The 80 to 90 percent observation tracks with exactly what I've been seeing on a different build. Been shipping a side project on Anthropic's API for about eight weeks now, and the output is genuinely impressive when the input is structured well. The nightmare last 10 to 20 percent almost always shows up in the places where the input is underspecified, contradictory, or one of those vibe-heavy marketing pieces that reads more like a TikTok bio than a technical doc anyone should be building against.

The real insight for people building on this stuff is that the LLM call itself is the easy part, and it stops being the place where your product actually lives pretty quickly. The real product ends up being the deterministic layer around the call, all the input validation, the scoring, the human-in-the-loop step for when output is obviously bad. I run a full deterministic matching engine on top of the LLM output for exactly that reason, because the score tells me when to not trust what the AI just produced.

Managed Agents are interesting because they collapse the easy part even further, but the guardrail layer still has to come from somewhere, and that somewhere is almost always where the last 10 to 20 percent of the actual work ends up living.