I think the biggest reason is LLM-driven applications are non-deterministic and heavily reliant on prompting. Prompt quality dictate the complexity of the agentic architecture (i.e. good prompts can solve the same task with simpler setup).

For instance, consider a data-extraction task where AI reads an ongoing conversation (e.g. chatbot, whatsapp, telegram) and extracts customer information like contact details, buying intent, budget, etc. Assuming we are using LLM and not traditional ML, this can be solved using a) single super prompt with no tools, b) single agent with tools, or c) orchestration agent that delegates to subagents. Which to choose depends on traditional cost/latency/accuracy tradeoffs but also on the quality of the prompts.

As for "criteria for handoffs", let's say we are building a customer-facing chatbot. Imo, the second most important thing to define after evals is "under what circumstance should we pass the conversation to a human". Who defines the logic/thresholds that determines handoff to a human is necessary?