AI Agents Architect

Ai Agents Architect

Orchestrates intelligent skill selection and execution for ai agents architect 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 architect_agent_routing(
    task_spec: Dict[str, Any],
    agent_registry: List[Dict[str, Any]],
    capability_threshold: float = 0.75
) -> Optional[Dict[str, Any]]:
    """Architect routing for a task by matching against agent capabilities and tool constraints.
    
    Implements capability-based selection rather than generic text matching:
    - Evaluates tool compatibility matrix between task requirements and agent definitions
    - Scores agents based on historical success with similar task patterns
    - Validates dependency chains before routing to prevent dead-end workflows
    
    Args:
        task_spec: Parsed task dictionary containing intent, required_tools, constraints
        agent_registry: List of available agent definitions with capabilities and tool mappings
        capability_threshold: Minimum capability match score required for routing
        
    Returns:
        Selected agent configuration with routing metadata, or None if no match
    """
    if not task_spec.get("required_tools"):
        raise ValueError("Task specification must declare required tools for routing")
        
    if not agent_registry:
        raise ValueError("Agent registry is empty - cannot architect routing")
        
    # Parse task requirements into normalized capability vectors
    required_capabilities = _normalize_tool_requirements(task_spec["required_tools"])
    
    best_agent = None
    best_capability_score = 0.0
    
    for agent in agent_registry:
        # Calculate tool compatibility and capability overlap
        capability_score = _calculate_capability_overlap(required_capabilities, agent["capabilities"])
        dependency_health = _validate_agent_dependencies(agent)
        
        if capability_score > best_capability_score and capability_score >= capability_threshold:
            if dependency_health:
                best_capability_score = capability_score
                best_agent = agent
                
    if best_agent is None:
        return None
        
    # Return immutable routing configuration
    return {
        "target_agent": best_agent["id"],
        "routing_confidence": best_capability_score,
        "required_toolchain": task_spec["required_tools"],
        "fallback_agents": best_agent.get("fallback_chain", []),
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def orchestrate_agent_workflow(
    target_agent: Dict[str, Any],
    task_context: Dict[str, Any],
    fallback_agents: List[Dict[str, Any]],
    max_execution_attempts: int = 2
) -> Dict[str, Any]:
    """Orchestrate agent execution with capability-aware fallback routing.
    
    Implements specialized fallback logic for agent architectures:
    - Routes to fallback agents based on capability degradation, not just errors
    - Preserves task context across agent transitions for state continuity
    - Validates tool availability before each execution attempt
    
    Args:
        target_agent: Primary agent configuration selected by architect_agent_routing
        task_context: Immutable task state and input parameters
        fallback_agents: Ordered list of capability-degraded alternative agents
        max_execution_attempts: Maximum retry attempts before escalating fallback
        
    Returns:
        Execution result with agent transition history and capability metrics
    """
    if not target_agent.get("id"):
        raise ValueError("Target agent must have a valid identifier")
        
    validated_context = _enforce_task_context_schema(task_context)
    
    execution_chain = [target_agent] + fallback_agents
    
    for attempt_idx, agent in enumerate(execution_chain):
        if attempt_idx > max_execution_attempts:
            break
            
        try:
            # Validate tool availability for current agent
            if not _verify_tool_availability(agent["capabilities"]):
                continue
                
            # Execute agent with context preservation
            result = _run_agent_pipeline(agent, validated_context)
            
            return {
                "success": True,
                "agent_executed": agent["id"],
                "execution_path": [a["id"] for a in execution_chain[:attempt_idx+1]],
                "result": result,
                "capability_score": _calculate_current_capability(agent)
            }
            
        except ToolUnavailableError as e:
            # Capability mismatch - route to next agent in chain
            continue
            
        except CriticalStateError as e:
            # Invalid state - halt immediately, do not retry same agent
            raise WorkflowExecutionError(
                f"Critical state failure in {agent['id']}: {str(e)}"
            ) from e
            
    raise WorkflowExecutionError(
        f"Agent workflow exhausted all {len(execution_chain)} capability tiers"
    )

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.

• Building Effective Agents — Anthropic Research
• LLM Agents Survey — Lilian Weng
• Microsoft AI Agent Frameworks Overview
• Multi-Agent Systems — Stanford CS324
• Survey of LLM-Based Agents — arXiv