How to Route Tasks to the Right AI Model

Inventory Your Task Types

Before you can route tasks, you need to know what tasks your system handles. Go through your agent codebase and document every distinct type of model call. Common categories include code generation, code review, content writing, data extraction, classification, summarization, question answering, tool call planning, and conversational responses.

For each task type, note its typical characteristics: average input length, expected output length, whether it requires creativity or precision, how critical accuracy is, and how frequently it occurs. This inventory becomes the foundation for your routing rules.

Do not over-categorize. Ten to twenty task types is sufficient for most agent systems. The goal is to distinguish between tasks that need different capability levels, not to create a taxonomy of every possible request. If two task types should route to the same model tier, you can group them together.

Map Tasks to Effort Levels

Assign each task type an effort level: low, medium, high, or max. Low effort tasks like data formatting, simple classification, and template filling can be handled by economy models. Medium effort tasks like standard content generation, basic coding, and moderate analysis go to workhorse models. High effort tasks like complex code review, multi-step reasoning, and expert content creation need frontier models. Max effort tasks like novel architecture design and critical decision support get the most capable model available.

When you are unsure about a task type, start by assigning it to a higher tier than you think it needs. It is safer to overpay initially than to underserve tasks. You can always move tasks to a lower tier later once you have production data confirming that cheaper models handle them adequately.

Some task types span multiple effort levels depending on the specific request. A coding task might be low effort for simple formatting but high effort for a complex algorithm implementation. For these variable tasks, use secondary criteria like input length, specified constraints, or domain keywords to refine the routing decision at request time.

Assign Models to Each Tier

Select specific models for each effort tier based on your provider access, budget, and task requirements. A typical assignment for 2026 might look like this. Economy tier: Claude Haiku or GPT-5 Nano for cloud, Ollama with Llama 3.2 for local. Workhorse tier: Claude Sonnet or Gemini 2.5 Pro. Frontier tier: Claude Opus or GPT-5.4. Max tier: the same frontier models with extended thinking enabled or the highest-capability configuration available.

Consider provider strengths when assigning models. If your agent does heavy coding work, Claude at the workhorse tier might produce better results than alternatives. If your agent needs structured JSON output frequently, GPT with Structured Outputs might be more reliable at the workhorse tier. Match model strengths to your most common task types within each tier.

Start with one model per tier and add alternatives for fallback coverage. Running multiple models at the same tier adds complexity, so only do it when you need provider diversity for reliability or when different providers genuinely excel at different subsets of tasks within the same tier.

Implement the Routing Logic

The routing layer sits between your application and the model API. The simplest implementation is a function that takes a task type string and returns a model identifier. Your application labels each request with its task type, the routing function maps it to a model, and the request goes to that model through LiteLLM or your provider SDK.

For rule-based routing, the implementation is a lookup table or a series of conditional checks. Task type equals "code_review" maps to the frontier model. Task type equals "data_format" maps to the economy model. This approach is easy to understand, easy to debug, and captures the majority of available savings.

For more dynamic routing, add input analysis to the routing function. Check the input length, look for keywords that indicate complexity (like "analyze," "compare," "evaluate" versus "format," "extract," "classify"), and consider any metadata your application provides about the request context. This refinement captures cases where the same task type varies in complexity.

Keep the routing logic fast. The router evaluates every request, so any overhead it adds gets multiplied across your entire traffic volume. A simple rule lookup adds microseconds. A classifier model call adds seconds and its own cost. For most systems, rules plus simple heuristics are the best trade-off between routing accuracy and routing overhead.

Add Confidence-Based Escalation

Confidence-based escalation is the safety net that catches routing mistakes. When a model returns a response with low confidence, the system automatically re-sends the request to a higher-tier model. This prevents quality failures on tasks that were incorrectly routed to a model that could not handle them.

Measuring confidence varies by use case. For classification tasks, the probability score of the top prediction serves as a natural confidence metric. For generation tasks, you can ask the model to rate its own confidence, check whether the output meets structural requirements, or compare the response length to expected norms. Short, hedging responses from a model that was asked for detailed analysis suggest low confidence.

Set escalation thresholds conservatively at first. A confidence threshold of 80 percent might escalate too many tasks unnecessarily, while 30 percent might let poor results through. Start around 50 percent and adjust based on production results. Track the percentage of tasks that escalate from each tier, aiming for 5 to 15 percent escalation as a sign that routing is appropriately aggressive without being reckless.

Monitor and Refine Routing Rules

Once your routing is live, production data reveals where the rules need adjustment. Track three key metrics: cost per successful output by task type, quality scores by task type and model tier, and escalation frequency by task type.

High escalation frequency for a specific task type means your routing is sending those tasks to a tier that cannot handle them reliably. Move that task type to a higher tier in your routing rules. Low or zero escalation for a task type at a high tier means you might be overspending. Try moving it to a lower tier and monitoring whether quality holds.

Review routing rules regularly, at least monthly, as model capabilities and pricing change. A model update might make a workhorse model capable of handling tasks that previously required a frontier model. New economy models might handle tasks that were previously assigned to workhorse. Keep your routing rules aligned with current model capabilities.

Log routing decisions alongside outcomes so you can analyze the relationship between routing choices and result quality. This data is the foundation for continuous improvement of your routing accuracy and, by extension, your cost efficiency.