Choose a model for an agent task by first asking whether the task needs an agent at all, then classifying the work, testing models against a task-specific quality bar, and comparing cost, latency, and policy constraints. This four-decision test is a practical synthesis of guidance from AWS, Microsoft, and Google Cloud—not a vendor-published standard or a validated benchmark.
1. Does the task need an agent?
Start with the shape of the work, not the model. An agentic workflow is useful when a task needs orchestration, tool use, or open-ended steps. If the work is predictable, highly structured, and can be completed in one model call, a non-agentic design may be more cost-effective. Google Cloud explicitly recommends considering non-agentic solutions for these cases: Choose a design pattern for your agentic AI system.
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This first decision prevents a common misallocation: adding agent coordination to a task whose inputs, steps, and output are already well defined. If a single call can meet the task’s quality and operational requirements, compare that approach before introducing an agent.
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2. What does the task require?
Break the workflow into task classes based on the work each step actually performs. AWS suggests classes such as simple classification, structured multi-step reasoning, and open-ended investigation. The useful distinctions are task structure, reasoning depth, and tool-use demands—not prompt length or a model’s general leaderboard rank. See AWS guidance on task-appropriate model selection.
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Then map each class to candidate models. A workflow with variable complexity across steps may benefit from more than one model. Anthropic notes that a multi-model design can fit such workflows, while one tuned model may be preferable when task difficulty is uniform or the workflow is a single dependent chain: Anthropic guidance on agentic workflows.
3. What quality bar must a route clear?
Define acceptance criteria for each task class before choosing a model. Test candidate models on examples representative of the workload, and select the least costly candidate that meets the required bar. A general benchmark can help create a shortlist, but it does not establish how a model will perform on your agent’s actual traffic.
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Measure outcomes by class rather than relying only on a blended average. Track task success or correctness alongside operating measures such as latency and token use. That makes it easier to detect a route that performs well overall but falls below the quality bar for a specific class. AWS’s recommendation is direct: “Benchmark candidate models on the workload’s own task distribution.”
4. What operating constraints govern the route?
Compare viable candidates across quality, cost, latency—including relevant tail latency—and policy or deployment requirements. There is no universal quality threshold, cost trade-off, or latency limit established for every team; these depend on the workload and its acceptance criteria. Microsoft advises: “Compare quality, cost, and latency against the acceptance criteria for the workload rather than reducing the decision to one aggregate score.” See Microsoft’s model-router evaluation guidance.
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Keep direct model selection where deterministic choice is required or evaluation does not show that routing is a good fit. For managed routing, check that its eligible model set and control behavior cover the cases where a specific model must be selected.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to put the test into practice
- Describe the workflow: List its steps, tools, expected outputs, and whether they are predictable or open-ended.
- Decide whether to use an agent: Compare an agentic design with a single-call or other non-agentic approach when the work is structured.
- Create task classes: Group steps by their actual structure, reasoning needs, and tool demands.
- Set acceptance criteria: Define what counts as successful work for each class, including any policy or deployment constraints.
- Evaluate candidates: Use representative examples and record quality, cost or token use, and latency by class.
- Choose the route: Assign the least costly candidate that clears the class’s quality bar, while preserving direct selection when predictability is required.
- Re-evaluate changes: Review assignments when the workload, available models, routing mode, or eligible model subset changes.
When to consider managed routing
Managed services can be options, not substitutes for defining task classes and acceptance criteria. AWS describes intelligent prompt routing within a model family. Microsoft describes its model router as analyzing requests to select a model and recommends evaluating it against meaningful workload baselines. Relevant documentation: Amazon Bedrock intelligent prompt routing, Microsoft model router, and Microsoft model-router evaluation.
Before adopting one, verify that its eligible models, routing behavior, and available controls suit your workload. Recheck results after changing the routing mode or model subset, and as the work or available models evolve. The services’ descriptions do not establish a universal performance gain or savings figure.
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