TRON is a reinforcement-learning environment for training enterprise-operations agents on stateful, multi-step work across eight enterprise domains. Each instance embeds a mock enterprise state — HR records, ITSM tickets, calendar entries, drive contents — and a task that can only be solved by choosing the right tool, in the right order, under the right policy. Reward signals are generated from deterministic verifiers over the resulting workflow, decomposing performance into policy adherence, tool-sequence correctness, and end-state fidelity. Difficulty is not asserted — it is measured on frozen signed pilots, with a cohort name and a measured date on every envelope.
SQL-based outcome verification: hidden expert-authored SQL queries that validate final database states to ensure exact business outcomes.
Resettable containerized sandbox: Model Context Protocol (MCP)-powered live server environments running against freshly seeded SQL database snapshots.
Long-horizon stateful planning: multi-step trajectories requiring up to 34 sequential steps with permanent, stateful database mutations.
02 · Key metrics
Three numbers that define the scope of the TRON environment.
HR remains the hardest domain across the board — most models sit in the single digits or teens; only — models clear 40%. TRON’s RL environment is designed to close exactly this gap.
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Fig. 1: Per-Domain Success Rate (HR, ITSM, CSM, Email, Calendar, Drive, Teams, Hybrid) across evaluated models.
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Fig. 2: Dual-Axis Process Scores (P-axis policy, T-axis tool sequence) by domain.