Rate limits and capacity
Distribute load across allowed resources; at limits, wait, switch or return an explicit error according to policy.
Product /Route Engine
Match each task's difficulty to the right model.
Within your allowed model set, choose paths using task needs, quality thresholds and runtime conditions to match model capability with business requirements.
Built for enterprise
01
Fit the task
Use lightweight models for extraction and classification, and more capable models for complex reasoning.
02
Honor constraints
Data scope, allowed models, quality and budget jointly constrain selection.
03
Maintain continuity
Handle timeouts, rate limits and resource failures within approved boundaries.
01 / Choose models by task difficulty
Turn task requirements into executable model-selection rules.
02 / Service continuity
Use fallback policies with clear limits.
Account for interface differences, error types and request state when deciding fallback, reducing business disruption.
Timeouts and retries
Bound timeouts and retries to avoid runaway costs and load.
Cross-model fallback
Check output, context and tool compatibility; an available interface alone is not enough to justify substitution.
Tasks already in progress
When streaming output or tool actions have started, define recovery and duplicate-execution risks rather than assuming a seamless switch.
03 / Policy examples
Follow a routing decision from input to outcome feedback.
Three static scenarios illustrate enterprise policy choices. They are decision examples, not deployed customer cases.
Simple information extraction
Extract order fields → classify as simple → consider lightweight models → check completeness.
Complex contract analysis
Cross-document analysis → classify as complex → consider capable models → check sources and review manually.
Sensitive material processing
Sensitive-data restriction → private models only → select by difficulty → escalate to people or report a limitation if capability is insufficient.
Uncertainty and escalation
When classification is uncertain or output misses the threshold, escalate the model or request human help under approved policy.
04 / Quality and outcome feedback
Refine policies using resource performance and task results.
Resource tests describe model-service performance; task evaluations show whether work was completed. Both inform improvement.
Route Trust
Availability, capability and version differences update resource admission and candidate sets.
Agent Eval
Task quality, success rates and costs help refine task-to-model mappings.
Policy release
Version changes, validate in tests or limited rollouts, then expand gradually.
Rollback on issues
Roll back on quality degradation or incompatibility, preserving the reason for the change.
05 / Operations and next steps
Make every model choice explainable.
Investigate paths, matching rules and outcomes, then agree on routing suited to your current business.
Recorded fields
Request ID, task type and difficulty, matching rule, selected model, fallback reason and status.
Runtime metrics
Latency, errors, consumption and fallback records; link business outcomes only when data exists.
What to prepare for a discussion
Bring your model list, task types, latency and quality requirements, and protocols that must be retained.