1. While AI is capable of driving massive value, chatbots are very rarely the solution
2. You need much more than this sort of text data to represent an enterprise. Timeseries, SAP (and other ERPs), and general relational data is part of building a knowledge graph, ontology, etc
3. Storing it the way this article presents makes it usable for agents, but not humans. Whereas the point of knowledge graph, ontology, etc is to create the same layer for both humans and AI to interact with
If storing it this way makes it usable for agents, then why don't humans just use agents when they need to interact with it?
2. I check my ontology for the answer, probably in a dashboard, and it takes 5 seconds. I have a link I can freely share around my enterprise and I haven't spent token costs.
Whats more, when I have sent my agent out to some tasks (go find out what revenue we're leaving on the table by not selling spot contracts to our biggest customers) my ontology gives me a few bits of data to validate the agents work against. For humans and AI to work together, they need the same context layer
I keep repeating ”what is good for humans in an organization is also good, or even required, for AI agents”.
Imagine every new instance of an AI agent as a new employee. With humans its ok to slowly accumulate knowledge through word of mouth, trail and error and the general inertia of larger orgs almost seem structured (or unstructured) knowledge-wise for this.
AI agents will never be useful in high value operations in a larger orgs without organizational knowledge available and reliable.
Ha, then it'd be doing a great job of internalizing institutional knowledge! Wait a few years and then put another one on top. I'm not sure how these things incorporate new knowledge over time, or handle re-orgs and strategy shifts, or adapt as new verticals are added. Do you need ever increasing numbers of agents to keep things in line?
As much as I'd love to have a perfect example of one of these running - it really would be very beneficial - I do have a vague feeling that these ECL concepts (and similar Enterprise-wide knowledge management AI panaceas) are the 21st century equivalents of trying to build comprehensive expert systems in Prolog.
This is cool though. Agents make it seem more plausible in a way that pure RAG systems don't. I am sure there is mileage in more focused cases (like at the author's startup, or departmentally.)
```The result after running 20 parallel agents on this for ~2 days:```
That's basically saying 'yeah, me and my 20 coworkers figure everything out of your company'. There is just nothing innovative apart from hoping the AI to magically just work.