I was reading this Anthropic engineering article on building agents, and one section stood out for its clarity and honesty:
https://www.anthropic.com/engineering/building-agents-with-the-claude-agent-sdk
The part on semantic search is especially worth attention.
Semantic search works by chunking context, embedding it as vectors, and retrieving results based on conceptual similarity. It is fast. It scales well. It looks clean on the architecture diagrams.
But it comes with real tradeoffs.
It is less accurate for complex reasoning.
It is harder to maintain as the context evolves.
It is less transparent, which makes debugging and trust difficult.
Anthropic makes a counterintuitive recommendation. Start with agentic search.
Agentic search allows the system to reason step by step, decide what to look for next, and adapt based on intermediate findings. It is slower, but it is explicit, debuggable, and closer to how real problem-solving works.
Semantic search should be added only when you truly need speed or broader variation. Not by default. Not because it is trendy.
This highlights a deeper principle in AI system design.
Correctness comes before performance.
Clarity comes before scale.
Product value comes before architectural elegance.
As AI native engineers, the goal is not to stack advanced tools. The goal is to build the smallest system that reliably works, then optimize with intent.
Start with reasoning. Optimize later.