The present application relates to the technical field of multi-agent
collaboration, and particularly relates to a task execution method and
system based on multi-agent
collaboration, which comprises the following steps: performing semantic analysis and multi-
granularity decomposition on user task instructions, generating a subtask
dependency structure and storing it in a dynamic task
pool; performing collaborative planning and pre-execution on the subtasks to be executed, generating a context snapshot and performing Push writing to a history state stack; retrieving an evidence chain and extracting a set of fact triplets based on
a domain knowledge base, performing consistency comparison on a candidate reasoning path based on the structured evidence triplets, and outputting a conflict confidence and a consistency
score; triggering a pop stack and physically clearing intermediate data in the error
branch when the consistency
score is lower than a threshold, restoring the historical context and injecting
negative feedback to generate a new reasoning
branch and return to continue pre-execution; outputting the result and entering the next subtask when the
verification is passed; thereby improving the
controllability and auditability of multi-agent collaborative task execution.