Smart Routing

Overview

Intelligent task routing using Q-Learning to select optimal execution paths. Simple tasks route to Agent Booster (WASM, <1ms, $0), medium tasks to efficient models, and complex tasks to Opus + multi-agent swarms.

When to Use

• Optimizing cost vs. quality tradeoffs for diverse task types
• When tasks range from simple transforms to complex multi-file changes
• Reducing latency for common code transformations
• Learning from routing history to improve future decisions

Routing Tiers

| Tier | Target | Latency | Cost | |------|--------|---------|------| | Agent Booster | Simple transforms (var-to-const, add-types) | <1ms | $0 | | Medium | Standard coding tasks | ~500ms | Low | | Complex | Multi-agent swarm coordination | 2-5s | Higher |

Agent Booster Transforms

• var-to-const - Variable declaration modernization
• add-types - TypeScript type annotation insertion
• add-error-handling - Try/catch wrapper insertion
• async-await - Promise chain to async/await conversion
• extract-function - Code block extraction to named functions
• add-jsdoc - Documentation generation

Agents Used

• agents/optimizer/ - Performance and cost optimization
• agents/architect/ - Complex task decomposition

Tool Use

Invoke via babysitter process: methodologies/ruflo/ruflo-task-routing