ai-agent-design-skill

name	ai-agent-design-skill
description	**Domain**: AI/ML Architecture Use when this capability is needed.

AI Agent Design Skill

Domain: AI/ML Architecture Inheritance: inheritable Version: 1.0.0 Last Updated: 2026-02-01

---

Overview

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.

---

Agent Architecture Fundamentals

What Is an AI Agent?

┌─────────────────────────────────────────────────────────────┐
│                      AI AGENT                               │
├─────────────────────────────────────────────────────────────┤
│  ┌─────────┐   ┌─────────┐   ┌─────────┐   ┌─────────┐    │
│  │ Perceive│ → │  Plan   │ → │   Act   │ → │  Learn  │    │
│  └─────────┘   └─────────┘   └─────────┘   └─────────┘    │
│       ↑                                          │         │
│       └──────────────────────────────────────────┘         │
│                    Feedback Loop                           │
└─────────────────────────────────────────────────────────────┘

Core Components:

• Perception: Receive and interpret inputs (user requests, environment state)
• Planning: Reason about goals, decompose tasks, select actions
• Action: Execute tools, API calls, or generate outputs
• Learning: Update memory, refine strategies based on outcomes

Agent vs. Chatbot vs. Workflow

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

---

Single-Agent Patterns

ReAct Pattern (Reasoning + Acting)

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!")

Plan-and-Execute Pattern

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:

• Tasks requiring multiple distinct phases
• When order of operations matters
• When partial failures need recovery

Reflexion Pattern

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                           │
└─────────────────────────────────────────────────────────────┘

---

Multi-Agent Patterns

Supervisor Pattern

Central coordinator delegates to specialized agents:

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│                    ┌────────────┐                           │
│                    │ Supervisor │                           │
│                    └─────┬──────┘                           │
│            ┌─────────────┼─────────────┐                    │
│            ↓             ↓             ↓                    │
│     ┌──────────┐  ┌──────────┐  ┌──────────┐               │
│     │ Research │  │  Writer  │  │ Reviewer │               │
│     │  Agent   │  │  Agent   │  │  Agent   │               │
│     └──────────┘  └──────────┘  └──────────┘               │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Use Cases:

• Content creation pipelines
• Research + analysis + reporting
• Code generation + review + testing

Hierarchical Teams

Nested supervisor structure for complex organizations:

┌─────────────────────────────────────────────────────────────┐
│                    Top Supervisor                           │
│            ┌─────────────┴─────────────┐                    │
│            ↓                           ↓                    │
│    ┌───────────────┐          ┌───────────────┐            │
│    │ Research Lead │          │ Writing Lead  │            │
│    └───────┬───────┘          └───────┬───────┘            │
│       ┌────┴────┐                ┌────┴────┐               │
│       ↓         ↓                ↓         ↓               │
│   ┌───────┐ ┌───────┐        ┌───────┐ ┌───────┐          │
│   │Web    │ │Paper  │        │Draft  │ │Edit   │          │
│   │Search │ │Review │        │Writer │ │Writer │          │
│   └───────┘ └───────┘        └───────┘ └───────┘          │
└─────────────────────────────────────────────────────────────┘

Debate/Adversarial Pattern

Multiple agents argue to reach better conclusions:

┌─────────────────────────────────────────────────────────────┐
│                                                             │
│   ┌──────────┐      Argue       ┌──────────┐               │
│   │ Agent A  │ ◄──────────────► │ Agent B  │               │
│   │ (Pro)    │                  │ (Con)    │               │
│   └────┬─────┘                  └────┬─────┘               │
│        │                             │                      │
│        └──────────┬──────────────────┘                      │
│                   ↓                                         │
│            ┌────────────┐                                   │
│            │   Judge    │  Synthesize best answer           │
│            └────────────┘                                   │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Benefits:

• Reduces hallucination through verification
• Explores multiple perspectives
• Better reasoning on complex questions

---

Tool Use Patterns

Tool Definition Best Practices

{
  "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",
         
              
              
              
        
      
    
     
  

Tool Design Principles:

1. Clear names: Verb + noun (search_database, send_email)
2. Rich descriptions: Include when to use and what it returns
3. Constrained parameters: Enums, ranges, validation
4. Sensible defaults: Reduce required decisions
5. Error handling: Return structured errors, not exceptions

Tool Selection Strategies

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 Tools

┌─────────────────────────────────────────────────────────────┐
│                Human-in-the-Loop Pattern                    │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│   Agent Action Request                                      │
│         │                                                   │
│         ↓                                                   │
│   ┌───────────────┐                                         │
│   │ Risk Check    │                                         │
│   └───────┬───────┘                                         │
│           │                                                 │
│     Low ──┴── High                                          │
│      │         │                                            │
│      ↓         ↓                                            │
│   Execute   ┌──────────┐                                    │
│   Directly  │ Human    │                                    │
│             │ Approval │                                    │
│             └────┬─────┘                                    │
│                  │                                          │
│          Approve/Reject/Modify                              │
│                                                             │
└─────────────────────────────────────────────────────────────┘

High-Risk Actions Requiring Approval:

• Financial transactions
• Data deletion
• External communications
• Permission changes
• Irreversible operations

---

Agent Memory Systems

Memory Architecture

┌─────────────────────────────────────────────────────────────┐
│                    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)     │   │
│  └─────────────────────────────────────────────────────┘   │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Memory Types

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

Memory Management Patterns

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

---

Planning Strategies

Task Decomposition

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

Goal-Oriented Planning

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 Handling & Recovery

Graceful Degradation

┌─────────────────────────────────────────────────────────────┐
│              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                        │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Loop Detection

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:

• Inject reflection prompt: "You seem to be repeating. What's different now?"
• Force tool change: Exclude recently used tools
• Replan: Discard current plan, start fresh
• Escalate: Ask user for clarification

---

Production Considerations

Observability

What to Log:

• Every LLM call (prompt, completion, tokens, latency)
• Tool calls (name, parameters, result, duration)
• State transitions (plan changes, memory updates)
• Errors and recovery attempts

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)

Cost Control

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 Guardrails

┌─────────────────────────────────────────────────────────────┐
│                  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 Comparison

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

---

Anti-Patterns

❌ Over-Autonomous Agent

Problem: Agent makes too many decisions without checkpoints Solution: Add approval gates for significant actions

❌ Unbounded Loops

Problem: No termination conditions Solution: Set max iterations, cost limits, time bounds

❌ Tool Explosion

Problem: Too many tools confuse the agent Solution: Curate tools, use retrieval for large toolsets

❌ Memory Bloat

Problem: Accumulating context without pruning Solution: Summarize, forget, consolidate

❌ Monolithic Agent

Problem: One agent does everything Solution: Decompose into specialized sub-agents

---

Activation Triggers

• "agent", "autonomous", "multi-agent"
• "tool use", "function calling"
• "ReAct", "plan and execute"
• "agent memory", "agent planning"
• "orchestration", "supervisor agent"
• "LangChain", "LangGraph", "AutoGen", "CrewAI"

---

Quick Reference

Agent Design Checklist

• Define clear agent persona and capabilities
• Design minimal, well-described tool set
• Implement appropriate memory architecture
• Add human-in-the-loop for high-risk actions
• Set up observability (logging, tracing)
• Configure safety guardrails
• Test with adversarial inputs
• Plan for cost control and scaling

When to Use Agents

✅ Good Fit:

• Open-ended research tasks
• Multi-step workflows with decisions
• Tasks requiring tool orchestration
• Personalized, context-aware interactions

❌ Poor Fit:

• Simple Q&A (use RAG)
• Deterministic workflows (use code)
• High-stakes with no human oversight
• Real-time, latency-critical applications

---

AI Agent Design skill — Building autonomous, reliable AI systems

---

Converted and distributed by TomeVault — claim your Tome and manage your conversions.