Routing Layer
Logic that selects which model, prompt, or tool to run based on inputs like task type, language, or complexity.
A routing layer chooses the right model, prompt, or tool for a request based on features like task type, language, or difficulty. It optimizes cost, latency, and quality.
Teams use routing to send easy tasks to cheaper models and complex ones to stronger models, or to pick specialized prompts/tools. It reduces waste and improves output consistency.
In workflows, the router sits before execution, evaluating inputs and policy signals. The benefit is predictable behavior and controlled spend without one-size-fits-all models.
Frequently Asked Questions
What signals drive routing?
Task classification, input length, language, detected intent, risk level, and historical performance. Keep signals simple and reliable.
How do I avoid routing flakiness?
Use deterministic rules where possible, log decisions, and keep a fallback route. Test classifiers and keep thresholds stable.
Can routing be learned automatically?
Yes—use A/B tests and feedback loops to adjust routes. Start with rules, then iterate with data-driven decisions.
How do I monitor routing performance?
Track per-route success, latency, cost, and error types. Alert on anomalies like unexpected route volume or degraded quality.
What if a route fails?
Use fallbacks—alternate model/prompt or human review. Keep timeouts and retries to avoid cascading failures.
How does routing control cost?
By sending trivial tasks to cheaper paths and reserving premium models for hard tasks. Measure cost per successful outcome.
Should routing consider compliance?
Yes—steer regulated data to approved models/endpoints. Block routes that violate data residency or PII rules.
How do I test routing changes?
Shadow or canary new rules, compare outcomes, and roll back if metrics worsen. Keep routing logic versioned.
Can routing combine multiple tools?
Yes—route to sequences like retrieve-then-generate or classify-then-act. Keep the graph simple to debug.
Agentic AI
An AI approach where models autonomously plan next steps, choose tools, and iterate toward an objective within guardrails.
Agentic Workflow
A sequence where an AI agent plans, executes tool calls, evaluates results, and loops until success criteria are met.
Agent Handoff
A pattern where one AI agent passes context and state to another specialized agent to keep multi-step automation modular.
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