Design Patterns for Multi-Agent AI Orchestration Systems

In the modern AI landscape, relying on a single Large Language Model (LLM) often results in single points of failure, accuracy issues, or cost inefficiencies. Multi-agent orchestration resolves this by assigning task segments to specialized models and aggregating the outputs. When a prompt enters v43.ai, the router divides it into subtasks (e.g., UI layout, logic compiling, database queries). A code specialist like Claude 3.5 Sonnet handles code structure, GPT-4o compiles quick patches, and Gemini reviews files. The orchestrator then cross-compares findings, runs unit tests, and compiles the best output. This cooperative intelligence yields development speeds up to 10x faster with 98% accuracy.