Hey Kranthi — "teams know their AI can be wrong, what's missing is the engineering discipline to make it reliably right" is a sentence that should be printed on a card and handed to every team shipping an AI product. Everyone acknowledges the problem and almost nobody has a system for managing it.
The abstention accuracy metric is the one that jumps out at me. I build AI agents for businesses and the single most important thing I scope is "when does this agent say I don't know and hand off to a human." An agent that confidently makes up an answer to a question it shouldn't touch is worse than one that does nothing at all. Training the system to know its own edges is harder than training it to be good at its core task — and nobody budgets time for it.
The compound system failure point is real too. Once you've got an agent pulling from a knowledge base, calling APIs, reading emails, and generating responses — a bug in any link of that chain produces a confident wrong answer at the end. And the user has no idea which component failed because the output looks the same either way. That's where the "harness layer" concept earns its weight — you need checkpoints between components, not just evaluation at the end. Really practical framework. This one's going in my reference library.
Hey Kranthi — "teams know their AI can be wrong, what's missing is the engineering discipline to make it reliably right" is a sentence that should be printed on a card and handed to every team shipping an AI product. Everyone acknowledges the problem and almost nobody has a system for managing it.
The abstention accuracy metric is the one that jumps out at me. I build AI agents for businesses and the single most important thing I scope is "when does this agent say I don't know and hand off to a human." An agent that confidently makes up an answer to a question it shouldn't touch is worse than one that does nothing at all. Training the system to know its own edges is harder than training it to be good at its core task — and nobody budgets time for it.
The compound system failure point is real too. Once you've got an agent pulling from a knowledge base, calling APIs, reading emails, and generating responses — a bug in any link of that chain produces a confident wrong answer at the end. And the user has no idea which component failed because the output looks the same either way. That's where the "harness layer" concept earns its weight — you need checkpoints between components, not just evaluation at the end. Really practical framework. This one's going in my reference library.
The financial services compliance assistance example resonates totally. I wrote about the drift state in FS and more broadly too. Good read!
Appreciate it Colleen. How are you handling or exploring this today?