Automatic model routing for production AI agents
Route by task, quality threshold, latency, cost, and risk—then verify every routing policy against real workloads and production outcomes.
Using the strongest model for every request is simple and often wasteful. Using the cheapest model everywhere is economical until quality failures erase the savings.
Automatic routing tries to choose an appropriate model for each task. The hard part is defining “appropriate” for your product.
Common routing strategies
Static selection
Choose one model after testing candidates on the full workload. This is easy to operate and debug, but it leaves savings on the table when task difficulty varies widely.
Rule-based routing
Use known product context such as route, customer tier, language, or workflow. Rules are transparent and deterministic, though they require maintenance as the product changes.
Query classification
A classifier predicts task type or difficulty and selects a model. This can adapt within one workflow, but classifier errors become routing errors.
Adaptive or market-informed routing
The router uses changing performance, preference, availability, or aggregate usage signals to choose among models. It can react quickly to the ecosystem, but teams must still verify the policy against their own constraints.
Define guardrails before optimization
Routing should optimize inside a safe candidate set. Filter models by required capabilities and compliance before comparing cost:
- Tool, vision, structured-output, or long-context support
- Data retention and regional processing
- Approved providers and deployment environments
- Maximum latency and minimum availability
- Safety behavior for the workflow
No routing score should override a hard product requirement.
Optimize cost per successful task
Cost per token hides retries, escalations, and repair work. Measure the complete task:
effective cost = model + retrieval + tools + retries + evaluation + human repair
A more expensive model may be cheaper for complex tasks if it completes them in one attempt. A small model may dominate on classification or extraction. The useful result is a routing frontier, not one global winner.
Keep multi-turn sessions coherent
Switching models mid-session can change tone, memory interpretation, tool behavior, and cache reuse. Decide whether the router should remain sticky for a session or re-evaluate at specific workflow boundaries.
Record both the requested route and the model actually selected. Without that evidence, an outcome regression is difficult to attribute.
Plan for fallbacks
Fallbacks protect availability, but a technically successful fallback can silently reduce capability. If the secondary model lacks a required tool or cannot follow the output schema, returning any answer is not success.
Test fallback paths explicitly. Alert when fallback rate changes, and compare outcomes by selected model rather than aggregating everything under the router name.
Release routing changes like product changes
Evaluate a new policy offline, replay representative traces, and shadow it on production traffic before assigning real requests. Roll out gradually and compare quality, latency, cost, tool success, escalation, and user repair by policy version.
Routing is a living system. Models, prices, provider reliability, and user behavior change, so the policy must be continuously measured.
Observe model routing with Currai
Currai preserves route decisions beside conversations, model calls, tools, latency, cost, and outcomes. Teams can identify which task classes benefit from smaller models and where routing introduces silent regressions.
Use the Currai integration skill, or read choosing models with production data.