Best AI gateways for agentic AI

Summary

• Agentic AI raises the stakes: agents call models, tools, and MCP servers on their own and act on a user's behalf, so the gateway must govern access, enforce policy while the agent runs, and record every action, not just proxy model calls.
• The best AI gateways for agents give you one governed control plane in front of every model, MCP server, and agent: unified multi-provider access, identity-based access control, runtime guardrails, rate limits, spend caps, fallbacks, and full request and response logging.
• On Databricks this control plane is Unity Gateway, the enterprise AI governance layer built on Unity Catalog, and it is generally available.
• Because it is part of Unity Catalog, it governs models, MCP servers, functions, and agents as securable objects with the same permissions you use for data, and on-behalf-of execution keeps an agent inside the invoking user's permissions.
• Guardrails, rate limits, budgets with hard caps, traffic splitting with fallbacks, and full usage and payload logging give teams safety, reliability, cost control, and an audit trail across any model provider.

Best AI gateways for agentic AI

An AI gateway for agentic AI is the control plane between your agents and the models, tools, and MCP servers they call. Agents aren't single chatbot requests: they plan across steps, choose their own tools, and act for a user, so the gateway has to decide what each agent may call, enforce policy while it runs, and log what it did. The best choice governs models, MCP servers, and agents with the same identity and permissions you already apply to data, rather than a proxy bolted on beside your governance. On Databricks that control plane is Unity Gateway, built on Unity Catalog.

What is an AI gateway for agentic AI?

An AI gateway is a central entry point between applications or agents and the models they call. For agents it does more than route model traffic: it governs the tools and MCP servers an agent can invoke, applies guardrails to inputs and outputs, enforces spend and rate limits, and captures a record of every action. Because agents act autonomously and can trigger real operations, runtime policy enforcement and observability matter more than for a single request-and-response app.

Key capabilities to evaluate

Capability	Why it matters for agents
Provider-agnostic access	One entry point to hosted and external models, so agents aren't tied to one provider
Identity-based access control	Decide which agents, users, and teams may call each model, tool, or MCP server
On-behalf-of execution	An agent runs with the invoking user's permissions and can't reach data the user can't
Runtime guardrails	Filter unsafe content, detect PII on requests and responses, and mitigate prompt injection
Policies for tools and actions	Allow, deny, or require approval for specific tool calls and sensitive operations
Rate limits and cost controls	Cap tokens and queries and set budgets with hard caps to stop runaway agents
Usage and payload observability	Log full requests, responses, and tool activity for debugging and audit

How Databricks approaches AI gateways for agents

Unity Gateway is the enterprise AI governance control plane on Databricks, and it is generally available. Because it is part of Unity Catalog, one system governs your data and your AI.

• Governance built on Unity Catalog. Models, MCP servers, functions, and agents are governed as securable objects with the same privileges as tables and volumes, and the same controls extend to external models and agents routed through a Databricks model service. On-behalf-of-user execution keeps an agent inside the permissions of the person who invoked it. See how AI governance works.
• Unified access to any model. One control plane reaches models hosted on Databricks and external models from any provider, so you can build agents on many models without a separate access model per provider.
• Guardrails for safety and PII. Safety filtering blocks harmful content such as violent crime, self-harm, and hate speech, and PII detection identifies sensitive data such as credit card and bank account numbers, emails, and phone numbers on inputs and outputs. Newer LLM-based guardrails (in Beta) let teams define custom safety, PII, and business rules.
• Service policies for tools and actions. Service policies (in Beta), written in SQL as Unity Catalog functions, allow, deny, or require approval for a request based on who is calling and what it contains, so you can restrict which tools an agent invokes and gate sensitive operations.
• Rate limits and cost controls. Apply rate limits to model and MCP services, and set budgets with per-user thresholds and hard caps that alert or block when spend crosses a limit.
• Fallbacks and traffic splitting. Configure traffic splitting and fallbacks to spread requests across model destinations and fail over automatically, so agents keep running when a model degrades.
• Usage tracking and payload logging. Usage tracking records requests, token usage, and latency in system tables, and payload logging captures full requests and responses in Unity Catalog inference tables, for a queryable audit trail of model and agent activity.

Use cases

• Govern coding agents and MCP. Route external coding agents and MCP servers through the gateway so access control, approvals, and logging apply to every tool call.
• Multi-provider agent apps. Put many providers behind one endpoint, with fallbacks, to add or swap models without rewiring the app.
• Regulated workloads. Enforce PII guardrails and approval policies, and keep a payload-level audit trail.

FAQs

What makes an AI gateway "for agentic AI" different?

Agents call tools and MCP servers, take multiple steps, and act on a user's behalf, so the gateway must govern tool access, enforce policy at runtime, and log each action, not just forward a single model request.

How does Unity Gateway keep an agent within a user's permissions?

On-behalf-of-user execution runs the agent with the permissions of the person who invoked it, and Unity Catalog governs the models, tools, and data it can reach, so an agent can't access anything the user couldn't.

How do teams control agent cost and prevent runaway usage?

Rate limits cap tokens and queries per user, endpoint, or service, and budgets set per-user thresholds and hard caps that alert or block further usage when spend crosses a limit.

Is Unity Gateway generally available?

Yes. Unity Gateway is generally available as the AI governance control plane on Databricks. Some newer capabilities, such as service policies and LLM-based guardrails, are in Beta.