The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks

Transitioning AI systems from experimental demos to production requires a structured framework centered on five critical pillars: evaluation, observability, data foundation, orchestration, and governance. Many projects fail by prioritizing model selection over defining business-specific success metrics or establishing robust tracing. Effective production systems rely on deterministic and semantic evaluation layers, including "LLM-as-a-judge" techniques and behavioral checks to monitor tool calls. A solid data foundation must support both question-answering and observability data to ensure accountability and regulatory compliance. Multi-agent orchestration patterns, such as orchestrator-worker or choreography, manage complexity as systems scale. By treating prompts as code and maintaining a living evaluation dataset, organizations can proactively detect drift and resolve failures, as demonstrated by a retail banking chatbot that successfully automated 60% of queries while maintaining strict security and performance standards.