How Adobe is Building Agentic Loop to Detect an Issue, Experiment with Various Solutions

NODES 2026 Agenda

How Adobe is Building Agentic Loop to Detect an Issue, Experiment with Various Solutions

Session track: AI Engineering

Session time: 08:00 pm - 08:30 pm UTC November 12

Session description:

At Adobe, our Data Governance system leverages Neo4j AuraDB to persist and retrieve the graph object model. Although our business is multi-tenant, we currently use a single-tenant Neo4j instance, which often leads to noisy-neighbor issues that are difficult to resolve. To address this, we are building an Agentic Loop that observes signals from multiple sources to detect problems, creates a copy of the graph, and runs experiments to validate hypotheses before sharing the solution with human developers. This approach has enabled us to reduce work that would typically take weeks or even months of correlating data across disparate systems to just minutes.

Speakers

Parag Awadhiya

Senior Technical Lead, Adobe

Parag Awadhiya is a Senior Technical Lead at Adobe Experience Platform with focus areas in AI Governance and Responsible AI. Parag has over 19 years of industry experience with extensive experience in AI andBig Data Technologies. Before Adobe Parag worked as a Development Team Lead at Overstock.com where he managed it's Personalization Team, which was responsible for it's Recommendation Engine. Parag holds a Masters Degree in Software Engineering from WGU and Bachelors Degree in Engineering from prestigious Indian Institute of Technology, Varanasi.

Jason Robison

Senior Computer Scientist, Adobe

Jason is a Senior Computer Scientist at Adobe. He has fifteen years in the software development industry. Jason has a particularly strong interest in agentic AI. Prior to Adobe, Jason held other roles at top tech companies, including lead of a globally distributed engineering team at Salesforce. Jason holds a Masters degree from the University of Colorado at Boulder.