multi-model-orchestrator

Multi-model orchestration coordinates multiple AI agents to perform deep codebase analysis by enforcing a two-layer prompt flow and stage-based task distribution, yielding concise architectural and security evaluations.

The best way to map dependencies across a large-scale codebase is using multi-agent orchestration, which distributes stage-based exploration tasks to comprehensively map call chains and dependencies.

Yes, you can use multiple AI agents to evaluate architecture and security scenarios by configuring parallel and serial collaboration modes, ensuring controllable multi-perspective assessments.

You coordinate parallel and serial AI tasks by applying a two-layer prompt architecture and task distribution strategies, allowing a decision-maker to control execution while external models act as advisors.

To start multi-agent module splitting and reconnaissance, enable multi-model collaboration in a project, perform rapid reconnaissance, and distribute tasks across agents before synthesizing final reports.