The Case for Boring AI Infrastructure

Hot Take: Your AI Stack Should Be Boring

Every week there's a new framework. LangChain, CrewAI, AutoGen, DSPy... Meanwhile, the most reliable AI systems I've seen in production use:

• Plain HTTP calls to model APIs
• PostgreSQL for state management
• Simple Python functions for "tools"
• Cron jobs for scheduling

Why Boring Wins

Debuggability: When your agent chain fails at 3 AM, you want to read a stack trace, not decode framework abstractions.

Hiring: Every developer knows HTTP + SQL. Not everyone knows the framework-of-the-month.

Longevity: That shiny framework might pivot or die. requests.post() will outlive us all.

When to Use a Framework

Frameworks shine for: - Rapid prototyping (prove the concept fast) - Complex agent graphs (10+ tools, branching logic) - Teams new to AI (guardrails help)

But if you're building for production, consider starting boring.