Building Production-Ready AI Agents: A Complete Guide

AI agents represent the next evolution in AI applications—systems that can autonomously reason, plan, and take actions to accomplish goals. This guide covers everything you need to build production-ready agents.

What Are AI Agents?

Unlike simple chatbots, AI agents can:

• Break down complex tasks into manageable steps
• Use tools to interact with external systems
• Maintain context across multiple interactions
• Self-correct when encountering errors

Agent Architecture

A typical AI agent consists of:

1. LLM Brain: The reasoning engine (GPT-4, Claude, etc.)
2. Memory: Short-term and long-term context storage
3. Tools: APIs, databases, calculators, web search
4. Planning: Break goals into actionable steps
5. Execution: Run actions and handle results

import { OpenAI } from 'openai';

class AIAgent {
  private llm: OpenAI;
  private tools: Map<string, Function>;
  private memory: Message[];

  async run(task: string): Promise<string> {
    // 1. Plan the approach
    const plan = await this.createPlan(task);
    
    // 2. Execute each step
    for (const step of plan.steps) {
      const result = await this.executeStep(step);
      this.memory.push({ role: 'assistant', content: result });
    }
    
    // 3. Synthesize final answer
    return this.generateResponse();
  }
}

Common Agent Patterns

1. ReAct (Reasoning + Acting)

The agent alternates between reasoning and acting:

Thought: I need to find the current weather
Action: search_weather(location="San Francisco")
Observation: 72°F, Sunny
Thought: Now I have the weather information
Answer: The weather in SF is 72°F and sunny

2. Plan-and-Execute

Create a complete plan upfront, then execute:

Plan:
1. Retrieve customer data from database
2. Calculate total purchases
3. Check if eligible for discount
4. Generate personalized offer

Execute each step sequentially...

3. Reflexion

Allow agents to self-reflect and improve:

Attempt 1: [fails]
Reflection: The error occurred because I didn't validate input
Attempt 2: [succeeds with validation]

Essential Tools for Agents

Web Search

def web_search(query: str) -> List[str]:
    """Search the web for current information"""
    results = serpapi.search(query)
    return [r['snippet'] for r in results[:5]]

Calculator

def calculate(expression: str) -> float:
    """Safely evaluate mathematical expressions"""
    return eval(expression, {"__builtins__": {}})

Database Access

def query_database(sql: str) -> List[Dict]:
    """Execute read-only SQL queries"""
    return db.execute(sql).fetchall()

Production Considerations

1. Safety and Guardrails

Always implement:

• Input validation: Sanitize all tool inputs
• Output filtering: Remove sensitive information
• Rate limiting: Prevent runaway loops
• Timeouts: Kill long-running tasks

2. Error Handling

async executeWithRetry(
  action: () => Promise<any>,
  maxRetries: number = 3
): Promise<any> {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await action();
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await this.sleep(Math.pow(2, i) * 1000);
    }
  }
}

3. Observability

Track:

• Token usage per task
• Tool call frequency
• Success/failure rates
• Execution time
• Cost per task

Real-World Use Cases

Customer Support Agent

• Answer questions using knowledge base
• Create support tickets
• Escalate to humans when needed

Data Analysis Agent

• Query databases
• Generate visualizations
• Summarize findings in plain English

DevOps Agent

• Monitor system health
• Diagnose issues
• Execute remediation scripts

Cost Optimization

Agents can be expensive. Here’s how to optimize:

1. Cache responses: Avoid re-processing identical queries
2. Use cheaper models for simple tasks: GPT-3.5 for routing
3. Limit tool calls: Set maximum iterations
4. Batch operations: Combine multiple small tasks

Testing Strategies

def test_agent_reliability():
    """Test agent on known tasks"""
    tasks = [
        "Calculate 15% tip on $50",
        "Find articles about RAG",
        "Schedule a meeting for tomorrow"
    ]
    
    for task in tasks:
        result = agent.run(task)
        assert validate_result(task, result)

Common Pitfalls

1. Infinite loops: Agent gets stuck repeating actions
2. Hallucinated tool calls: LLM invents non-existent tools
3. Context overflow: Memory grows too large
4. Tool overuse: Agent calls tools unnecessarily

Conclusion

AI agents unlock powerful automation capabilities, but they require careful design and robust error handling. Start simple, test thoroughly, and scale gradually.

Ready to build AI agents for your organization? Let’s talk.