Intent Routing

Automatic classification and routing of user requests to optimal execution strategies.

Overview

Intent routing is the first phase of every Tachikoma request. It:

1. Classifies the user's intent
2. Evaluates task complexity
3. Routes to the optimal execution strategy
4. Ensures clarity before proceeding

Classification Process

User Request
    ↓
Extract Intent
    ↓
Match to Patterns
    ↓
Confidence Score
    ↓
Route Decision

Complexity Levels

Low Complexity

When:

• Simple queries (<50 lines)
• Well-defined, single-step tasks
• High confidence (>0.9)

Strategy: Direct response Latency: 1-2s

Examples:

• "What does this function do?"
• "How do I implement X?"
• "Explain this error message"

Medium Complexity

When:

• Focused tasks requiring tools
• One domain of knowledge
• Moderate confidence (0.7-0.9)

Strategy: Single skill Latency: 5-15s

Examples:

• "Create a new API endpoint"
• "Refactor this component"
• "Add tests for this module"

High Complexity

When:

• Multi-step workflows
• Cross-domain knowledge
• Moderate confidence (0.5-0.7)

Strategy: Skill chain Latency: 15-45s

Examples:

• "Implement authentication flow"
• "Set up CI/CD pipeline"
• "Migrate database schema"

Very High Complexity

When:

• Large-context tasks (>2000 tokens)
• Complex orchestration
• Lower confidence (<0.5)

Strategy: RLM orchestration or subagent Latency: 45-120s

Examples:

• "Refactor entire codebase"
• "Research architecture patterns"
• "Optimize system performance"

Confidence Thresholds

Score	Action	Rationale
< 0.5	Ask clarification	Too uncertain, risk of error
0.5-0.7	RLM/Subagent	Complex, needs exploration
0.7-0.9	Single skill/chain	Clear intent, moderate complexity
> 0.9	Direct response	Simple, well-understood

Configuration

Intent routes are defined in config/intent-routes.yaml:

routes:
  # Debug and troubleshooting
  debug:
    patterns:
      - "debug"
      - "fix bug"
      - "troubleshoot"
    confidence_threshold: 0.7
    skill: dev
    strategy: direct

  # Verification-focused tasks
  verify:
    patterns:
      - "verify"
      - "test"
      - "validate"
    confidence_threshold: 0.6
    skill_chain: implement-verify
    strategy: sequential

  # Complex tasks
  complex:
    patterns:
      - "refactor"
      - "migrate"
      - "optimize"
    confidence_threshold: 0.5
    subagent: rlm-optimized
    strategy: rlm

  # Simple queries
  query:
    patterns:
      - "what is"
      - "how do i"
      - "explain"
    confidence_threshold: 0.9
    strategy: direct

Decision Tree

User Input
    ↓
Extract Intent Keywords
    ↓
Match Against Routes
    ↓
Confidence > 0.7?
    ├── NO → Ask user for clarification
    ↓ YES
Context > 2000 tokens?
    ├── YES → Use RLM subagent
    ↓ NO
Task Complexity?
    ├── Simple → Direct response
    ├── Medium → Single skill
    ├── High → Skill chain
    └── Very High → RLM orchestration
    ↓
Load context module (if applicable)
    ↓
Execute
    ↓
Reflect (freedom to question)

Best Practices

For Users

1. Be specific — Clear requests get classified faster
2. Provide context — Mention relevant files or domains
3. Clarify ambiguity — If asked, provide more detail

For Skill Authors

1. Define clear patterns — Specific keywords improve routing
2. Set appropriate thresholds — Match confidence to task risk
3. Consider complexity — Route based on actual task requirements

Examples

Example 1: Clear Intent → Direct Response

User: "How do I create a new API endpoint in Express?"

Classification:

• Pattern: "how do i"
• Domain: Express
• Confidence: 0.95
• Complexity: Low

Route: Direct response Latency: 1-2s

Example 2: Medium Complexity → Single Skill

User: "Create a new REST API endpoint for user authentication"

Classification:

• Pattern: "create", "api endpoint"
• Domain: Authentication
• Confidence: 0.85
• Complexity: Medium

Route: Single skill (code-agent) Latency: 5-15s

Example 3: High Complexity → Skill Chain

User: "Implement OAuth2 authentication with JWT tokens, refresh tokens, and role-based access control"

Classification:

• Pattern: "implement", "authentication"
• Domain: OAuth2, JWT, RBAC
• Confidence: 0.75
• Complexity: High

Route: Skill chain (implement-verify-test) Latency: 15-45s

Example 4: Very High Complexity → RLM

User: "Refactor the entire authentication system to use microservices architecture with proper separation of concerns"

Classification:

• Pattern: "refactor", "entire system"
• Domain: Microservices
• Confidence: 0.5
• Complexity: Very High

Route: RLM orchestration Latency: 45-120s

Research

This feature is based on research from:

• Cost-Aware Routing — "When Do Tools and Planning Help LLMs Think?" (arXiv:2601.02663)
  • Finding: Tools improve accuracy by +20% but add 40x latency
  • Implication: Match tool usage to task complexity

Learn more about the research →

See Also

• Context Management — Loading project-specific context
• Skill Execution — How skills are invoked
• Skill Chains — Orchestrating multiple skills
• PAUL Methodology — Structured development