# Hybrid Verification

Building an LLM workflow on a platform like Dify used to mean manually picking nodes, filling in parameters and wiring connections, which is slow and error-prone. ChatDify hands the job to three collaborating agents: a planner that decomposes the request into a directed acyclic graph, an executor that synthesizes node parameters under schema constraints and compiles them into Dify's DSL, and a reviewer that runs static checks plus sandboxed execution with LLM judging in a closed refinement loop. On 100 production-inspired Dify tasks with Qwen-3.7, GLM-5.1 and DeepSeek-v4 as backends, success rates were 0.96, 0.91 and 0.75, above the zero-shot baseline with dynamic repair.

Building an LLM workflow on a platform like Dify used to mean manually picking nodes, filling in parameters and wiring connections, which is slow and error-prone. ChatDify hands the job to three collaborating agents: a planner that decomposes the request into a directed acyclic graph, an executor that synthesizes node parameters under schema constraints and compiles them into Dify's DSL, and a reviewer that runs static checks plus sandboxed execution with LLM judging in a closed refinement loop. On 100 production-inspired Dify tasks with Qwen-3.7, GLM-5.1 and DeepSeek-v4 as backends, success rates were 0.96, 0.91 and 0.75, above the zero-shot baseline with dynamic repair.