Practice — Frameworks Landscape: LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, Assistants API (5 questions)

A startup built its first customer-facing AI feature in a two-week sprint using a general-purpose orchestration framework (chains/graphs of LLM calls, retrievers and tools) because it had the fastest path to a demo. The demo went well, the feature is now live, and traffic is growing. The on-call engineer just spent six hours debugging a latency spike and couldn't easily find the literal prompt and token count sent to the model for the slow requests — everything was buried inside the framework's chain internals.

1. Explain why this outcome was predictable from the framework's core abstraction, not just bad luck.
2. Would you recommend ripping the framework out? Justify your answer.
3. Propose a concrete plan for what changes now, in priority order.