Complex, multi-step reasoning demands agents to be capable of dynamically exploring diverse research directions and adapting plans on the fly. We introduce Gnosis Deepsearch, a schema-guided framework for long-horizon planning in complex Question Answering, where variables, clues, and constraints are captured in a structured state representation maintained across trajectories. This state is cascaded into parallel search paths broadening exploration without relying on sequential execution, thereby mitigating both limited search space coverage and catastrophic forgetting. We couple Gnosis with a retriever that sidesteps challenges associated with retrieval and LLM reading when longer documents are involved. Our retrieval strategy ensures that the most relevant snippets from longer documents are considered, showcasing several benefits within both Gnosis and a more general ReAct framework on the BrowseComp-Plus benchmark. Extensive experiments on several QA benchmarks of varying difficulty demonstrate the strong performance of Gnosis across different configurations.