NIHAR MALALI

While the Mosaic AI Agent Framework provides the foundation, building sophisticated multi-agent systems on Databricks requires orchestration patterns that coordinate multiple specialized agents. LangGraph, combined with Unity Catalog’s tool governance and Delta Lake’s transactional storage, creates a powerful stack for enterprise multi-agent pipelines.

LangGraph on Databricks — Stateful Agent Orchestration

LangGraph extends LangChain with graph-based workflows where nodes represent agent actions and edges encode transitions, conditions, and routing logic. On Databricks, this becomes particularly powerful.

• Graph-as-Agent — Each LangGraph graph is registered as an MLflow model, making it versionable, deployable, and monitorable through standard Databricks infrastructure.
• Conditional Routing — Nodes can route to different sub-agents based on classification results, confidence thresholds, or business rules — all defined in Python with full testability.
• Persistent State — Agent conversation state and intermediate results are checkpointed to Delta tables, enabling long-running workflows that survive failures and can be audited retroactively.
• Human-in-the-Loop Nodes — Graph nodes that pause execution and wait for human approval before proceeding with high-stakes actions like data modifications or external API calls.

Multi-Agent Patterns for Enterprise Data

• Analyst-Reviewer Pipeline — A data analyst agent generates SQL queries and insights from lakehouse data, while a reviewer agent validates the SQL, checks for data quality issues, and ensures compliance with business rules before returning results.
• ETL Supervisor Pattern — A supervisor agent coordinates extraction agents (API connectors, file parsers), transformation agents (schema mapping, data cleaning), and loading agents (Delta table writers) — each specialized and independently scalable.
• Research and Synthesis — Multiple retrieval agents search different data domains (customer data, product catalogs, support tickets) in parallel, and a synthesis agent merges their findings into a coherent response with source attribution.

Unity Catalog as the Agent Tool Registry

Unity Catalog functions serve as the tool registry for multi-agent systems. This design pattern has several advantages over ad-hoc tool definitions.

• Centralized Discovery — Agents discover available tools through Unity Catalog’s metadata layer, enabling dynamic tool selection based on the task at hand.
• Permission Inheritance — Tool access follows Unity Catalog’s permission model. A finance agent can access finance functions but not HR functions, enforced at the platform level.
• Versioning and Lineage — Every tool invocation is tracked with full lineage, making it possible to audit which agent called which tool with what parameters and what data was accessed.
• Reusability — Tools built for one agent are immediately available to other agents. A SQL execution tool, a Slack notification tool, or a Jira creation tool become shared infrastructure.

Production Deployment Considerations

• Model Serving Endpoints — Deploy multi-agent graphs behind Databricks Model Serving with auto-scaling, A/B testing, and traffic routing.
• Cost Control — Use pay-per-token external model routing to optimize cost across providers. Route simple queries to smaller models and complex reasoning to larger ones.
• Monitoring — MLflow and Lakehouse Monitoring provide dashboards for agent latency, token usage, error rates, and quality metrics — all stored in Delta tables for custom analytics.

Multi-agent systems on Databricks benefit from a unique advantage: the orchestration layer, the data layer, the governance layer, and the serving layer all live on the same platform. This eliminates the integration tax that plagues multi-agent deployments on general-purpose cloud infrastructure.