8 Essential AI Agent Design Patterns for Developers

Core Concepts: What Makes an Agent?

AI agents extend standard LLMs by combining four key components:

1. Planning – Autonomous task decomposition and sequencing
2. Tool Use – Integration with APIs, databases, and external services
3. Memory – Context preservation across interactions
4. Dynamic Decision-Making – Adaptive responses based on real-time inputs

Unlike single-turn LLM queries, agents operate in loops: perceive → plan → act → refine.

Pattern 1: Reflection

Principle: Self-review outputs before finalizing to catch errors. Workflow:

def reflective_agent(task):
    initial_output = llm.generate(task)
    critique = llm.generate(f"Identify flaws in: {initial_output}")
    final_output = llm.generate(f"Revise this: {initial_output} | Issues: {critique}")
    return final_output

Use Case: Code generation (e.g., fixing syntax errors post-draft).

Pattern 2: Tool Use

Principle: Augment LLMs with external capabilities via APIs. Key Tools:

• Search APIs (Google, SerpAPI)
• Code interpreters (Python REPL)
• Database connectors Example:

agent_query = "Current Bitcoin price"
if needs_financial_data(agent_query):
    results = call_api("coinmarketcap")
    return llm.summarize(results)

Pattern 3: ReAct (Reasoning + Action)

Principle: Transparent step-by-step execution with observable reasoning. Structure:

1. Thought: “I need weather data for Tokyo”
2. Action: Call WeatherAPI
3. Observation: “Temperature: 22°C, rainy”
4. Repeat until task completion

Pattern 4: Planning

Principle: Pre-execution task decomposition. Example: Article Writing

1. Research topic → 2. Create outline → 3. Draft sections → 4. Add citations → 5. Proofread → 6. Format

Pattern 5: Multi-Agent Collaboration

Architecture:

• Researcher (gathers data)
• Writer (drafts content)
• Reviewer (quality checks)
• Publisher (formats output) Frameworks: AutoGen (Microsoft), CrewAI, MetaGPT

Pattern 6: Routing

Principle: Direct inputs to specialized sub-agents. Implementation:

def router(query):
    if "refund" in query:
        return billing_agent(query)
    elif "error 404" in query:
        return tech_support_agent(query)

Pattern 7: Parallelization

Principle: Concurrent execution of independent tasks. Use Case: Market analysis from four simultaneous perspectives: technical, business, user experience, legal.

Pattern 8: Prompt Chaining

Principle: Break complex tasks into sequential prompts. Example: Content Creation

1. “Search for AI ethics trends 2024” → 2. “Generate outline from these sources” → 3. “Draft 800-word article” → 4. “Polish for academic tone”

Getting Started Roadmap

Week 1: Reflection + Tool Use (build a self-debugging code generator) Week 2: ReAct + Prompt Chaining (create a research assistant) Week 3: Routing + Parallelization (develop a customer support triage system) Week 4: Planning + Multi-Agent (design a content production pipeline)

(Figures and case details in this article are as reported in the source material and have not been independently verified.)

Series: AI Practice