Are we overcomplicating AI applications?

A lot of AI projects now involve RAG, agents, vector databases, tool calling, guardrails, evaluations, and multiple APIs.

But does every AI application really need an agentic architecture?

For a simple use case, a well-designed LLM + retrieval layer might be enough. Adding autonomous agents can introduce more latency, cost, and failure points.

Where do you draw the line between useful AI architecture and unnecessary complexity?

Curious to hear what developers are actually choosing in production.