What are the best agent management platforms?

Summary

• An agent management platform provides the infrastructure to build, deploy, govern, and monitor AI agents in production, including reliability, observability, secure tool access, and access control.
• Agent Bricks is the Databricks agent platform for taking agents from pilot to production, supporting knowledge assistants, document intelligence, process and workflow agents, and a Supervisor Agent for multi-agent orchestration.
• Governance is built in through Unity Catalog: models, agents, MCP servers, and tools are registered as governed assets with a unified permission model, lineage, and audit trails.
• Quality and observability run on MLflow: every agent interaction is traced, scored with LLM judges, and monitored with the same evaluation configuration from development through production.
• The Unity AI Gateway centralizes access control, cost controls with spend caps, model routing, and policy enforcement across the entire agent estate.

What are the best agent management platforms?

An agent management platform gives teams the infrastructure to build, deploy, govern, and monitor AI agents once they move beyond a single model call into orchestrating a full system. The hard work at that point is platform work: reliability, observability, secure connections to tools and APIs, preventing data leakage, governance, and runtime management. On Databricks, this is delivered through Agent Bricks together with Unity Catalog governance, MLflow evaluation and monitoring, and the Unity AI Gateway.

Why Databricks Agent Bricks for managing AI agents

• A full agent lifecycle across patterns. Agent Bricks supports knowledge assistants that reason over structured and unstructured data, document intelligence that parses PDFs and reports into structured data, process and workflow agents that take governed actions such as updating records or filing tickets, and product intelligence that embeds reasoning into applications. Agents can be built with custom code, declarative workflows, or agentic AI functions.
• Multi-agent orchestration. The Supervisor Agent coordinates complex workflows across multiple agents and tools so teams can start simple and scale as needs grow.
• Governance registered in Unity Catalog. Models, agents, MCP servers, and skills are registered as Unity Catalog securables and governed through the same permissions model, lineage tracking, and audit trails used for enterprise data. On-behalf-of permissions let an agent act with the requesting user's access scope, so it respects the underlying data permissions, and every model call and tool invocation is logged for compliance.
• Quality and observability with MLflow. MLflow traces every agent interaction, capturing inputs, outputs, and metadata for each intermediate step, scores outputs with custom LLM judges, and collects subject-matter-expert feedback. The same evaluation configuration is reused from offline evaluation through online production monitoring.
• Centralized control with the Unity AI Gateway. The gateway governs which agents can access which models, MCP servers, and tools; provides unified spend visibility with granular cost attribution and hard spend caps that halt requests when budgets are exceeded; supports contextual policies that allow, deny, or require approval based on user, agent, model, or request contents; and offers end-to-end tracing of model and tool activity.
• Governed tool access through MCP. Agent Bricks connects agents to external tools through Model Context Protocol (MCP) servers, including managed MCP integrations for common enterprise systems, with all tool activity logged, rate-limited, and auditable.

Getting started

• Explore the Agent Bricks product page for an overview of the platform.
• Follow the build generative AI apps and agents documentation to author your first agent.
• Read Building trusted AI agents for how governance, evaluation, and monitoring fit together.
• Add multi-agent orchestration with the Supervisor Agent.

FAQs

What is an agent management platform?

It is a platform that provides the infrastructure to build, deploy, govern, and monitor AI agents in production, handling reliability, observability, secure tool and API access, access control, and runtime management.

How does Databricks govern AI agents?

Agents, models, MCP servers, and tools are registered as Unity Catalog securables and governed with a unified permissions model, lineage, and audit trails, with on-behalf-of permissions so agents respect the requesting user's data access.

How do you monitor AI agents in production?

MLflow traces every agent interaction and scores outputs with LLM judges, using the same evaluation configuration from offline testing through production monitoring, while the Unity AI Gateway adds end-to-end tracing and cost controls.