Architecture and Lessons from Building Enterprise AI Agents

Building reliable enterprise AI agents requires considerably more than selecting a capable language model. Teams must make difficult decisions concerning architecture, orchestration, tool use, memory, permissions, observability, human oversight, and failure recovery.

In this technical session, we shall share the architecture behind the enterprise AI agents we have built, together with the mistakes, trade-offs, and lessons encountered along the way. The purpose is practical: to help technical and product leaders avoid expensive false starts and make better architectural decisions sooner.

Attend to learn:

• The architecture required to move from an AI prototype to a production agent
• Where OpenAI, Claude, and other foundation models fit within the broader system
• How to approach tools, memory, workflows, permissions, and human oversight
• Which architectural decisions proved effective, and which we would approach differently
• How to avoid some of the mistakes we made while building enterprise agents