L4 - Reasoning

The Reasoning layer (L4) is the cognitive core of the agent, responsible for decision-making, planning, and inference.

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Overview

The Reasoning layer transforms inputs and context into actionable decisions. It respects Persona constraints, accesses Memory for context, and produces structured decisions for Capability invocation.

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Core Responsibilities

Decision Making

Choose appropriate actions based on context and goals

Planning

Break down complex tasks into executable steps

Inference

Draw conclusions from available information

Confidence Scoring

Assess certainty of decisions and predictions

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Key Requirements

Decision Structure

Requirement	Description
Structured Output	Produce machine-readable action decisions
Confidence Scores	Include certainty estimates when applicable
Rationale	Explain reasoning process (chain-of-thought)
Alternatives	Consider and document alternative options

Layer Interactions

Requirement	Description
Respect Persona	Honor constraints defined in L5
No Direct Memory Access	Never bypass L2 Memory interface
Validate Capabilities	Never bypass L3 Capability validation
Timeout Compliance	Respect request timeout limits

Observability

Requirement	Description
Log Decisions	Record all decisions with full rationale
Handle Ambiguity	Gracefully manage unclear inputs
Error Recovery	Handle model errors without crashing

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Decision Format

Structure

{
  "decision_id": "uuid",
  "timestamp": "2026-01-15T12:00:00Z",
  "trace_id": "uuid",
  "action": "web_search",
  "parameters": {
    "query": "ARAL specification examples",
    "max_results": 5
  },
  "confidence": 0.95,
  "rationale": "User asked for examples. Web search is most appropriate capability for finding documentation.",
  "chain_of_thought": [
    "User wants ARAL examples",
    "Examples likely documented online",
    "web_search capability available",
    "Query: 'ARAL specification examples'"
  ],
  "alternatives": [
    {
      "action": "database_query",
      "confidence": 0.3,
      "reason": "Local database might have cached examples, but likely outdated"
    }
  ],
  "metadata": {
    "model": "claude-3-opus",
    "temperature": 0.7,
    "tokens_used": 1250
  }
}

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Inference Strategies

Direct Inference

Quick, single-step reasoning for simple tasks.

{
  "strategy": "direct",
  "input": "What time is it?",
  "action": "get_time",
  "confidence": 1.0
}

Chain-of-Thought

Step-by-step reasoning for complex problems.

{
  "strategy": "chain_of_thought",
  "steps": [
    "User wants weather information",
    "Need location first",
    "Ask user for location",
    "Then call weather_api"
  ]
}

Multi-Step Planning

Breaking down complex goals into sequences.

{
  "strategy": "planning",
  "goal": "Book a flight to Paris",
  "plan": [
    { "step": 1, "action": "search_flights", "params": {...} },
    { "step": 2, "action": "compare_prices", "params": {...} },
    { "step": 3, "action": "book_flight", "params": {...} }
  ]
}

Self-Reflection

Iterative improvement through feedback loops.

{
  "strategy": "self_reflection",
  "iterations": [
    {
      "attempt": 1,
      "result": "insufficient_information",
      "reflection": "Need more context about user preferences"
    },
    {
      "attempt": 2,
      "result": "success",
      "reflection": "Better result after clarification"
    }
  ]
}

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Confidence Scoring

Confidence Levels

Score	Meaning	Action
0.9 - 1.0	Very High	Proceed with action
0.7 - 0.9	High	Proceed, log uncertainty
0.5 - 0.7	Moderate	Request confirmation
0.3 - 0.5	Low	Seek clarification
0.0 - 0.3	Very Low	Decline or defer

Confidence Calculation

{
  "confidence_factors": {
    "input_clarity": 0.9,
    "context_availability": 0.8,
    "capability_reliability": 0.95,
    "historical_success": 0.92
  },
  "final_confidence": 0.89
}

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Persona Constraint Enforcement

Checking Constraints

{
  "decision": "delete_database",
  "persona_check": {
    "allowed": false,
    "reason": "Persona does not include 'database:write' permission",
    "alternative": "Request human approval for dangerous action"
  }
}

Confirmation Required

{
  "decision": "send_email",
  "persona_constraint": {
    "require_confirmation": true,
    "reason": "High-risk action requires human approval",
    "prompt": "Confirm sending email to 500 recipients?"
  }
}

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Error Handling

Ambiguous Input

{
  "error": "ambiguous_input",
  "input": "it",
  "clarification_needed": "What does 'it' refer to?",
  "suggestions": [
    "the weather",
    "the document",
    "the previous result"
  ]
}

Model Error

{
  "error": "model_error",
  "message": "Inference model timeout",
  "fallback_action": "use_simpler_model",
  "retryable": true
}

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Best Practices

1. Always Provide Rationale: Enable debugging and user trust
2. Include Confidence Scores: Help users understand uncertainty
3. Consider Alternatives: Document decision-making process
4. Respect Constraints: Never bypass Persona restrictions
5. Implement Timeouts: Prevent infinite reasoning loops
6. Log Everything: Maintain audit trail for decisions
7. Handle Ambiguity: Ask for clarification when needed
8. Use Appropriate Strategy: Match strategy to task complexity

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Reasoning Patterns

ReAct

Reason + Act pattern for iterative problem-solving

Tree of Thoughts

Explore multiple reasoning paths in parallel

Chain of Thought

Step-by-step logical reasoning

Self-Consistency

Multiple reasoning paths, majority vote

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Related Layers

• L2 - Memory: Provides context for reasoning
• L3 - Capabilities: Executes reasoning decisions
• L5 - Persona: Constrains reasoning behavior
• Specification: Full requirements for L4 Reasoning

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