|
An AI agent handling a customer request may need to pull account history, check the latest policy, and determine what it's authorized to do. If it retrieves an outdated policy or misses a key piece of account history, it can make the wrong decision even when the underlying model works as intended.
Context engineering is the work of deciding what information agents receive, when they receive it, and how they can use it. In production, those decisions shape how teams connect data and knowledge sources, enforce permissions, and keep context current across multi-step workflows.
Join DBTA for a roundtable on how to architect for trusted context as AI agents move into production. Panelists will discuss where different approaches fit and how to balance the quality of the context agents receive with the speed and cost of retrieving it.
What You'll Learn:
- Where vector search, knowledge graphs, and semantic layers fit
- How to keep context fresh and enforce access controls
- What agents should remember across workflow steps
- How to trace agent decisions and diagnose context failures
Register Now to attend the webinar Context Engineering for Agentic AI: Architecting for Trusted Context. Don't miss this live event on Tuesday, January 19, 11 AM PT / 2 PM ET.
Interested in Sponsoring?
|