Agentic AI systems go beyond simple LLM interactions—they require autonomous reasoning, multi-step planning, tool orchestration, and persistent memory to execute complex enterprise workflows. This blog explores the architectural patterns and production considerations for building such systems on Databricks, leveraging Mosaic AI Agent Framework, Vector Search, Unity Catalog governance, and MLflow for end-to-end lifecycle management. From evaluation strategies to deployment guardrails, these are the hard-earned lessons from real-world implementations.
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