Policy-Gated AI Model Router with Capacity Reservation

Model routing is an admission decision. Capability, evaluation, and data policy establish eligibility before cost or latency can influence selection.

AI infrastructure · TypeScript

Policy-Gated AI Model Router

Model selection begins with eligibility; cost and latency optimize only among proven-safe candidates.

const eligible = registry.models.filter(model =>
  model.capabilities.hasAll(task.required)
  && model.regions.includes(task.dataRegion)
  && evals.passes(model.version, task.evaluationSet, task.minimumScore)
  && policy.allows(task.risk, model),
)

const ranked = eligible.sort((a, b) => score(a, task) - score(b, task))
for (const model of ranked) {
  const reservation = await capacity.tryReserve(model, task.maximumUsage)
  if (!reservation) continue
  try { return await infer(model, task, { signal: deadlineSignal }) }
  catch (error) { if (!isSafeFallback(error)) throw error }
  finally { await capacity.settle(reservation) }
}
return deterministicFallback(task) // never route to an ineligible model

Invariant: Every selected model satisfies the task’s capability, policy, evaluation, budget, and deadline constraints.

Use when: Several models can serve a task, but quality, privacy, cost, and capacity constraints differ.

Why this boundary matters

An available substitute may lack the capability, evaluation evidence, or data policy required by the task. Blind fallback trades availability for correctness.

Failure policy

Boundary	Action
Candidate fails capability or data policy	Remove before optimization
Evaluation evidence is stale	Quarantine from consequential tasks
Capacity and budget reserved	Execute inside the remaining deadline
Primary times out	Try one eligible alternate only if budget remains
No eligible model	Use deterministic fallback, review queue, or fail
Tool outcome ambiguous	Reconcile before any model or tool retry

Trade-offs

Routing improves resilience and economics but expands the evaluation matrix and can hide provider differences. Every model, prompt, tool, and policy combination creates a versioned behavior that needs evidence.

Decision rule: Route dynamically only among configurations already approved for the exact task and risk class; never make a model eligible merely because it is available.

Further reference

Browse all engineering snippets · Read the inference-routing architecture