Multi-Model Routing

Part of Agent Skills™ by googleadsagent.ai™

Description

Multi-Model Routing is the intelligent dispatch of agent tasks to the optimal model provider based on task characteristics, cost constraints, latency requirements, and availability. Production AI systems that rely on a single model provider are fragile and expensive. Multi-Model Routing creates a resilient, cost-efficient agent architecture that leverages the strengths of Claude, GPT, Gemini, and open-source models, automatically selecting the best model for each task and failing over gracefully when a provider is unavailable.

This skill documents the multi-model routing architecture powering the Buddy™ agent at googleadsagent.ai™, which routes between Claude (primary — strongest reasoning), GPT-4o (secondary — strong function calling), and Gemini (tertiary — large context, low cost) based on task classification. The routing layer reduced costs by 45% compared to using Claude for all tasks while maintaining equivalent quality scores, because many subtasks (formatting, summarization, data extraction) perform identically on cheaper models.

The routing decision incorporates four factors: model strengths (code reasoning, long context, structured output, creative writing), cost per token (varies 100x between model tiers), latency targets (real-time vs. batch), and availability (rate limits, outages, degraded performance). A circuit breaker pattern ensures that temporary provider issues don't cascade into user-facing failures.

Use When

• Monthly AI costs need reduction without sacrificing quality
• You need resilience against single-provider outages or rate limits
• Different subtasks have fundamentally different model requirements
• Latency-sensitive and latency-tolerant tasks coexist in the same system
• You want to evaluate new models without fully committing to them