# AutomationBench Pro

ToolSpace reverses the order in which agent training environments are synthesized: it first mines data, state and retry dependencies between adjacent calls in execution traces from real MCP services and open agent corpora using deterministic rules, clusters the resulting weighted tool-composition graph into environments, synthesizes databases only where state is mutated, and then runs state-aware random walks with rollback to decompile replayable chains into natural-language tasks with assertion-based checks. Across seven long-horizon tool-use benchmarks, Qwen3.6-35B-A3B rises from 23.0 to 41.0 average score, or 43.2 with agent RL, and GLM-5.2 rises from 44.6 to 56.6.

ToolSpace reverses the order in which agent training environments are synthesized: it first mines data, state and retry dependencies between adjacent calls in execution traces from real MCP services and open agent corpora using deterministic rules, clusters the resulting weighted tool-composition graph into environments, synthesizes databases only where state is mutated, and then runs state-aware random walks with rollback to decompile replayable chains into natural-language tasks with assertion-based checks. Across seven long-horizon tool-use benchmarks, Qwen3.6-35B-A3B rises from 23.0 to 41.0 average score, or 43.2 with agent RL, and GLM-5.2 rises from 44.6 to 56.6.