🧠 One answer to "how do you give agents accurate business context?": auto-build ontologies and a knowledge graph from existing data, then serve it via MCP. An open-source project from AWS. Title: Context Ontology Accelerator (aws/context-ontology-accelerator) URL: A semantic context layer that gives AI agents validated business context. Three highlights stand out. 🔎 A Scan → Model → Serve pipeline Connect diverse data sources to discover schemas and ingest documents (Scan), induce formal ontologies and build a unified knowledge graph (Model), and expose it via VKG SPARQL federation and MCP (Serve). It derives semantic structure from existing data, no manual knowledge engineering. ✅ Consistency validated by reasoning engines HermiT and ELK validate ontology consistency, enabling rule-based checks from formal constraints. Agents query validated business rules instead of relying on memorized training data. 🏗 AWS-native and production-minded Deployed via AWS CDK, a VKG powered by Ontop, and namespace RBAC (owner/maintainer/data-steward/data-analyst). API design via Smithy, UI in React + Cloudscape. A solid foundation for running agents within validated context while keeping explainability. #KnowledgeGraph# #AIAgents#

There has been this sort of persistent "stuckness" in AI Investing where the promise of what is possible has not shown up in practical tools for investors that have meaningfully bent the curve of alpha. To me there is a very fundamental reason for this. AI can be very helpful for one off situations: let me track management commentary over 12 quarters, summarize my morning inbox, run a compensation tiers analysis with the proxy, etc. Super helpful things (if not still a bit challenging to set your system up in a seamlessly usable way). The conceptual unlock is the concept of seamlessly "connecting the dots". What does readthrough from Company A mean to revenue forecasts at Company B? How does the move in the 10-Year impact all of my companies with variable rate debt? Seamless, automated. Some of the smartest AI builders I know have been focused on this concept of company & industry ontologies. What does that mean? Better to give you an example. I was a healthcare services investor for 10+ years. The concept of medical trend was a key thing to get right. Was trend accelerating or decelerating? Would the HMOs beat or miss on MLR? Did they price their new members correctly? What is this new Part B drug launch doing to loss ratios? These questions had big implications across the healthcare service food chain. And there were various company-level and industry data sources to track (medical claims, etc), and a lot of the value in identifying these inflections came from being out in the field, talking to companies, working to identify inflections first. I made a lot of money doing this better than the average healthcare investor. If i can press a button and re-build this system, holy crap it's so exciting. I can then actually automate all of that alpha. But, not so fast. The complexity inherent in this whole system is immense, and the number of failure rates along the supply chain is huge, and can be as simple as not normalizing MLRs for an acquisition or market exit and thus having a distorted baseline to drive the model. Bulling in member files, state level reserves, the ongoing research pipe, and the clean substrate of the financial modeling corpus is a *necessary* element. As in alternative data, the first order observation is quickly priced by quants, and all the alpha is in the 2nd order observation (i.e. "UNH missed on MA MLR but it's due to their Stars reduction in this county and HUM actually shrunk there, so dumb market is selling off HUM but it's a misread and a big buy", for example.) The dept of normalizing MLRs, cleaning up the models, then connecting all of this together into one tracking system is effectively an industry and company ontology. How the economic relationships of an industry and company interact. I've talked to a number of people attacking this bottleneck in AI Investing, and they have been working to build ontologies. Sometimes, with simple examples, they work. But for complicated examples like the above, they are terrible. There is just too much complexity. And, my prior is all of the fundamental alpha is in the complicated ontologies. There is one other fundamental truth. Investors will not trust an output without interrogating the logic chain. "HUM will beat on MLR". OK, show me every input and the full logic chain as to why. This is very hard to do in python. I think it's super obvious you do this in Excel. It's the only path forward I see, honestly. Particularly to build an "over my dead body" system for institutional investors that *already* have this system built out. Let me automate that for you. "But Brett, Excel sucks and python is better". Ok, maybe someday. But navigating the cultural change of human investors needs to be considered. And, as a person who spent 10+ years working in or adjacent to quantamental strategies (with pretty good intel on what is being built out there), no one is even close to being able to building a trend ontology as described above. It won't happen in python. This is why AI Excel is the key accelerant in AI Investing, in my opinion.

"There is an ontological difference, even before an aesthetic one, between art and what a machine can generate through statistical calculation based on millions of images created by others," the pope wrote. "Algorithms lack the spark of humanity."

orld renowned Oncologist Professor Angus Dalgleish: “I’m terrified what they’ve done Fauci and Gates have injected the world with CANCER!” The mRNA shots were NEVER safe. They are experimental gene therapies complete with multiple genetic inserts that never belonged in the body