Speaker
Description
At barra, we implement agentic AI projects within complex, large-scale enterprise environments. The enterprise landscape introduces significant organisational complexity as well as rigid requirements for stability, scalability, and economic viability. Navigating these requirements demands a departure from experimental setups. This talk provides an overview of our operational learnings, specifically focusing on the hybrid architectural patterns necessary to reconcile the non-deterministic flexibility of LLMs with the reliability of classical software systems. We will discuss methodologies for choosing optimal agent topologies, such as balancing monolithic versus mesh architectures based on organizational constraints. Furthermore, we address the critical task of integrating deep organizational knowledge into the agent's context. Finally, we explore strategies for observability and output evaluation to handle unpredictable agent behaviors. Our experience offers a perspective on how to mature agentic systems from prototypes into robust, production-ready enterprise tools.