Building Production-Ready AI Agents

AI agents are the hottest trend in software. Most examples are demos that break in production. Here's how to build agents that actually work.

What Makes a "Real" Agent?

An AI agent:

1. Perceives its environment (reads data, monitors systems)
2. Reasons about what to do (plans, makes decisions)
3. Acts autonomously (calls APIs, modifies state)
4. Learns from feedback (improves over time)

The key word is autonomously—a chatbot that waits for user input isn't an agent.

Architecture Patterns

Pattern 1: ReAct (Reason + Act)

def react_agent(task: str, tools: list, max_iterations: int = 10):
    """ReAct loop: Thought → Action → Observation → Thought..."""
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": task}
    ]

    for iteration in range(max_iterations):
        response = client.chat.completions.create(
            model="gpt-4-turbo",
            messages=messages,
            tools=tools
        )

        message = response.choices[0].message
        messages.append(message)

        if not message.tool_calls:
            return message.content

        # Execute tool calls
        for tool_call in message.tool_calls:
            result = execute_tool(tool_call.function.name,
                                json.loads(tool_call.function.arguments))
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result)
            })

    raise MaxIterationsExceeded("Agent did not complete task")

Pattern 2: Planning Agents

Break complex tasks into sub-tasks with dependency resolution.

Pattern 3: Multi-Agent Systems

Multiple specialized agents collaborating via a coordinator.

Tool Design

Tools are the interface between your agent and the world.

Good Tool Design

from pydantic import BaseModel, Field

class SearchDatabaseTool(BaseModel):
    """Search the customer database for matching records."""

    query: str = Field(
        description="SQL WHERE clause conditions"
    )
    limit: int = Field(default=10)

    def execute(self) -> list[dict]:
        # Validate query (prevent SQL injection)
        if any(keyword in self.query.upper()
               for keyword in ['DROP', 'DELETE', 'UPDATE']):
            raise ValueError("Query contains forbidden keywords")

        results = db.execute(
            f"SELECT * FROM customers WHERE {self.query} LIMIT {self.limit}"
        ).fetchall()

        return [sanitize_customer_record(r) for r in results]

Key principles:

1. Clear descriptions for the LLM
2. Strong typing prevents garbage inputs
3. Safety checks (assume agent will try to break things)
4. Idempotency for safe retries

Tool Sandboxing

class SandboxedFileSystem:
    """File system access restricted to allowlisted directories."""

    def __init__(self, allowed_paths: list[Path]):
        self.allowed_paths = [p.resolve() for p in allowed_paths]

    def read_file(self, path: str) -> str:
        file_path = Path(path).resolve()

        if not any(file_path.is_relative_to(allowed)
                   for allowed in self.allowed_paths):
            raise PermissionError(f"Access denied: {path}")

        if file_path.stat().st_size > 10 * 1024 * 1024:  # 10MB
            raise ValueError("File too large")

        return file_path.read_text()

Handling Failure Modes

Infinite Loops

class LoopDetector {
  private history: string[] = [];
  private readonly maxRepeats = 3;

  checkForLoop(action: string): void {
    this.history.push(action);

    const recentActions = this.history.slice(-this.maxRepeats);
    if (recentActions.every(a => a === action)) {
      throw new InfiniteLoopError(
        `Agent stuck repeating "${action}"`
      );
    }
  }
}

Hallucinated Tool Calls

Return helpful errors when agent tries to call non-existent tools.

Cost Explosions

Implement rate limiting on API token usage.

Observability

from opentelemetry import trace

@tracer.start_as_current_span("agent_execution")
def run_agent(task: str):
    span = trace.get_current_span()
    span.set_attribute("agent.task", task)

    with tracer.start_as_current_span("agent.reasoning"):
        response = llm.complete(task)

    for tool_call in response.tool_calls:
        with tracer.start_as_current_span(f"tool.{tool_call.name}"):
            result = execute_tool(tool_call.name, tool_call.arguments)

This gives you traces showing exactly what the agent did.

Production Checklist

• Rate limiting (per user, per agent, global)
• Infinite loop detection
• Tool sandboxing and permissions
• Observability (traces, metrics, logs)
• Graceful degradation (fallback to human)
• Cost monitoring and alerts
• Human-in-the-loop for high-risk actions
• Testing (unit, integration, load)
• Incident response plan
• Regular output quality evaluation

Recommended Stack

LLM Providers:

• OpenAI (GPT-4 Turbo) - Best reasoning
• Anthropic (Claude 3.5) - Best for long context
• Google (Gemini 1.5) - Best for multimodal

Agent Frameworks:

• LangGraph (production state machines)
• AutoGen (multi-agent systems)
• Custom (maximum control)

Observability:

• LangSmith (LLM-specific tracing)
• Datadog / Honeycomb
• Helicone (cost tracking)

Conclusion

AI agents are powerful but dangerous. The difference between a demo and production is:

• Robust error handling
• Observability
• Safety constraints
• Testing

Build agents like you build any production system: with paranoia and monitoring.

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