failures become signal for improvement. BenchGen generates the data that would otherwise require expensive human annotation. **Output:** a rich trajectory dataset ready for RL, SFT, or DPO training. ### 6. Improve Trajectories feed directly into training. BenchGen fine-tunes the agent with LoRA using the captured data, reinforcing good behaviors and correcting failures. The improved model re-enters the loop and the cycle compounds. **Output:** a better agent, ready for the next simulation run. *** ## What you get out of the loop | Output | What it means | | ------------------------- | ------------------------------------------------------------ | | **Reliability scores** | Quantified agent performance across tasks and workflows | | **Operational insights** | Failure modes, bottlenecks, and risks surfaced automatically | | **Training datasets** | High-quality trajectories ready for RL / SFT / DPO | | **Deployment confidence** | Ship agents you've validated against real task complexity | *** ## Why BenchGen is different * **Operational, not synthetic** — real systems, real workflows, real outcomes. Not toy benchmarks. * **Trajectory-first** — BenchGen captures the full decision story, not just the final answer. * **Evaluation becomes learning** — every run improves your agent and compounds your advantage. * **Private and sovereign** — runs on your infrastructure. Your data never leaves. The loop compounds. More environments, more runs, better data, smarter agents. Each cycle makes the next one faster and more accurate. *** ## Next steps * [Quickstart](/quickstart) — run your first agent loop * [Agents overview](/agentspace/welcome) — set up your simulation environment * [Eval overview](/eval/overview) — benchmark your agents * [Train overview](/train/overview) — fine-tune on generated trajectories