Sandipan Bhaumik

This talk outlines a production AI playbook, a framework designed to guide the deployment of AI systems at an enterprise scale. It addresses common pitfalls encountered when moving AI from demos to production, emphasizing the need for structured approaches to evaluation, observability, data foundation, orchestration, and governance. The framework aims to ensure AI systems are measurable, accountable, and reliable in real-world applications.

This talk addresses the challenges of scaling multi-agent AI systems beyond a single agent, emphasizing that complexity grows exponentially, turning simple AI problems into distributed systems challenges. It introduces key patterns for coordination, state management, and failure recovery, arguing that robust system design, not just AI capabilities, is crucial for production-grade multi-agent applications. The core thesis is that adopting established distributed systems principles is essential for building reliable and valuable multi-agent systems.