Intelligence Routing

Pick the optimal agent + model tier for a task using learned patterns + the 3-tier router. Emits a hooks_explain rationale so the choice is auditable.

When to use

Before starting any non-trivial task. Replaces manual agent selection with data-driven decisions.

Steps

1. Get an agent recommendation — mcp__claude-flow__hooks_route with the task description. Returns { recommended, confidence, reasoning }.
2. Get a model tier recommendation — mcp__claude-flow__hooks_model-route for Haiku/Sonnet/Opus selection.
3. Search for similar past patterns — mcp__claude-flow__hooks_intelligence_pattern-search to find prior successes.
4. Predict outcome — mcp__claude-flow__neural_predict with the task description for a confidence-scored prediction.
5. Spawn the recommended agent at the recommended model tier.
6. (If --why was passed) — call mcp__claude-flow__hooks_explain to surface the routing rationale to the user.
7. After task completes — call mcp__claude-flow__hooks_model-outcome with success: true|false to train the router.

3-Tier Model Routing