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You are an AI platform architect. Design an explainable, testable, progressively deployable model-routing policy for the application below. Do not send every request to the largest model by default. Application and user scenarios: {application} Primary task classes and examples: {task examples} Candidate models and known capabilities: {model inventory} Context windows and tool support: {context and tools} Quality and safety thresholds: {quality thresholds} Latency objectives: {latency} Cost budget and billing units: {cost} Privacy, data-residency, and compliance requirements: {privacy and compliance} Availability, concurrency, and rate-limit information: {runtime constraints} Allowed telemetry and feedback signals: {telemetry} First audit every time-sensitive input. If model price, regional availability, rate limits, context size, or capability lacks a dated source, label it "verification required" rather than treating it as current fact. Then return: 1. A task-classification table defining complexity, context, tools, privacy, accuracy, latency, and cost needs for each class, with positive examples and confusing counterexamples. 2. A routing decision table that filters models by hard constraints before ranking eligible options by quality, latency, and cost. Explain every rule; do not substitute brand reputation or one benchmark for task evidence. 3. Escalation and degradation rules covering when to move up from a small model, when to fall back to a compatible model, and how to handle timeout, rate limit, empty response, tool failure, and low confidence. Never silently weaken safety or privacy to stay online. 4. Budget controls for request, session, and daily limits, including what the system returns when a limit is reached, which tasks may continue after user confirmation, and which must stop. 5. An evaluation set with representative cases, expected behavior, scoring rubric, maximum latency, and cost-recording fields for every task class. Include multilingual, long-context, and failure-injection cases. 6. A rollout plan covering offline replay, shadow routing, limited traffic, rollback thresholds, and monitoring. Do not change production configuration directly. Finish with machine-readable pseudoconfiguration and a short team-facing decision note. Distinguish conclusions supported by supplied data from assumptions that require measurement.