Why AI Agents Need Work Context | Skan AI x Databricks Video
Beyond the Data Layer: Why AI Agents Need Complete Work Context | Skan AI x Databricks
Enterprises have invested heavily in unifying their data, and that foundation matters. But enterprise data platforms record the in-system activity and outcomes of work, not the decisions, handoffs, and exceptions that produced it.
In this session, Dr. Prashanth Southekal, TDWI Research Fellow and Founder at DBP Institute, explores how work context adds the missing operational layer so AI agents can act on how the business really runs.
Key insights
1. Data is the committed state of work
Transaction records and event logs show the final result. The judgment calls and workarounds that happen across applications along the way stay invisible, and those are exactly what an agent needs to learn.
2. Context turns data into execution logic
By observing real work at scale, Skan AI distills patterns, sequences and exception paths into structured context agents can execute against.
3. Better together
Combining a strong data foundation with observed work context gives enterprises a more complete picture, helping teams decide where to deploy AI and giving agents the grounding to perform in production.
Request a demo
Get a tailored demo of the Skan AI platform so you can see how work context strengthens your enterprise AI strategy.
Frequently asked questions
Enterprise data is essential, but it captures outcomes rather than the process itself. Agents need to understand how work actually gets done, including the exceptions and decisions that never reach a database.
Work context is the observed record of how tasks move across people, systems, and applications. Skan AI captures it through privacy-first desktop observation and organizes it into the Context Graph of Work.
Skan AI adds an operational layer on top of your data, showing how work happens across applications so teams can prioritize AI opportunities and ground agents in real execution.
