From Chaos to Choreography: Multi-Agent Orchestration Patterns That Actually Work — Sandipan Bhaumik
Apr 8, 2026 · 26:29
Sandipan Bhaumik, Data & AI Tech Lead at Databricks, argues that scaling AI agents from one to five transforms an AI problem into a distributed systems problem, exposing teams to race conditions, stale reads, and cascading failures. He recounts a production deployment where a credit decisioning system incorrectly approved 20% of applicants because a caching layer wasn't invalidated between agents. Bhaumik presents two coordination patterns: choreography (event-driven, decentralized) for simple workflows with high autonomy, and orchestration (central coordinator) for complex workflows needing rollback and observability. He advocates for immutable state snapshots (append-only versions) and data contracts (schema validation at handoffs) to eliminate race conditions, and demonstrates failure recovery using circuit breakers and compensation (saga) patterns. The episode delivers a production-grade architecture on Databricks using LangGraph, Unity Catalog, Delta Lake, and MLflow for tracing, with code examples for state handoff and circuit breaker logic.
