Multi Agent Patterns

Multi Agent Patterns

Orchestrates intelligent skill selection and execution for multi agent patterns 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_multi_agent_task(
    task: Dict[str, Any],
    agent_registry: List[Dict[str, Any]],
    min_confidence: float = 0.75
) -> Dict[str, Any]:
    """Route a decomposed task to the optimal agent in a multi-agent system.
    
    Evaluates agents based on capability overlap, historical success rate,
    and current queue depth to prevent bottlenecks.
    """
    if not task.get("intent") or not agent_registry:
        raise ValueError("Task requires valid intent and available agents")
        
    task_capabilities = _extract_intent_capabilities(task["intent"])
    scored_agents = []
    
    for agent in agent_registry:
        cap_match = _calculate_capability_overlap(task_capabilities, agent["capabilities"])
        history_score = agent.get("success_rate", 0.0) * 0.4
        load_penalty = agent.get("queue_depth", 0) * 0.05
        
        raw_score = (cap_match * 0.5) + history_score - load_penalty
        if raw_score >= min_confidence:
            scored_agents.append({
                "agent_id": agent["id"],
                "score": round(raw_score, 3),
                "estimated_latency_ms": agent.get("avg_latency_ms", 500)
            })
            
    if not scored_agents:
        return {"status": "no_match", "fallback": "human_review"}
        
    scored_agents.sort(key=lambda x: x["score"], reverse=True)
    selected = scored_agents[0]
    
    # Return immutable routing decision
    return {
        "status": "routed",
        "target_agent": selected["agent_id"],
        "confidence": selected["score"],
        "routing_timestamp": time.time(),
        "task_hash": hashlib.md5(json.dumps(task, sort_keys=True).encode()).hexdigest()
    }

Pattern 2: Execution with Fallback

def execute_agent_with_resilience(
    routing_decision: Dict[str, Any],
    task_payload: Dict[str, Any],
    fallback_agents: List[str],
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute a task via the routed agent with multi-level fallback handling.
    
    Implements agent-specific error recovery: transient timeouts trigger retries,
    capability mismatches trigger fallback routing, and critical failures escalate.
    """
    agent_id = routing_decision["target_agent"]
    attempt = 0
    
    while attempt <= max_retries:
        try:
            response = _invoke_agent_api(agent_id, task_payload)
            
            if response.get("status") == "success":
                return {
                    "status": "completed",
                    "agent_id": agent_id,
                    "result": response["data"],
                    "attempts": attempt + 1,
                    "latency_ms": response.get("latency_ms", 0)
                }
            elif response.get("error_type") == "TRANSIENT_TIMEOUT":
                attempt += 1
                continue
            else:
                raise AgentCapabilityError(response.get("error_msg"))
                
        except AgentCapabilityError as e:
            # Capability mismatch - trigger fallback routing
            if fallback_agents and attempt < max_retries:
                agent_id = fallback_agents[attempt % len(fallback_agents)]
                attempt += 1
                continue
            raise e
            
        except TransientNetworkError:
            attempt += 1
            continue
            
    # All retries exhausted - escalate to human or secondary specialist
    return {
        "status": "escalated",
        "original_agent": routing_decision["target_agent"],
        "fallback_chain_exhausted": True,
        "error_context": "Max retries reached or capability mismatch",
        "escalation_target": "human_operator"
    }

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

• Implement a dependency graph for all tasks before dispatch — only execute nodes whose dependencies are satisfied
• Use a central coordinator that maintains global state and communicates results between parallel agents via immutable messages
• Set explicit timeouts per task and implement circuit breakers: abort parallel execution if error rate exceeds threshold
• Log all inter-agent communications with timestamps, sender, receiver, payload hash, and outcome for debugging

MUST NOT DO

• Do not allow parallel agents to modify shared mutable state without locking — use message-passing or per-task snapshots
• Avoid fan-out patterns that spawn more than 20 parallel tasks simultaneously without rate limiting
• Never start dependent tasks before confirming upstream task completion — verify status, don't assume success
• Do not ignore agent failures during parallel execution; aggregate and report all errors together rather than failing fast on first

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.

• Multi-Agent Systems — Wikipedia Overview
• Building Effective Agents — Anthropic Research
• Multi-Agent System Survey — arXiv
• LLM Agent Survey — Lilian Weng
• Multi-Agent Collaboration — Meta AI Research