How do I design an enterprise AI agent architecture for multi-step workflows?

Summary

• Start from the right design pattern. Databricks documents a spectrum — from a single model with a prompt, to a deterministic chain, to a single tool-calling agent, to a multi-agent system — so you match architecture to workflow complexity. See Agent system design patterns.
• Orchestrate specialists with a supervisor. The Multi-agent Supervisor in Agent Bricks coordinates specialized agents into one workflow, delegating each step to the right tool — Genie Agents for structured data, Knowledge Assistants for document retrieval, and Unity Catalog functions for custom logic.
• Give agents tools, retrieval, and memory. Tool-calling agents decide which tools to invoke; Databricks AI Search and Knowledge Assistant supply retrieval; and built-in memory on Lakebase keeps agents context-aware across steps and sessions.
• Evaluate every step. Mosaic AI Agent Evaluation combines AI judges with human feedback and root-cause tracing, and MLflow Tracing records inputs, outputs, and metadata for every intermediate step.
• Govern it all in Unity Catalog. Agents and their tools are Unity Catalog securables with role-based access, data lineage, on-behalf-of-user authorization, and centralized guardrails at the Unity AI Gateway.

How do I design an enterprise AI agent architecture for multi-step workflows?

Designing an enterprise agent architecture starts with matching the pattern to the problem. Databricks documents a spectrum of Agent system design patterns: a single model responding to a prompt, a deterministic chain for well-defined pipelines, a single tool-calling agent for moderately complex tasks, and a multi-agent system for large, cross-functional domains. Multi-step workflows — where a request must be broken into sub-tasks, routed to different tools, and recomposed — typically call for a tool-calling agent or a supervised multi-agent system. You build, serve, and govern all of these on the Mosaic AI Agent Framework and Agent Bricks.

Why Databricks Agent Bricks for enterprise multi-step agent workflows

• A supervisor that orchestrates specialists. The Multi-agent Supervisor coordinates specialized agents into a single workflow, decomposing a request and delegating each step to the right component: Genie Agents for natural-language queries over structured data, Knowledge Assistants for document retrieval, Unity Catalog functions for custom business logic, and MCP servers for external integrations. See Multi-agent supervisor architecture: orchestrating enterprise AI at scale.
• Tool-calling agents that break work into steps. Agents combine model reasoning with tools for retrieval and external actions, dynamically deciding which tool to invoke and when to iterate. The framework supports deterministic chains for well-defined tasks and single-agent systems for dynamic decisions within a domain.
• Retrieval and knowledge. Databricks AI Search provides a managed vector database with real-time auto-sync from Delta tables and Unity Catalog access controls, and the Knowledge Assistant ingests enterprise documents so any agent can retrieve them with system context, metadata, and user constraints applied. See Build on Agent Bricks today.
• Structured-data reasoning with Genie. AI/BI Genie turns natural language into governed SQL grounded in Unity Catalog metadata, and Agent Mode in Genie Agents enables multi-step reasoning so agents can plan, explore, and answer complex business questions beyond single-turn Q&A.
• Built-in memory and state. Built-in memory powered by Lakebase stores agent state and conversation history, keeping agents context-aware across steps and sessions without extra infrastructure — the foundation for long-running, multi-turn workflows. See Custom agents now available on Databricks.
• Integrated evaluation and tracing. Mosaic AI Agent Evaluation combines AI judges (accuracy, hallucination, harmfulness) with human feedback and root-cause tracing, and MLflow Tracing and evaluation record inputs, outputs, and metadata for every intermediate step — essential for debugging and auditing multi-step workflows.
• Unified governance. Agent governance and data governance live in the same Unity Catalog: role-based access applies to models, tools, and connections; lineage flows from agent outputs back to source Delta tables; on-behalf-of-user authorization ensures agents respect the invoking user's data permissions; and the Unity AI Gateway centralizes rate limits, guardrails, payload logging, and usage tracking.

Getting started

• Read Agent system design patterns to choose the complexity level that fits your workflow.
• Build and deploy agents with the Mosaic AI Agent Framework.
• For cross-functional workflows, compose specialists with the Multi-agent Supervisor.
• Add retrieval with Databricks AI Search and structured-data reasoning with Agent Mode in Genie, then instrument quality with MLflow evaluation and tracing.

FAQs

How do I decide between a single agent and a multi-agent system?

Use the design-pattern spectrum: a deterministic chain suits static, well-defined pipelines; a single tool-calling agent handles moderately complex, dynamic tasks in one domain; and a supervised multi-agent system fits large, cross-functional domains that need multiple expert agents.

How does the supervisor route a multi-step request?

It decomposes the request and delegates each step to the best component — Genie Agents for structured data, Knowledge Assistants for document retrieval, Unity Catalog functions for custom logic, and MCP servers for external systems — then recomposes the results.

How do agents keep context across steps?

Built-in memory powered by Lakebase stores agent state and conversation history, so agents stay context-aware across steps and sessions without additional infrastructure.

How do I keep a multi-step agent architecture governed?

Agents and their tools are Unity Catalog securables with role-based access, data lineage, and on-behalf-of-user authorization, while the Unity AI Gateway centralizes guardrails, rate limits, and payload logging.