ai-agent-development

# Ai Agent Development Orchestrates intelligent skill selection and execution for ai agent development 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 ```python def evaluate_agent_skill_pool( request: AgentRequest, skill_registry: List[SkillMetadata], historical_metrics: Dict[str, float] ) -> Optional[RankedSkill]: """Evaluate available agent skills using multi-factor scoring for AI development tasks. Implements Law 2 (Parse at boundary) by validating request structure first. Calculates weighted scores based on trigger overlap, historical success rate, and dependency health status. """ if not request.intent or not skill_registry: raise ValueError("Request intent and skill registry are required") scored_candidates = [] for skill in skill_registry: # Law 3: Return new structures, never mutate registry trigger_match = _calculate_semantic_overlap(request.intent, skill.triggers) history_score = historical_metrics.get(skill.id, 0.5) dep_health = _check_dependency_status(skill.dependencies) # Weighted multi-factor scoring composite_score = ( 0.4 * trigger_match + 0.35 * history_score + 0.25 * dep_health ) if composite_score >= 0.65: scored_candidates.append(RankedSkill( skill=skill, confidence=composite_score, breakdown={"trigger": trigger_match, "history": history_score, "deps": dep_health} )) scored_candidates.sort(key=lambda x: x.confidence, reverse=True) return scored_candidates[0] if scored_candidates else None ``` ### Pattern 2: Execution with Fallback ```python def run_agent_skill_with_resilience( skill: RankedSkill, execution_context: Dict, fallback_registry: List[SkillMetadata] ) -> ExecutionResult: """Execute an AI agent skill with a structured fallback chain. Implements Law 4 (Fail Fast/Loud) by immediately halting on invalid states. Applies a 2-level fallback: parameter adjustment -> alternative skill -> human escalation. """ if not _validate_execution_context(execution_context, skill.skill.schema): raise InvalidStateError(f"Context violates {skill.skill.id} schema requirements") attempts = 0 max_attempts = 2 while attempts <= max_attempts: try: result = skill.skill.executor(execution_context) _update_confidence_score(skill.skill.id, success=True) return ExecutionResult( success=True, skill_id=skill.skill.id, output=result, confidence=skill.confidence, attempts=attempts + 1 ) except SchemaValidationError as e: raise InvalidStateError(f"Hard validation failure in {skill.skill.id}: {e}") from e except TransientAgentError as e: attempts += 1 if attempts > max_attempts: break execution_context = _adjust_parameters_for_retry(execution_context, e) # Fallback chain exhausted alt_skill = _find_alternative_skill(fallback_registry, skill.skill.id) if alt_skill: return run_agent_skill_with_resilience(alt_skill, execution_context, fallback_registry) _log_critical_failure(skill.skill.id, execution_context) raise SkillExecutionError(f"All fallbacks exhausted for {skill.skill.id}. Escalating to 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 | Skill | Purpose | |---|---| | `agent-confidence-based-selector` | Intelligent skill selection with multi-factor scoring and fallback chains | | `agent-task-routing` | Routing tasks to the most appropriate specialized 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. - [LLM Agents Survey — Lilian Weng](https://lilianweng.github.io/posts/2023-06-23-agent/) - [Survey of LLM-Based Agents — Stanford HAI](https://arxiv.org/abs/2308.11432) - [Building Effective Agents — Anthropic Research](https://www.anthropic.com/research/building-effective-agents) - [LangGraph Multi-Agent Documentation](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) - [Agent Architecture Patterns — Microsoft AI Research](https://www.microsoft.com/en-us/research/project/language-models-for-agents/)