I have build a narrow AI for credit decisioning on a 100B portfolio between 2012 and 2020. This "agent" can make autonomous credit decisions, if and only if the agent is 100% certain that all inputs are accurate. The value comes from the workflow, not the model.
LLMs change this as there is now a general, I like to call them vanilla models, that does not specifically be trained to the data set. Would I use that in this workflow? Likely not.
(a) it is likely that the narrow model is cheaper to operate than a larger model without seeing a substantial benefit in productivity.
(b) in regulated industries we always need to be able to explain why the AI made a decision. If there is no clear governance framework around operating the agent, then we can't use it. Case in point > "nH predict"
Workflows means some organization signed off on what has to be done. Checklists, best practices, etc.
Agents on the other hand have a goal and you have no idea or what they’re going to do to achieve it. I think of an agent’s guardrails as essentially a “blacklist” of actions, while a workflow is a “whitelist”.
To me, agents are a gimmick the same way that real-time chat, or video, is a gimmick. It is good for entertainment but actually has negative value for getting actual work done.
Think of it this way… just as models have a tradeoff between explore and exploit, the agents can be considered as capable of exploration while the workflows exploit best practices. Over time and many tasks, everything is standardized into best practices, so the agents become worse than completely standardized workflows. They may be useful to tinker at the edges but not to make huge decisions. Like maybe agents can be used to set up some personalized hooks for users at the edges of some complex system.
https://medium.com/@falkgottlob/many-ai-agents-are-actually-...