Agents
A workflow where an LLM or classifier first labels an incoming request, then sends it to a prompt, tool set, or model built for that category.
A workflow where an LLM or classifier first labels an incoming request, then sends it to a prompt, tool set, or model built for that category. Typical uses are splitting customer support traffic into general questions, refunds, and technical issues, or sending easy queries to a small, cheap model and hard ones to a more capable model. Routing works when the categories are distinct and easy to classify. A misrouted request goes down the wrong path, and nothing in the pattern brings it back.
Primary source: anthropic.com
Example
In practice, developers reach for Routing (Agent Pattern) when they need the capability described above as part of an AI feature or workflow.
Hands-on guides, comparisons, and tutorials that cover Agents.
FAQ
A workflow where an LLM or classifier first labels an incoming request, then sends it to a prompt, tool set, or model built for that category.
Routing (Agent Pattern) sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Developers Digest publishes tutorials and videos that cover Agents topics including Routing (Agent Pattern). Check the blog and YouTube channel for hands-on walkthroughs.
Related
A safety architecture where a model routes blocked queries to a more constrained model instead of refusing outright.
An agent workflow where one LLM call writes a response and a second call checks it against explicit criteria and sends back feedback.
An ensemble technique where several LLMs answer the same prompt in layers.

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