This is the follow up to our first blog post, The Agent Said It Was Done. The Database Disagreed. ThinkingBox-Training is now available on GitHub.

We built ThinkingBox to evaluate an agent's work by comparing the final database state with the expected outcome. We then used the same kind of executable signal as a reward on training tasks outside ThinkingBox-Bench. For Qwen3.8-27B, average success rose from 51.70% to 60.78%, while tasks completed correctly on all twenty attempts rose from 38 to 107 with 50 GRPO steps.

Figure 1. ThinkingBox turns executable checks of terminal database state into rewards for post-training agents on tasks outside ThinkingBox-Bench.

We built ThinkingBox to catch agents that say they are done when they are not.

Each of its 507 tasks in ThinkingBox-Bench (our benchmark dataset) hands an agent a real backend and a set of tools, lets it work, then checks what really changed in the database. In the first post, we caught a model closing a customer's ticket as resolved while the carrier exception behind it was still open. Nine competent-looking tool calls, one wrong database row.

Then we noticed what we had built. For most tasks, an executable check can return 1 or 0 directly from the final database state. That is not just a grader. This can be a reward function.⁺

So we constructed a training pool from ThinkingBox-format tasks outside the benchmark and used their executable outcomes as rewards. The catch was choosing which tasks are suitable for agent training: most tasks are either already solved or hopeless for the model in front of you, and both teach it nothing.

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