AI gateway vs agent gateway

Summary

• An AI gateway is a general-purpose proxy that routes requests from applications to any type of AI model (LLMs, embedding models, image generation, etc.) and enforces policies like access control, rate limiting, and cost management.
• An agent gateway is specifically designed to orchestrate agent requests, manage agent state, handle multi-turn conversations, and track agent execution across complex workflows.
• AI gateways focus on model access and governance across all AI use cases; agent gateways focus on agent-specific capabilities like workflow orchestration and agent lifecycle management.
• An organization may use an AI gateway for model routing and governance, with an agent gateway layered on top for agent-specific needs, or use a unified gateway that handles both.
• The distinction reflects specialization: general AI workloads vs. agentic workflows.

AI gateway vs agent gateway

AI gateways and agent gateways are related concepts that often blur together, but they serve distinct purposes. An AI gateway is a general-purpose proxy between applications and AI services. An agent gateway is a specialized proxy for agentic workflows: managing agent state, orchestrating reasoning, handling tool calls, and tracking execution. Some products combine both; others specialize.

What is an AI gateway?

An AI gateway sits between applications and AI models or services. It enforces policy for all AI requests. Responsibilities include: routing to available models (choosing between Anthropic, OpenAI, etc.), enforcing model allowlisting, implementing rate limiting, tracking token usage and costs, scanning for sensitive data leakage (prompt injection), ensuring compliance, and audit logging. AI gateways are model-agnostic and apply across LLM calls, embeddings, chat, and more.

What is an agent gateway?

An agent gateway orchestrates agentic workflows. Agent workflows involve multiple steps, state management, tool calls, and loops. An agent gateway handles: routing agent requests, managing agent state across turns, executing tool calls, tracking execution traces for debugging, managing retries and error recovery, and enforcing agent-specific policies (tool allowlisting, step limits, timeouts). Agent gateways may also manage agent instance lifecycle or coordinate with runtimes elsewhere.

Key capabilities to evaluate

Capability	General gateway	Agent gateway
Model routing	Core	Not typically
Rate limiting	Core	Optional
Cost tracking	Core	Core
Request validation	Core	Not typically
Multi-turn state	Not typically	Core
Tool orchestration	Not typically	Core
Execution tracing	Basic	Advanced
Error recovery	Basic	Advanced
Lifecycle management	Not typically	Core
Policy enforcement	Core	Core

How Databricks approaches this

Databricks Unity Gateway provides general LLM request routing and governance. It sits between applications and models from Databricks, Anthropic, OpenAI, and other providers. Unity Gateway enforces access control (integrated with Unity Catalog), cost tracking, model allowlisting, request validation, and audit logging.
For agentic workflows, Databricks Genie Agents orchestrate execution. Genie Agents manage state, handle tool calls through the Model Context Protocol (MCP), track execution, and manage lifecycle. When Genie Agents call external models, they route through Unity Gateway, which applies governance. This layered approach lets Genie Agents specialize in orchestration while Unity Gateway specializes in LLM governance.

Common deployment patterns

Specialized layers: Dedicated gateways for agent control and AI governance, allowing specialization but adding complexity.
Unified gateway: A single gateway (like Unity Gateway) combines both, simpler operationally.
Lightweight approach: Agent control plane with built-in basic governance (cost tracking, audit logs, model allowlisting) for teams without strict regulatory needs.

FAQs

Do I need both?

It depends. If you have both simple LLM requests and complex agent workflows, both allows specialization. If workloads are primarily agentic, a single agent gateway may suffice with AI governance features.

What is tool allowlisting?

Tool allowlisting restricts which tools (database access, APIs, file operations) an agent can call. It prevents unintended actions. For example, an agent might access a sales database but not a HR system.

Can an AI gateway handle agents?

Partially. It can route agent requests and enforce policies on model calls, but doesn't manage agent state, orchestrate tool calls, or handle multi-turn conversations. Add a dedicated agent framework for full support.

What is execution tracing?

Execution tracing logs all agent steps: what it thought, which tool it called, what it returned, and reasoning. Agent gateways and frameworks provide this for debugging complex workflows.

What is the Model Context Protocol (MCP)?

MCP is an open standard for agents to discover and call tools. Databricks Genie Agents use it to connect to external tools, enabling flexible integration without hard-coded logic.