I enjoyed reading this but felt like it missed a few of the points on why a lot of companies are indexing heavily on the context layer.
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
> 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?
Let's say that you want to know who your largest customer is, both by order value and volume. I could either:
1. Prompt my agent and deal with writing the prompt, waiting for the agent to sift through all the data (which would be massive), and pay the token costs, all of which has to be repeated everytime I want to answer this question, OR
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
Yea I agree. for most big enterprises, you probably need robust RBAC and multitenancy.
But I do think this pattern of letting agents figure out your company autonomously in some text-like format will be the core pattern going forward
Exactly this. Having spent almost three decades in enterprise context I see a lot of reinvention of something like a poor mans, unstructured, enterprise architecture - because AI agents.
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.
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