Complexity Signal

Overview

complexity estimates whether a request is easy, medium, or hard by comparing it with configured example sets. It is independent of topic: two requests in the same domain can still need different model tiers.

Key Advantages

• Separates estimated difficulty from topic classification.
• Reuses one easy/medium/hard policy across multiple decisions.
• Tunes routing with examples instead of a custom classifier schema.

What Problem Does It Solve?

Domain routing alone cannot distinguish a short factual request from a multi-step analysis request. Complexity supplies a reusable difficulty signal so decisions can escalate only the traffic that needs it.

When to Use

Use complexity when model cost or reasoning mode should vary with estimated task difficulty. Do not treat it as a correctness or safety guarantee; use domain-specific evaluation and safety signals for those concerns.

Configuration

routing:
  signals:
    complexity:
      - name: needs_reasoning
        threshold: 0.75
        description: Escalate multi-step reasoning or synthesis-heavy prompts.
        hard:
          candidates:
            - solve this step by step
            - compare multiple tradeoffs
            - analyze the root cause
        easy:
          candidates:
            - answer briefly
            - quick summary
            - simple rewrite

A rule emits a suffixed name. Decisions must reference <rule>:easy, <rule>:medium, or <rule>:hard:

routing:
  decisions:
    - name: escalate-hard-prompts
      description: Route hard prompts to the reasoning model.
      priority: 150
      rules:
        operator: AND
        conditions:
          - type: complexity
            name: needs_reasoning:hard
      modelRefs:
        - model: reasoning-model
          use_reasoning: true

For optional prototype-bank tuning, configure the family-level module once:

global:
  model_catalog:
    modules:
      complexity:
        prototype_scoring:
          enabled: true
          max_prototypes: 8
          top_m: 2

Dependencies and Limitations

• Complexity uses the configured semantic embedding runtime. A remote embedding provider receives the request text used for classification.
• Candidate phrases and thresholds must be calibrated together against labeled traffic. Re-evaluate them whenever the embedding model changes.
• Ambiguous prompts can land in the medium band; always define a route or fallback for every band you rely on.
• See a complete example: config/fragments/signal/complexity/escalation.yaml.