<div id="readability-page-1" class="page"><div><img src="https://devstarsj.github.io/assets/https://images.unsplash.com/photo-1677442136019-21780ecad995?w=1920&amp;q=80">AI agents have evolved from experimental toys to production-ready systems. In 2026, they handle customer support, code reviews, data analysis, and complex workflows autonomously. Here’s how to build agents that deliver real value.<img src="https://images.unsplash.com/photo-1677442136019-21780ecad995?w=800&amp;q=80" alt="AI visualization"> Photo by <a href="https://unsplash.com/@googledeepmind">Google DeepMind</a> on Unsplash<h2 id="what-makes-a-good-ai-agent">What Makes a Good AI Agent?</h2>The difference between a chatbot and an agent is autonomy. Agents:<ul><li>Execute multi-step tasks without constant human intervention</li><li>Use tools to interact with external systems</li><li>Maintain context across long conversations</li><li>Know when to ask for help vs. proceed independently</li></ul><h2 id="core-architecture-pattern">Core Architecture Pattern</h2>Modern agents follow a simple but powerful loop:<div><pre><code>class Agent:
 def __init__(self, llm, tools, memory):
 self.llm = llm
 self.tools = tools
 self.memory = memory
 
 def run(self, task: str) -&gt; str:
 context = self.memory.retrieve(task)
 
 while not self.is_complete():
 # Reason about next action
 action = self.llm.decide(task, context, self.tools)
 
 if action.type == "tool_call":
 result = self.tools.execute(action.tool, action.params)
 context.append(result)
 elif action.type == "response":
 return action.content
 elif action.type == "ask_human":
 return self.escalate(action.question)
 
 return self.summarize(context)
</code></pre></div>Tools are how agents interact with the world. Well-designed tools make agents more capable:<div><pre><code>@tool(description="Search company knowledge base for relevant documents")
def search_docs(query: str, limit: int = 5) -&gt; list[Document]:
 """
 Clear, specific descriptions help the LLM choose the right tool.
 Return structured data, not raw strings.
 """
 results = vector_db.search(query, top_k=limit)
 return [Document(id=r.id, title=r.title, snippet=r.text[:500]) 
 for r in results]

@tool(description="Create a support ticket in Jira")
def create_ticket(
 title: str,
 description: str,
 priority: Literal["low", "medium", "high", "critical"]
) -&gt; dict:
 """
 Constrained parameters prevent errors.
 Return confirmation with IDs for follow-up.
 """
 ticket = jira.create_issue(
 project="SUPPORT",
 summary=title,
 description=description,
 priority=priority
 )
 return {"ticket_id": ticket.key, "url": ticket.permalink()}
</code></pre></div><img src="https://images.unsplash.com/photo-1485827404703-89b55fcc595e?w=800&amp;q=80" alt="Robot hand"> Photo by <a href="https://unsplash.com/@agk42">Alex Knight</a> on Unsplash<h2 id="memory-strategies">Memory Strategies</h2>Agents need memory to maintain context across sessions:<h3 id="short-term-memory">Short-term Memory</h3>Keep recent conversation in the context window. Simple but limited.<h3 id="long-term-memory">Long-term Memory</h3>Store important facts in a vector database:<div><pre><code>class AgentMemory:
 def __init__(self, vector_store):
 self.vector_store = vector_store
 self.session_buffer = []
 
 def store(self, interaction: str, metadata: dict):
 # Embed and store important interactions
 embedding = embed(interaction)
 self.vector_store.upsert(
 id=uuid4(),
 vector=embedding,
 metadata={**metadata, "timestamp": datetime.now()}
 )
 
 def retrieve(self, query: str, k: int = 10) -&gt; list[str]:
 # Semantic search for relevant memories
 results = self.vector_store.search(embed(query), top_k=k)
 return [r.text for r in results]
</code></pre></div><h3 id="structured-memory">Structured Memory</h3>For specific domains, use structured storage:<div><pre><code># Customer context stored in structured format
customer_memory = {
 "customer_id": "cust_123",
 "preferences": {"timezone": "PST", "language": "en"},
 "recent_orders": [...],
 "open_tickets": [...],
 "sentiment_history": [...]
}
</code></pre></div><h2 id="evaluation-and-testing">Evaluation and Testing</h2>Agents are hard to test because outputs vary. Use these strategies:<div><pre><code># Define success criteria, not exact outputs
def test_refund_agent():
 result = agent.run("I want a refund for order #12345")
 
 # Check actions taken, not specific wording
 assert "refund_initiated" in result.actions
 assert result.refund_amount == 49.99
 assert result.customer_notified == True

# Use LLM-as-judge for open-ended evaluation
def evaluate_response(response: str, criteria: list[str]) -&gt; float:
 prompt = f"""
 Evaluate this agent response against criteria:
 Response: {response}
 Criteria: {criteria}
 Score 0-1 for each criterion.
 """
 scores = llm.evaluate(prompt)
 return sum(scores) / len(scores)
</code></pre></div><h2 id="production-considerations">Production Considerations</h2><h3 id="rate-limiting">Rate Limiting</h3>Agents can make many API calls. Implement backoff:<div><pre><code>@retry(
 stop=stop_after_attempt(3),
 wait=wait_exponential(multiplier=1, min=4, max=60)
)
def call_llm(prompt: str) -&gt; str:
 return llm.complete(prompt)
</code></pre></div><h3 id="observability">Observability</h3>Log everything for debugging:<div><pre><code>import structlog

logger = structlog.get_logger()

def agent_step(action):
 logger.info(
 "agent_action",
 action_type=action.type,
 tool=action.tool if action.tool else None,
 tokens_used=action.token_count,
 latency_ms=action.latency
 )
</code></pre></div><h3 id="cost-control">Cost Control</h3>Set budgets per task:<div><pre><code>class BudgetedAgent:
 def __init__(self, max_tokens: int = 100000):
 self.max_tokens = max_tokens
 self.tokens_used = 0
 
 def step(self, prompt):
 if self.tokens_used &gt;= self.max_tokens:
 raise BudgetExceeded("Token limit reached")
 
 result = self.llm.complete(prompt)
 self.tokens_used += result.usage.total_tokens
 return result
</code></pre></div><h2 id="the-future-multi-agent-systems">The Future: Multi-Agent Systems</h2>The next frontier is agents that collaborate:<div><pre><code># Orchestrator assigns tasks to specialized agents
class Orchestrator:
 def __init__(self):
 self.agents = {
 "researcher": ResearchAgent(),
 "writer": WriterAgent(),
 "reviewer": ReviewerAgent()
 }
 
 def write_article(self, topic: str) -&gt; str:
 # Research phase
 research = self.agents["researcher"].run(
 f"Gather information about {topic}"
 )
 
 # Writing phase
 draft = self.agents["writer"].run(
 f"Write article based on: {research}"
 )
 
 # Review phase
 feedback = self.agents["reviewer"].run(
 f"Review and improve: {draft}"
 )
 
 return self.agents["writer"].run(
 f"Apply feedback: {feedback}"
 )
</code></pre></div><h2 id="key-takeaways">Key Takeaways</h2><ol><li>Start simple: A well-designed tool set beats a complex reasoning engine</li><li>Test actions, not words: Evaluate what agents do, not how they say it</li><li>Plan for failure: Agents will make mistakes—build in guardrails</li><li>Monitor costs: LLM calls add up fast in agentic loops</li><li>Human-in-the-loop: Know when to escalate vs. proceed autonomously</li></ol>The best agents feel invisible—they handle routine work so humans can focus on what matters. <div>이 글이 도움이 되셨다면 공감 및 광고 클릭을 부탁드립니다 :) </div></div></div>

Dev Note

AI agents have evolved from experimental toys to production-ready systems. In 2026, they handle customer support, code reviews, data analysis, and complex workflows autonomously. Here’s how to build agents that deliver real value.
Photo by Google DeepMind on Unsplash
What Makes a Good AI Agent?
The difference between a chatbot and an agent is autonomy. Agents:
Execute multi-step tasks without constant human intervention
Use tools to interact with external systems
Maintain context across long conversations
Know when to ask for help vs. proceed independently
Core Architecture Pattern
Modern agents follow a simple but powerful loop:
class Agent:
    def __init__(self, llm, tools, memory):
        self.llm = llm
        self.tools = tools
        self.memory = memory
    
    def run(self, task: str) -> str:
        context = self.memory.retrieve(task)
        
        while not self.is_complete():
            # Reason about next action
            action = self.llm.decide(task, context, self.tools)
            
            if action.type == "tool_call":
                result = self.tools.execute(action.tool, action.params)
                context.append(result)
            elif action.type == "response":
                return action.content
            elif action.type == "ask_human":
                return self.escalate(action.question)
        
        return self.summarize(context)
Tool Design Principles
Tools are how agents interact with the world. Well-designed tools make agents more capable:
@tool(description="Search company knowledge base for relevant documents")
def search_docs(query: str, limit: int = 5) -> list[Document]:
    """
    Clear, specific descriptions help the LLM choose the right tool.
    Return structured data, not raw strings.
    """
    results = vector_db.search(query, top_k=limit)
    return [Document(id=r.id, title=r.title, snippet=r.text[:500]) 
            for r in results]
@tool(description="Create a support ticket in Jira")
def create_ticket(
    title: str,
    description: str,
    priority: Literal["low", "medium", "high", "critical"]
) -> dict:
    """
    Constrained parameters prevent errors.
    Return confirmation with IDs for follow-up.
    """
    ticket = jira.create_issue(
        project="SUPPORT",
        summary=title,
        description=description,
        priority=priority
    )
    return {"ticket_id": ticket.key, "url": ticket.permalink()}
Photo by Alex Knight on Unsplash
Memory Strategies
Agents need memory to maintain context across sessions:
Short-term Memory
Keep recent conversation in the context window. Simple but limited.
Long-term Memory
Store important facts in a vector database:
class AgentMemory:
    def __init__(self, vector_store):
        self.vector_store = vector_store
        self.session_buffer = []
    
    def store(self, interaction: str, metadata: dict):
        # Embed and store important interactions
        embedding = embed(interaction)
        self.vector_store.upsert(
            id=uuid4(),
            vector=embedding,
            metadata={**metadata, "timestamp": datetime.now()}
        )
    
    def retrieve(self, query: str, k: int = 10) -> list[str]:
        # Semantic search for relevant memories
        results = self.vector_store.search(embed(query), top_k=k)
        return [r.text for r in results]
Structured Memory
For specific domains, use structured storage:
# Customer context stored in structured format
customer_memory = {
    "customer_id": "cust_123",
    "preferences": {"timezone": "PST", "language": "en"},
    "recent_orders": [...],
    "open_tickets": [...],
    "sentiment_history": [...]
}
Evaluation and Testing
Agents are hard to test because outputs vary. Use these strategies:
# Define success criteria, not exact outputs
def test_refund_agent():
    result = agent.run("I want a refund for order #12345")
    
    # Check actions taken, not specific wording
    assert "refund_initiated" in result.actions
    assert result.refund_amount == 49.99
    assert result.customer_notified == True
# Use LLM-as-judge for open-ended evaluation
def evaluate_response(response: str, criteria: list[str]) -> float:
    prompt = f"""
    Evaluate this agent response against criteria:
    Response: {response}
    Criteria: {criteria}
    Score 0-1 for each criterion.
    """
    scores = llm.evaluate(prompt)
    return sum(scores) / len(scores)
Production Considerations
Rate Limiting
Agents can make many API calls. Implement backoff:
@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=60)
)
def call_llm(prompt: str) -> str:
    return llm.complete(prompt)
Observability
Log everything for debugging:
import structlog
logger = structlog.get_logger()
def agent_step(action):
    logger.info(
        "agent_action",
        action_type=action.type,
        tool=action.tool if action.tool else None,
        tokens_used=action.token_count,
        latency_ms=action.latency
    )
Cost Control
Set budgets per task:
class BudgetedAgent:
    def __init__(self, max_tokens: int = 100000):
        self.max_tokens = max_tokens
        self.tokens_used = 0
    
    def step(self, prompt):
        if self.tokens_used >= self.max_tokens:
            raise BudgetExceeded("Token limit reached")
        
        result = self.llm.complete(prompt)
        self.tokens_used += result.usage.total_tokens
        return result
The Future: Multi-Agent Systems
The next frontier is agents that collaborate:
# Orchestrator assigns tasks to specialized agents
class Orchestrator:
    def __init__(self):
        self.agents = {
            "researcher": ResearchAgent(),
            "writer": WriterAgent(),
            "reviewer": ReviewerAgent()
        }
    
    def write_article(self, topic: str) -> str:
        # Research phase
        research = self.agents["researcher"].run(
            f"Gather information about {topic}"
        )
        
        # Writing phase
        draft = self.agents["writer"].run(
            f"Write article based on: {research}"
        )
        
        # Review phase
        feedback = self.agents["reviewer"].run(
            f"Review and improve: {draft}"
        )
        
        return self.agents["writer"].run(
            f"Apply feedback: {feedback}"
        )
Key Takeaways
Start simple: A well-designed tool set beats a complex reasoning engine
Test actions, not words: Evaluate what agents do, not how they say it
Plan for failure: Agents will make mistakes—build in guardrails
Monitor costs: LLM calls add up fast in agentic loops
Human-in-the-loop: Know when to escalate vs. proceed autonomously
The best agents feel invisible—they handle routine work so humans can focus on what matters.

Building AI Agents in 2026: From LLMs to Autonomous Systems

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