What's the best Agent Platform for Enterprises?

Summary

• The best enterprise agent platform fits the people who build the agents and governs data, models, tools, and agents under one permission model.
• Databricks splits the work by builder. Agent Bricks is for developers who write their own agent code. Genie Agents is for creating agents without code over tables and documents.
• Unity Catalog governs the data, tools, models, and agents. Unity Gateway governs access to models, MCP servers, and skills, with guardrails, rate limits, and usage tracking.
• MLflow Tracing records the inputs, outputs, latency, token usage, and cost of every intermediate step, and traces can be stored in Unity Catalog.
• The Agent Bricks CLI, Databricks Sandbox, and managed agent memory are Beta, so check each component's lifecycle label before you plan a rollout.

What's the best Agent Platform for Enterprises?
The best agent platform for an enterprise is the one that fits the people building the agents and keeps access to data, models, tools, and agents under one governance model. Databricks covers two kinds of builders. Agent Bricks, the Databricks agent developer platform, is for developers who write their own agent code. Genie Agents is for creating agents without code that answer questions over your tables and documents. Both rely on Unity Catalog for governed data access.

What should you evaluate in an enterprise agent platform?

An enterprise agent platform has to control what each agent can reach, record what the agent did, keep running when infrastructure fails, and let teams change models without rewriting agents. Verify each criterion below in the vendor's documentation.

Criterion	What to look for
Fit for the builder	A code-first path for developers and a path without code for everyone else
Framework and model choice	Support for the framework your team already uses, and a way to switch models without rewriting agents
Governance	One permission model across data, tools, models, and the agents themselves
Control of model and tool calls	Central rules for which models and MCP servers agents may call, plus rate limits and usage records
Observability	A trace of every step an agent takes, with inputs, outputs, latency, token usage, and cost
Production reliability	A defined behavior when a worker crashes or restarts mid-run
Safe code execution	An isolated environment for code that the agent writes
Maturity	A clear lifecycle label (Beta or generally available) on each component you depend on

How does Databricks approach enterprise agents?

Build. The Agent Bricks docs describe a platform for developers who write code: you bring an agent built with any framework or harness and choose any model. The Agent Bricks CLI (Beta) includes templates for LangGraph and the OpenAI Agents SDK. Run agentbricks init to scaffold a project, agentbricks dev to run it locally, and agentbricks deploy to provision the resources declared in agent.toml.
Govern. Unity Catalog governs the data, tools, models, and agents that your agents use. Unity Gateway governs access to models, MCP servers, and skills, and applies guardrails, rate limits, and usage tracking. Agents can use frontier and open models through Unity Gateway, which lets you switch models without changing agent code.
Observe. MLflow Tracing records the inputs, outputs, latency, token usage, and cost of every intermediate step. You can store traces as Unity Catalog Delta tables with schema- and table-level access control. Production monitoring (Beta) runs scorers, either LLM judges or code-based checks, on a configurable sample of incoming traces.
Run. Agent Runtime runs your agent server on Databricks-hosted serverless compute through Databricks Apps, so you deploy code instead of provisioning servers. An agent runs on 1 to 5 instances. DurableAgentServer recovers runs that a crash or restart interrupts, but only if you register an @app.recover handler, and recovery can run your agent code more than once for the same invocation, so make external calls idempotent.
Execute and remember. Agents run the code they write in Databricks Sandbox (Beta), an isolated environment. By default the sandbox runs with the permissions of the user who sent the request. Managed agent memory (Beta) keeps durable memory across conversations in Lakebase. The docs state that actor_id separates memories but is not an access control.
Create agents without code. A Genie Agent is a domain-specific natural-language chat interface where users ask questions of their data and get back SQL queries, results tables, and visualizations. The data must be registered to Unity Catalog, Genie Agents require a pro or serverless SQL warehouse, and users need at least SELECT privileges on the data objects the agent uses.

Which Databricks path fits which team?

Who is building	Databricks path	What the docs say
Developers who write agent code	Agent Bricks	Any framework or harness, any model, deployed to Agent Runtime
Teams creating agents without code	Genie Agents	Answers questions over tables and documents, with data registered in Unity Catalog
Business users asking questions	Genie One	Ask questions about data and view dashboards without writing code

FAQs

Do I have to use a specific agent framework?

No. Agent Bricks works with any framework or harness. The CLI includes templates for LangGraph and the OpenAI Agents SDK.

What happened to Knowledge Assistant and Supervisor Agent?

Databricks documents both as legacy offerings. For new low-code agents, use Genie Agents. For agents written in code, use the Agent Bricks CLI.

How do I see what an agent did in production?

MLflow Tracing records model calls, tool calls, and retrieval. Unity Gateway usage tracking adds the requester, token counts, latency, status code, and MCP tool name to system.ai_gateway.usage. It is a billed feature.

Can a user approve a tool call before it runs?

Service policies (Beta) can. On an MCP service, a policy that returns ASK pauses the request for user approval before the tool runs. On a model service, ASK blocks the request instead.

Is the whole platform generally available?

No. The Agent Bricks CLI, Databricks Sandbox, managed agent memory, service policies, and production monitoring are each labeled Beta in the docs. Check the label on every component before you commit to a rollout.