The most robust agentic workflows today use a Router-Coordinator architecture. Instead of asking one LLM to perform every step, use a lightweight, fast model to act as a router that classifies the intent, then dispatches the task to a specialized agent or a deterministic toolset.
When building your router, don't just rely on prompt instructions. Use semantic similarity scores or fine-tuned classification heads to determine the path. If the query is low-complexity, route it to a small, low-latency model. If it requires heavy reasoning, send it to a larger model with specific context.
This pattern drastically reduces cost and improves the success rate of your tool-calling. It allows you to wrap your specialized agents in dedicated guardrails without bloat.
My tip for today: Implement a fallback loop using a structured output parser. If your agent fails to return the expected schema, don't just retry the whole request. Use a targeted correction prompt that feeds the error trace back into the agent alongside the original tool definition.
How are you currently handling routing logic in your multi-agent systems?
#aiengineering #llms #softwarearchitecture #agenticworkflows #machinelearning
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