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Ie, the derivation of “knowledge units” will be passive. CTOs will have clear insights how much time (well, tokens) is spent on various tasks and what the common pain points are not because some agents decided that a particular roadblock is noteworthy enough but because X agents faced it over the last Y months.
What I think we will see in the future is company-wide analysis of anonymised communications with agents, and derivations of common pain points and themes based on that. Ie, the derivation of “knowledge units” will be passive. CTOs will have clear insights how much time (well, tokens) is spent on various tasks and what the common pain points are not because some agents decided that a particular roadblock is noteworth…
How is this pronounced phonetically?
What I think we will see in the future is company-wide analysis of anonymised communications with agents, and derivations of common pain points and themes based on that. Ie, the derivation of “knowledge units” will be passive. CTOs will have clear insights how much time (well, tokens) is spent on various tasks and what the common pain points are not because some agents decided that a particular roadblock is noteworth…
How will you derive pain points and roadblocks if you don’t trust LLMs to identify them?
Certainly worthy of experimenting with. Hope it goes well
Earlier quoted context omitted.
How will you derive pain points and roadblocks if you don’t trust LLMs to identify them?
I trust that an LLM can fix a problem without the help of other agents that are barely different from it. What it lacks is the context to identify which problems are systemic and the means to fix systemic problems. For that you need aggregate data processing.
Earlier quoted context omitted.
How will you derive pain points and roadblocks if you don’t trust LLMs to identify them?
I trust that an LLM can fix a problem without the help of other agents that are barely different from it. What it lacks is the context to identify which problems are systemic and the means to fix systemic problems. For that you need aggregate data processing.
Earlier quoted context omitted.
I trust that an LLM can fix a problem without the help of other agents that are barely different from it. What it lacks is the context to identify which problems are systemic and the means to fix systemic problems. For that you need aggregate data processing.
What I mean is, how do you identify a “problem” in the first place?
It’s like what they do in support or sales. They have conversational data and they use it to improve processes. Now it’s possible with code without any sort of proactive inquiry from chatbots.