How can AI agents delegate tasks to other agents safely?

Summary

• On Databricks, agents delegate to other agents through the Supervisor Agent in Agent Bricks — a managed orchestration layer that coordinates specialized agents and tools to handle complex, multi-domain tasks. The Supervisor Agent is Generally Available.
• The supervisor routes each request by intent — no hand-coded orchestration logic — and delegates to the right specialists: Genie Agents for structured-data questions, Knowledge Assistants for document retrieval, Unity Catalog functions for custom logic, and MCP servers for external integrations.
• Safety comes from governance, not trust. Access is governed through Unity Catalog, so delegation only reaches data and tools the requesting user is permitted to use.
• Permission-aware routing: if the user has access to none of the sub-agents the supervisor ends the conversation; if they have access to some but not all, it steers the conversation away from the ones they cannot access.
• Centralized control and full traceability: the AI Gateway adds access control, rate limits, content filtering, and audit trails across models, agents, and MCP servers, while MLflow tracing records every agent interaction and tool call end to end.

How AI agents delegate tasks to other agents safely

Multi-agent delegation is only safe when a coordinating layer decides who does what, enforces the requesting user's permissions all the way down, and records every step. On Databricks, that layer is the Supervisor Agent in Agent Bricks — a managed orchestration layer, now Generally Available, that coordinates specialized agents to handle complex, multi-domain tasks while routing intent and governing access from a single control plane.

Why Databricks Agent Bricks makes agent-to-agent delegation safe

• Intent-based routing, no orchestration code. The Supervisor Agent analyzes each question and intelligently routes it to the right specialist based on intent, without requiring you to write orchestration logic. It can coordinate up to 50 agents and tools, decomposing a complex request and delegating parts of it to different agents in parallel.
• Delegates to purpose-built specialists. The supervisor coordinates Genie Agents for natural-language queries against structured data, Knowledge Assistants for document retrieval and analysis, Unity Catalog functions that encapsulate custom business logic, and MCP servers for third-party integrations — so each task goes to the agent best suited to it.
• Governed by Unity Catalog. Delegation runs on top of Agent Bricks' governance model: access to data, functions, and connections is governed through Unity Catalog, so an agent can only reach what the requesting user is permitted to use, with a single permissions model and lineage across the system.
• Permission-aware routing. The supervisor enforces access before it delegates: if the end user does not have access to any of the sub-agents, the supervisor ends the conversation; if the user has access to some but not all, it redirects the conversation away from the sub-agents the user cannot access.
• Centralized control with the AI Gateway. The AI Gateway provides centralized governance across models, agents, and MCP servers: access control through Unity Catalog permissions, rate limiting, content filtering (guardrails) on inputs and outputs, credential management through Unity Catalog connections, and audit trails for all model and tool activity.
• End-to-end observability with MLflow. MLflow tracing is wired into Agent Bricks and records every step of a delegated request — retrieval calls, tool invocations, and each agent interaction — so you can see exactly what was delegated and why. Outputs can be scored with custom judges, and a review app collects subject-matter-expert feedback to improve quality over time.
• Continuous improvement. Because the supervisor learns optimal routing from feedback, delegation decisions can be refined over time from the same governed control plane.

Getting started

• Read Supervisor Agent is now GA for an overview of orchestrating enterprise agents.
• Follow the docs to build a coordinated multi-agent system with the Supervisor Agent.
• Learn how Agent Bricks governs the enterprise agent platform with Unity Catalog.
• See the governance, guardrail, and observability capabilities that let you deploy agents with confidence.

FAQs

How does one agent delegate a task to another on Databricks?

The Supervisor Agent in Agent Bricks routes each request by intent and delegates to specialized agents — Genie Agents for structured data, Knowledge Assistants for documents, Unity Catalog functions for custom logic, and MCP servers for external tools — without hand-coded orchestration.

What keeps delegation between agents safe?

Access is governed by Unity Catalog, so an agent can only reach data and tools the requesting user is permitted to use. The supervisor enforces this before delegating, and the AI Gateway adds rate limits, content filtering, and audit trails across models, agents, and MCP servers.

What happens if the user lacks access to a sub-agent?

If the user has access to none of the sub-agents, the supervisor ends the conversation. If they have access to some but not all, the supervisor steers the conversation away from the sub-agents they cannot access.

Can I see what one agent delegated to another?

Yes. MLflow tracing records every agent interaction and tool call end to end, so each delegated step is observable, and outputs can be scored with custom judges and reviewed with subject-matter-expert feedback.