You’ve been asked to build an AI agent. Exciting! But before you start frantically Googling “how to agent,” let’s pause. Because here’s the truth: AI agents aren’t magic. Sometimes, they’re just overkill.

Yes, I’m being that person. The one who says, “Hold on, maybe we don’t need to reinvent the wheel here.” To avoid building a Rube Goldberg machine when a lever would suffice, ask yourself these five questions first:

📚 Data Type: Fuzzy or Structured?

If your data is structured (think form fields, sensor readings, or anything that fits neatly into a spreadsheet), skip the AI agent. A basic rule-based workflow will do the job reliably. Save the AI for when your data looks like a teenager’s bedroom: messy emails, half-finished documents, and cryptic user requests. That’s where agents shine.

⚙ Process Complexity: Straightforward or Chaotic?

Routine, linear tasks (like moving data between systems or generating weekly reports) are perfect for traditional automation. They’re predictable and won’t suddenly decide to reimagine your process as abstract performance art. But if your workflow has more twists than a mystery novel (“If the user says X, check database Y, then notify Bob unless it’s a holiday…”), an AI agent’s adaptability might save the day.

💡 Context Needed: Minimal or Encyclopedia?

Does your task require minimal data? Think copying files or calculating totals. If so, adding an AI layer is like hiring a Michelin-starred chef to make a microwave dish. But if your use case demands synthesizing mountains of context—emails, documents, historical records—to make decisions, let the agent play librarian.

🗣 Natural Language: Necessary or Optional?

If users need to chat with the system (“Find invoices from Bob, but skip anything before March…”), an AI agent’s ability to parse messy language is worth the effort. But if your users are perfectly happy clicking buttons or filling out forms, don’t force a chatbot on them. Not every problem needs a conversation.

🙈 Error Tolerance: Strict or Flexible?

AI agents are unpredictable. They’ll occasionally misinterpret requests or hallucinate answers. If your process requires 100% reliability (e.g., compliance checks, financial approvals), stick to rules. Your auditors will thank you. But if you can tolerate occasional hiccups and iterate over time (e.g., drafting internal docs), agents might be worth the trade-off.

As I argued in my last post, simplicity wins. AI agents are powerful for handling ambiguity and dynamic inputs, but they add complexity, cost, and unpredictability. Ask yourself: *Does this problem actually require “intelligence,” or can a straightforward solution work?

Your goal isn’t to win a “Most Cutting-Edge Project” award. It’s to build something that works—without giving your team migraines. So unless you truly need that AI layer, embrace the boring. Your future self (and your sanity) will appreciate it. 😉