Multi Advisor

Multi Advisor

Orchestrates intelligent skill selection and execution for multi advisor workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def route_to_best_advisor(
    task_context: Dict[str, Any],
    advisor_registry: List[Dict[str, Any]],
    min_confidence: float = 0.75
) -> Optional[Dict[str, Any]]:
    """Route a task to the most suitable advisor using multi-factor scoring.
    
    Implements Law 1 (Early Exit) and Law 2 (Parse at boundary):
    - Validates task context and registry upfront
    - Computes weighted scores without mutating inputs
    """
    if not task_context.get("intent") or not advisor_registry:
        raise ValueError("Task intent and advisor registry are required")
        
    task_features = _extract_intent_features(task_context["intent"])
    scored_advisors = []
    
    for advisor in advisor_registry:
        if not advisor.get("active"):
            continue
            
        similarity = _cosine_similarity(task_features, advisor["trigger_vectors"])
        historical_success = advisor.get("success_rate", 0.0)
        availability_score = 1.0 if advisor.get("health") == "healthy" else 0.3
        
        # Multi-factor weighted scoring
        composite_score = (
            0.4 * similarity +
            0.35 * historical_success +
            0.25 * availability_score
        )
        
        if composite_score >= min_confidence:
            scored_advisors.append({
                "advisor_id": advisor["id"],
                "name": advisor["name"],
                "confidence": round(composite_score, 3),
                "factors": {"similarity": similarity, "history": historical_success, "availability": availability_score}
            })
            
    if not scored_advisors:
        return None
        
    # Atomic Predictability (Law 3) - Return new sorted list
    scored_advisors.sort(key=lambda x: x["confidence"], reverse=True)
    return scored_advisors[0]

Pattern 2: Execution with Fallback

def execute_advisor_with_resilience(
    selected_advisor: Dict[str, Any],
    task_payload: Dict[str, Any],
    fallback_advisors: List[Dict[str, Any]],
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute the selected advisor with automatic fallback chaining.
    
    Implements Law 4 (Fail Fast/Loud) and fallback orchestration:
    - Validates payload before dispatch
    - Retries on transient failures, falls back to secondary advisors
    - Returns structured execution metadata
    """
    if not _validate_payload(task_payload, selected_advisor["schema"]):
        raise ExecutionError(f"Payload validation failed for {selected_advisor['name']}")
        
    execution_log = []
    current_advisor = selected_advisor
    
    for attempt in range(max_retries + 1):
        try:
            response = _dispatch_to_advisor(current_advisor, task_payload)
            
            # Success path - Atomic result construction
            return {
                "status": "success",
                "advisor_used": current_advisor["name"],
                "result": response["data"],
                "confidence": current_advisor["confidence"],
                "attempts": attempt + 1,
                "latency_ms": response["latency_ms"],
                "log": execution_log
            }
            
        except TransientNetworkError as e:
            execution_log.append(f"Attempt {attempt+1} failed: {str(e)}")
            if attempt == max_retries:
                break
            continue
            
        except AdvisorSpecificError as e:
            # Fail fast on invalid state - do not retry
            raise ExecutionError(f"Invalid state in {current_advisor['name']}: {e}") from e
            
    # Fallback chain execution
    for fallback in fallback_advisors:
        try:
            execution_log.append(f"Falling back to {fallback['name']}")
            response = _dispatch_to_advisor(fallback, task_payload)
            return {
                "status": "fallback_success",
                "advisor_used": fallback["name"],
                "result": response["data"],
                "confidence": fallback["confidence"],
                "attempts": max_retries + 1,
                "latency_ms": response["latency_ms"],
                "log": execution_log
            }
        except Exception as e:
            execution_log.append(f"Fallback {fallback['name']} failed: {str(e)}")
            
    raise ExecutionError(f"All advisors and fallbacks exhausted for task: {task_payload.get('id')}")

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• What Is Multi-Agent — LangChain Blog
• Building Effective Agents — Anthropic Research
• Multi-Agent System Survey — arXiv (2402.16817)
• LLM Agent Survey — Lilian Weng
• Expert Ensembling for LLMs — arXiv Paper