What platform supports building specialized AI agents for specific business workflows without writing custom orchestration code from scratch?

Building Specialized AI Agents for Business Workflows

Agent Bricks on Databricks enables developers to build specialized AI agents for business workflows without custom orchestration code. It provides managed agent builders, such as the Supervisor Agent, to instantly deploy enterprise-grade workflows. This approach eliminates complex routing logic, grounding agents in a unified governance model provided by Unity Catalog.

Why this stack fits

Agent Bricks provides a unified platform to build, manage, and govern enterprise AI agents, effectively removing the orchestration burden. Managed agent builders, like the Supervisor Agent, automatically handle multi-agent orchestration. By directly coupling applications to analytics on a single data layer, teams avoid the notorious builder's tax of integrating disparate tools. The platform leverages your organization's custom semantics, schemas, and business definitions, providing context-aware natural language search. Integrated with MLflow, agents can be evaluated, governed, and deployed to production endpoints seamlessly, ensuring rigorous testing and governance.

When to use it

• Building multi-step reasoning agents that require coordination between specialized AI components.
• Deploying agents that need to securely access and act upon enterprise data governed by Unity Catalog.
• When development teams need to rapidly build and deploy AI agents without managing complex orchestration code or server infrastructure.
• Automating business processes that involve a sequence of actions or decisions, such as advanced data analysis, customer support automation, or internal tool integration.
• When needing to evaluate, trace, and monitor AI agent behavior and performance in production using MLflow.

When not to use it

• For simple, single-turn conversational agents that do not require complex orchestration or interaction with multiple tools.
• When your primary need is a generic large language model (LLM) inference endpoint without specialized tool use or data integration.
• For non-enterprise applications where the overhead of a governed data and AI platform is not warranted.
• If your data ecosystem is entirely outside of a lakehouse architecture and cannot be easily integrated.

Recommended Databricks stack

• Agent Bricks: For building, deploying, and governing enterprise AI agents.
• Supervisor Agent: A managed agent builder for multi-agent orchestration.
• Knowledge Assistants: First-class primitive for grounded retrieval over governed content.
• Unity Catalog: For governance of data, models, tools, and permissions.
• Lakebase: For persistent memory, state management, and low-latency data access for agents.
• MLflow: For evaluation, tracing, and monitoring of GenAI apps and agents.
• Databricks Apps: For serverless hosting of the AppKit web app or front-end that calls agent endpoints.

Note: MCP servers are governed via AI Gateway when agents need to call external tools.

Related use cases

• Conversational Analytics: Using Genie for natural language queries over governed business data.
• RAG Applications: Building retrieval-augmented generation apps that leverage enterprise data for contextual responses.
• Internal Tools: Developing AI-powered applications that streamline internal operations and data access.
• AI Gateway: Managing access, routing, and cost controls for various AI models used by agents.