Mixture of Experts (MoE) routes queries to specialist LLMs using confidence scoring rather than binary classification. A router LLM receives the question and emits a score (0.0–1.0) for each expert: price_expert, genre_expert, publisher_expert, author_expert. The expert with the highest score handles the query.
Unlike the Supervisor pattern which makes a categorical routing decision, MoE's continuous scores are transparent and debuggable — you can see exactly how confident the router was in each expert and why the winner was chosen. This makes it easier to diagnose misrouting and improve the router's prompts.
Each expert has a tailored system prompt and tool access optimized for its domain. This specialization means each expert can be more accurate and concise than a generalist agent tackling the same question — MoE effectively creates a team of focused specialists rather than one overloaded generalist.