Enterprise Agents Have a Structure Problem
summary
The paper claims most enterprise agents fail because models lack a machine‑readable metadata layer and instead guess at raw schemas and KBs; the proposed fix is building semantic layer + canonical queries and, when needed, a database graph, which the presenter estimates solves about 80% of failures with the first two layers. Practical tooling includes semantic retrieval, metadata graphs, embedding live datasources and a feedback loop for logging, evaluation, and automated context updates; the harder database graph adds flexibility but costs more to maintain.
AI-written summary · about
chapters (6)
- 0:00The state of enterprise agents
- 1:02Understanding the challenges
- 2:45Solving agent ambiguity
- 5:42Addressing data staleness
- 8:36Managing user preference
- 11:28Conclusion and future outlook
related talks
20mThe Three Ways AI Agents Fail. Here's How to Fix Yours.
45mNever Send a Human to do an Agent's Search
21m3 ingredients for building reliable enterprise agents
58mKeynote: Your Agents Are Smart. Your Data Model Isn’t
21mOvercoming Data Barriers for Successful AI Projects
28mAI Agents as Architectural Strategists | Aligning System Design with Business Goals
25mFrom Chaos to Control: Building Enterprise-Grade LLM Agents That Actually Work
38mPanel Discussion: AI Needs a Foundation That Never Fails