Best practices for building with AI agents

This week, Anthropic dropped an excellent blog post on best practices for building with AI agents

This week, Anthropic dropped an excellent blog post on best practices for building with AI agents, and I couldn’t agree more with their recommendation against over-reliance on agentic frameworks. While these frameworks can simplify tasks like API calls, tool chaining, and response parsing, they often introduce unnecessary abstraction.

This not only makes debugging harder but also encourages unnecessary complexity when simpler solutions would suffice. At first, it feels like you’re moving faster, but soon you find yourself trying to fit your problem into their assumptions and abstractions. More often than not, this approach makes your solution more brittle and overly complex.

Another key challenge is that "agentic" software fundamentally differs from traditional workflow software. There’s no consensus yet on the best way to build them, and the frameworks in this space are evolving rapidly. Relying heavily on them can feel like trying to build on shifting sands.

In my experience, building and rebuilding from scratch is often the best way to truly wrestle with software design for agents. It forces you to make deliberate decisions about:

• The right abstraction for your needs

• Synchronization and orchestration between agents

• Inputs and outputs for each module

• Human-in-the-loop patterns.

Starting with direct API calls and carefully adding layers of abstraction as needed has served me well.

If you do use a framework, Anthropic’s advice remains spot on: make sure you deeply understand what’s happening under the hood.

Author

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