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Agents are ready, but your architecture probably isn't
Catherine Brown
- Source
- Databricks
- Published
- Added to Yomu
Summary
Enterprise AI initiatives often produce activity rather than value because organizations start with technology instead of a defined outcome and overlook data architecture, governance, and semantic context. Agentic systems add risk when they can send messages, update records, place orders, or delete records, making permissions and situational controls essential. The discussion argues that dashboards and batch pipelines are poorly matched to low-latency, high-scale agent workloads, which require transactional infrastructure alongside existing analytics. Lakebase is presented as that transactional foundation, while AgentBricks, Databricks Apps, and Genie provide agent development and monitoring, application delivery, and conversational data access. The recommended path is to define success first, isolate a focused pilot, learn what works, and redesign underlying processes rather than merely add AI to them.
Context
Enterprise organizations are moving from AI experimentation and task automation toward agentic systems that take actions, but many still face AI sprawl, siloed data, incomplete governance, disconnected batch systems, and architectures designed for analytics rather than low-latency transactional applications.
Approach / What changed
The discussion recommends starting with a measurable business outcome, building governance for agent permissions and behavior, and running a focused pilot team in isolation from legacy constraints. It presents Lakebase as a transactional database for agentic workloads alongside the existing analytics layer, with AgentBricks, Databricks Apps, and Genie covering agent development and monitoring, application delivery, and conversational data access.
Takeaways
- Agents need governance that covers their actions, permissions, destinations, and interactions with other agents; human permissions alone are insufficient because agents lack situational awareness.
- Dashboards and batch pipelines introduce latency and are mismatched with agentic applications that need low-latency service for many simultaneous users at scale.
- A focused pilot with a clearly defined outcome can reveal what works before learnings are scaled across the broader organization, while process redesign is needed for transformational results.