Building AI Agents in 2026: From LLMs to Autonomous Systems

on Ai, Agents, Llm, Claude, Gpt, Automation, Machine learning



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.

AI visualization 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()}

Robot hand 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

  1. Start simple: A well-designed tool set beats a complex reasoning engine
  2. Test actions, not words: Evaluate what agents do, not how they say it
  3. Plan for failure: Agents will make mistakes—build in guardrails
  4. Monitor costs: LLM calls add up fast in agentic loops
  5. 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.

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