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RL Environment
Trains and evaluates agents on enterprise requirements generation through a four-stage pipeline — EXTRACT → INTERVIEW → GRAPH → STORY_GEN — over real production work items, with dense per-step rewards and a six-metric terminal composite judged against BA-authored golden user stories.
Screenshots
Industry
Persona / role
Problem
Certified Business Analysts turn dense enterprise specifications into structured user stories that engineers can build against, but this work is scarce, slow, and expensive. Frontier LLMs can draft stories in one shot yet still score below BA-grade output on real production features. Benchmarks like BA Agent Bench quantify this gap with a six-metric composite, but a single terminal score over a long BA workflow is too sparse to serve as a training signal on its own.
Solution
We recast the BA workflow as a reinforcement-learning environment. Each episode gives an agent one real work-item plus its knowledge base; the agent runs four staged actions — feature extraction, stakeholder interview, entity graph, and story generation — then finishes. Every step earns a small shaped reward for format, ordering, and document grounding, and the terminal reward is the same six-metric composite (alignment, coherence, completeness, compliance, testability, specification quality) judged against BA-authored gold.
Impact
The environment gives RL researchers a dense, signal-rich testbed for enterprise requirements generation, where no such RL environment previously existed. Teams can now train and compare requirements-generation agents on the same tasks, verifiers, and gold as the benchmark leaderboard, opening a training path toward closing the gap between frontier LLMs and human BAs on real business work.
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