AI Agents Are Cool… But Let’s Not Jump Into the Deep End First 🏊‍♂️

So I was explaining to someone recently how building with LLMs doesn’t always mean creating full-blown AI agents. You know what I mean - those systems where the AI decides what tools to use, when to use them, and orchestrates workflows all by itself.

It felt great to share a high-level, intuitive view of why starting with agents is often the wrong move. And guess what? Anthropic’s piece Building Effective Agents and High Growth Engineers post Stop Building Agents articulate these thoughts beautifully.

💡 TL;DR

Agents are like the Lamborghinis of AI—they look flashy and powerful, but they’re not always practical for the daily commute. In most cases, especially early on, you’re better off with simpler, more predictable patterns:

  • Prompt Chaining: When tasks flow in sequence.
  • Routing: For directing different inputs to the right handler.
  • Orchestrator-Worker: When big tasks need dynamic subtasks.
  • Evaluator-Optimizer: For polishing outputs until they shine.
  • Parallelization: To speed things up when calls don’t depend on each other.

🤖 When to Actually Use Agents?

Only when your workflow is too unpredictable or creative for predefined steps—like in data exploration, ideation, or code refactoring with human oversight.

🚫 And please… don’t use agents high-stakes decisions especially in Financial Transactions. That’s asking for chaos.

This isn’t about stifling innovation—it’s about using the right tool for the job and avoiding unnecessary complexity. Especially if you’re just getting started, simplicity wins.

#AI #GenAI #LLM #MachineLearning #ArtificialIntelligence #Tech #Engineering #ProductManagement #Innovation #AgenticAI