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**Domain**: AI/ML Architecture
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Baseado na classificação ocupacional SOC
Focus blocks, distraction management, and flow state triggers for cognitively demanding work
Internal metacognitive skill for automatic capability discovery — self-triggers when uncertain about available skills
End-to-end academic paper drafting for CHI, HBR, journals, and conferences with venue-specific templates, drafting workflows, and revision strategies.
Patterns for thesis writing, dissertations, research papers, literature reviews, and scholarly work.
Domain knowledge for AI adoption measurement, psychometric instrument development, and appropriate reliance research
Practical application of AIRS psychometric assessment for AI readiness, reliance calibration, and adoption optimization
| name | AI Agent Design Skill |
| description | **Domain**: AI/ML Architecture |
Domain: AI/ML Architecture Inheritance: inheritable Version: 1.0.0 Last Updated: 2026-02-01
Comprehensive patterns for designing AI agents—autonomous systems that use LLMs to reason, plan, and execute multi-step tasks. Covers single-agent architectures, multi-agent orchestration, tool use, memory systems, and production deployment patterns.
┌─────────────────────────────────────────────────────────────┐
│ AI AGENT │
├─────────────────────────────────────────────────────────────┤
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Perceive│ → │ Plan │ → │ Act │ → │ Learn │ │
│ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │
│ ↑ │ │
│ └──────────────────────────────────────────┘ │
│ Feedback Loop │
└─────────────────────────────────────────────────────────────┘
Core Components:
| Aspect | Chatbot | Workflow | Agent |
|---|---|---|---|
| Autonomy | Low | None | High |
| Planning | None | Predefined | Dynamic |
| Tool Use | Limited | Fixed sequence | Flexible |
| Memory | Session only | None | Persistent |
| Error Recovery | Retry/fail | Fail | Reason & adapt |
The foundation of most modern agents:
┌──────────────────────────────────────────┐
│ ReAct Loop │
├──────────────────────────────────────────┤
│ 1. Thought: Reason about the task │
│ 2. Action: Choose and execute a tool │
│ 3. Observation: Process tool output │
│ 4. Repeat until task complete │
└──────────────────────────────────────────┘
Example Trace:
User: What's the weather in Seattle and should I bring an umbrella?
Thought: I need to check Seattle weather to answer this question.
Action: weather_api(location="Seattle, WA")
Observation: {"temp": 52, "condition": "rain", "precipitation": 80%}
Thought: It's raining with 80% precipitation chance. User should bring umbrella.
Action: respond("It's 52°F and raining in Seattle with 80% chance of
precipitation. Yes, definitely bring an umbrella!")
For complex, multi-step tasks:
┌─────────────────────────────────────────────────────────────┐
│ Plan-and-Execute │
├─────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ │
│ │ Planner │ Create high-level plan │
│ └──────┬──────┘ │
│ ↓ │
│ ┌─────────────┐ │
│ │ Executor │ Execute each step │
│ └──────┬──────┘ │
│ ↓ │
│ ┌─────────────┐ │
│ │ Replanner │ Adjust plan based on results │
│ └─────────────┘ │
└─────────────────────────────────────────────────────────────┘
When to Use:
Self-improvement through reflection:
┌─────────────────────────────────────────────────────────────┐
│ Reflexion │
├─────────────────────────────────────────────────────────────┤
│ 1. Attempt task │
│ 2. Evaluate outcome (success/failure) │
│ 3. Generate reflection on what went wrong │
│ 4. Store reflection in memory │
│ 5. Retry with reflection context │
└─────────────────────────────────────────────────────────────┘
Central coordinator delegates to specialized agents:
┌─────────────────────────────────────────────────────────────┐
│ │
│ ┌────────────┐ │
│ │ Supervisor │ │
│ └─────┬──────┘ │
│ ┌─────────────┼─────────────┐ │
│ ↓ ↓ ↓ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Research │ │ Writer │ │ Reviewer │ │
│ │ Agent │ │ Agent │ │ Agent │ │
│ └──────────┘ └──────────┘ └──────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
Use Cases:
Nested supervisor structure for complex organizations:
┌─────────────────────────────────────────────────────────────┐
│ Top Supervisor │
│ ┌─────────────┴─────────────┐ │
│ ↓ ↓ │
│ ┌───────────────┐ ┌───────────────┐ │
│ │ Research Lead │ │ Writing Lead │ │
│ └───────┬───────┘ └───────┬───────┘ │
│ ┌────┴────┐ ┌────┴────┐ │
│ ↓ ↓ ↓ ↓ │
│ ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ │
│ │Web │ │Paper │ │Draft │ │Edit │ │
│ │Search │ │Review │ │Writer │ │Writer │ │
│ └───────┘ └───────┘ └───────┘ └───────┘ │
└─────────────────────────────────────────────────────────────┘
Multiple agents argue to reach better conclusions:
┌─────────────────────────────────────────────────────────────┐
│ │
│ ┌──────────┐ Argue ┌──────────┐ │
│ │ Agent A │ ◄──────────────► │ Agent B │ │
│ │ (Pro) │ │ (Con) │ │
│ └────┬─────┘ └────┬─────┘ │
│ │ │ │
│ └──────────┬──────────────────┘ │
│ ↓ │
│ ┌────────────┐ │
│ │ Judge │ Synthesize best answer │
│ └────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
Benefits:
{
"name": "search_database",
"description": "Search the product database. Returns matching products with prices. Use when user asks about product availability or pricing.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search terms (product name, category, or SKU)"
},
"max_results": {
"type": "integer",
"default": 10,
"description": "Maximum results to return (1-100)"
},
"filters": {
"type": "object",
"properties": {
"min_price": { "type": "number" },
"max_price": { "type": "number" },
"in_stock": { "type": "boolean" }
}
}
},
"required": ["query"]
}
}
Tool Design Principles:
| Strategy | Description | When to Use |
|---|---|---|
| Direct | LLM chooses from all tools | < 10 tools |
| Categorized | Group tools, select category first | 10-50 tools |
| Retrieval | Embed tool descriptions, retrieve relevant | 50+ tools |
| Routing | Specialized selector model | Production scale |
┌─────────────────────────────────────────────────────────────┐
│ Human-in-the-Loop Pattern │
├─────────────────────────────────────────────────────────────┤
│ │
│ Agent Action Request │
│ │ │
│ ↓ │
│ ┌───────────────┐ │
│ │ Risk Check │ │
│ └───────┬───────┘ │
│ │ │
│ Low ──┴── High │
│ │ │ │
│ ↓ ↓ │
│ Execute ┌──────────┐ │
│ Directly │ Human │ │
│ │ Approval │ │
│ └────┬─────┘ │
│ │ │
│ Approve/Reject/Modify │
│ │
└─────────────────────────────────────────────────────────────┘
High-Risk Actions Requiring Approval:
┌─────────────────────────────────────────────────────────────┐
│ Agent Memory │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Working Memory │ │
│ │ Current conversation + recent context (in prompt) │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Short-Term Memory │ │
│ │ Session state, intermediate results (key-value) │ │
│ └─────────────────────────────────────────────────────┘ │
│ │ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Long-Term Memory │ │
│ │ Facts, preferences, history (vector DB + graph) │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
| Type | Storage | Retrieval | Use Case |
|---|---|---|---|
| Episodic | Vector DB | Semantic search | Past conversations, experiences |
| Semantic | Graph DB | Structured query | Facts, relationships, knowledge |
| Procedural | Code/prompts | Direct lookup | How to perform tasks |
| Working | Prompt context | Always present | Current task state |
Summarization: Compress old conversations
Full History → Summarize → Store Summary → Discard Full
Forgetting: Remove low-value memories
Memories → Score by (recency × importance × access_count) → Prune lowest
Consolidation: Merge related memories
Similar Memories → Cluster → Create consolidated memory → Archive originals
Complex Task: "Build a marketing campaign for our new product"
│
┌───────────────┼───────────────┐
↓ ↓ ↓
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Research │ │ Content │ │ Launch │
│ Phase │ │ Phase │ │ Phase │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ │ │
┌──────┴──────┐ ┌───┴───┐ ┌───┴───┐
↓ ↓ ↓ ↓ ↓ ↓
Analyze Survey Create Write Schedule Monitor
Competitors Users Assets Copy Posts Results
Current State: No marketing campaign
Goal State: Campaign live with 10K impressions
│
↓
┌─────────────────────┐
│ Gap Analysis │
│ What's missing? │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Action Generation │
│ What can close gap? │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Action Selection │
│ Best next step? │
└─────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ Error Recovery Ladder │
├─────────────────────────────────────────────────────────────┤
│ │
│ Level 1: Retry │
│ └── Same action, maybe with backoff │
│ │
│ Level 2: Rephrase │
│ └── Reformulate the action (different query) │
│ │
│ Level 3: Alternative │
│ └── Use different tool for same goal │
│ │
│ Level 4: Partial │
│ └── Return partial results, note limitations │
│ │
│ Level 5: Escalate │
│ └── Ask human for help │
│ │
│ Level 6: Abort │
│ └── Cannot complete, explain why │
│ │
└─────────────────────────────────────────────────────────────┘
Agents can get stuck. Detect and break loops:
def detect_loop(action_history, window=5, threshold=0.8):
"""Detect if agent is repeating similar actions."""
if len(action_history) < window * 2:
return False
recent = action_history[-window:]
previous = action_history[-window*2:-window]
# Compare action patterns
similarity = calculate_similarity(recent, previous)
return similarity > threshold
Recovery Actions:
What to Log:
Trace Structure:
Trace: user_request_abc123
├── parse_intent (50ms)
├── plan_generation (200ms)
├── step_1_research
│ ├── tool_call: search_web (150ms)
│ └── tool_call: summarize (100ms)
├── step_2_write
│ └── llm_call: generate_draft (300ms)
└── step_3_review
└── llm_call: critique (200ms)
| Strategy | Implementation |
|---|---|
| Token budgets | Set max tokens per task |
| Step limits | Maximum N actions per request |
| Tiered models | GPT-4 for planning, GPT-3.5 for execution |
| Caching | Cache tool results, LLM responses |
| Early termination | Stop when "good enough" |
┌─────────────────────────────────────────────────────────────┐
│ Safety Layer │
├─────────────────────────────────────────────────────────────┤
│ │
│ Input Validation │
│ ├── Prompt injection detection │
│ ├── PII/sensitive data filtering │
│ └── Request rate limiting │
│ │
│ Action Validation │
│ ├── Tool parameter sanitization │
│ ├── Scope/permission checks │
│ └── Dangerous action blocking │
│ │
│ Output Validation │
│ ├── Content policy compliance │
│ ├── Hallucination detection │
│ └── Sensitive data redaction │
│ │
└─────────────────────────────────────────────────────────────┘
| Framework | Strengths | Best For |
|---|---|---|
| LangChain | Comprehensive, many integrations | Rapid prototyping |
| LangGraph | Stateful, graph-based flows | Complex multi-agent |
| AutoGen | Multi-agent conversations | Research, code gen |
| CrewAI | Role-based teams | Business workflows |
| Semantic Kernel | Enterprise, .NET/Python | Microsoft stack |
| Agents SDK (OpenAI) | Simple, hosted | Quick single-agent |
Problem: Agent makes too many decisions without checkpoints Solution: Add approval gates for significant actions
Problem: No termination conditions Solution: Set max iterations, cost limits, time bounds
Problem: Too many tools confuse the agent Solution: Curate tools, use retrieval for large toolsets
Problem: Accumulating context without pruning Solution: Summarize, forget, consolidate
Problem: One agent does everything Solution: Decompose into specialized sub-agents
✅ Good Fit:
❌ Poor Fit:
AI Agent Design skill — Building autonomous, reliable AI systems