What Makes a Dialog Agent Useful?

Task completion in multi-turn agents tracks more with turn-boundary detection than model size. GPT-4o on a 6-step booking flow hits ~70% completion; smaller models with structured slot-filling close that gap to under 10%.

In production RAG pipelines, the real break point is ambiguous queries with no clarification loop — not retrieval accuracy. Most teams wire a single LLM call and call it done. LangGraph's interrupt() node exists for this. Blaming hallucinations while skipping dialog state is just expensive guessing.

Ship customer-facing flows over 3 turns? Add explicit state via LangGraph. Single-turn Q&A bots: ignore this entirely.