why does reliability fall off a cliff once agents leave the chat box?

a pilot setup, usually a single agent with a broad prompt, does great in sandboxed tests. answers are accurate, instructions get followed. easy to demo, easy to feel good about. then we put it in production. the agent has to chain tool calls, pull from messy internal data, and write back to a system of record. that's when things get weird. the output reads fine. grammatically clean, sounds confident. but it quietly violates a business rule or misses a data constraint that never made it into the context window. what I keep coming back to: the orchestration layer, the boring hard-coded logic around the model, ends up doing more work than the model itself. and it's where most of the bugs live. has anyone figured out a clean way to scale this from "helpful chatbot" to agent that can be trusted without ending up with a maintenance pit?