What Is an Agent Gateway?
Definition: Agent Gateway An agent gateway is a centralized control plane that manages routing, authentication, access control, and observability for autonomous AI agent traffic — including agent-to-agent (A2A) calls and agent-to-tool interactions. It sits between AI agents and the systems they access, enforcing consistent governance across every invocation. |
AI agents are no longer isolated: they call other agents, invoke tools, query APIs, and consume context from data sources — often across teams and infrastructure boundaries. As these agentic workloads scale, organizations need a dedicated layer that controls how agent traffic flows, who can invoke what, and how every action is logged.
An agent gateway provides that layer. It acts as the single enforcement point through which all agent-to-agent and agent-to-tool traffic passes — giving platform teams visibility and control without requiring each agent to implement its own routing logic, auth, or logging.
What Is an Agent Gateway Used For?
The agent gateway handles three core responsibilities that every enterprise agentic deployment eventually needs to manage centrally:
1. Routing Agent Traffic
Agents invoke other agents or tools at runtime. Without a gateway, each agent must know where every downstream agent lives, manage its own connection logic, and handle load distribution independently. An agent gateway centralizes this: incoming agent invocations are routed to the correct registered agent based on name, with built-in load balancing and failover across multiple instances.
2. Enforcing Access Control
Not every agent should be able to call every other agent. The gateway enforces who can invoke what — applying team-level isolation, API key authentication, and permission policies at a single point rather than scattering access logic across individual agents.
3. Capturing Observability
Every agent call is logged with full trace context: which agent was invoked, by whom, with what inputs, and at what cost. Spend attribution per agent, iteration budgets, and audit trails are all maintained centrally — a requirement for compliance in regulated industries.
The agent gateway is distinct from both the AI gateway and the MCP gateway:
An AI gateway controls LLM model calls (which model is called, with what config, at what spend limit).
An MCP gateway routes tool calls and context using the Model Context Protocol.
An agent gateway routes full agent-to-agent workflows using the A2A Protocol — a higher-level coordination layer above both.
Agent Gateway vs. AI Gateway — How They Relate
An AI gateway is the control plane for model calls. It sits between your application and LLM endpoints — routing requests to GPT-4o, Claude, Gemini, or any other model — while enforcing spend limits, rate limits, and fallback logic at the inference layer.
An agent gateway operates one level up the stack. Where the AI gateway manages the model call itself, the agent gateway manages agent-to-agent coordination: which agent gets invoked, how the request is routed across agent instances, and how the entire multi-step workflow is traced.
They are not competing products — they are complementary layers. In LiteLLM's architecture, both exist within a unified AI gateway platform:
AI Gateway | Agent Gateway |
|---|---|
Controls LLM model calls and endpoints | Controls agent-to-agent and agent-to-tool traffic |
Routes to GPT-4o, Claude, Gemini, etc. | Routes to registered agent instances by name |
Enforces inference spend limits and rate limits | Enforces agent invocation budgets and access control |
Logs prompt/completion pairs with token costs | Logs full agent traces with spend attribution per agent |
Operates at the model layer | Operates at the orchestration/workflow layer |
For a full breakdown of what an AI gateway is and how it differs from other gateway types, see What Is an AI Gateway? — the pillar article in this series.
Agent Gateway vs. MCP Gateway — What's the Difference?
The Model Context Protocol (MCP) is an open standard for how AI agents call tools and retrieve context — think function calling with a standardized wire format. An MCP gateway routes these tool calls, managing which tools are available, how they're invoked, and how their outputs are returned.
An agent gateway is concerned with a different layer: not individual tool calls, but full agent-to-agent workflows. Where an MCP gateway handles a single tool invocation within an agent's reasoning loop, an agent gateway handles the coordination between multiple agents — including passing tasks, managing multi-step flows, and enforcing governance across the entire orchestration.
In practice, both are needed for sophisticated agentic deployments:
An agent calls an MCP gateway to use a tool (web search, code execution, database lookup).
An agent gateway coordinates how agents delegate tasks to other agents across the organization.
In LiteLLM, both are built into the same platform — not separate products. The MCP gateway is available at /mcp, while the agent gateway operates via the A2A endpoint.
Why Enterprises Need an Agent Gateway
As organizations move from single-agent prototypes to multi-agent production systems, the governance problems scale fast.
The M×N Problem
In a multi-agent deployment, you might have M different agents (a research agent, a coding agent, a planning agent, a compliance agent) that can each invoke N different tools and downstream agents. Without a centralized gateway, each agent team builds its own routing logic, manages its own auth credentials, and implements its own logging — creating a fragmented, ungovernable mesh.
An agent gateway collapses this complexity to a single enforcement point. Every agent call — regardless of which team owns the calling agent or the receiving agent — passes through the gateway. Auth is centralized. Routing is standardized. Logging is automatic.
Spend Attribution Per Agent
In enterprise environments, different teams own different agents, and those agents consume compute resources differently. The gateway attributes costs to individual agents, enabling chargeback reporting, budget alerts, and spend optimization at the team level — not just at the org level.
Iteration Budgets
Autonomous agents can spiral into runaway loops if left unconstrained. An agent gateway enforces iteration budgets — limits on how many steps an agent can take within a single workflow. This is a critical safety control for production agentic systems, preventing runaway agent behavior that could generate unexpected costs or unintended side effects.
Audit Trails and Compliance
Every agent invocation is logged, timestamped, and attributed to a specific agent identity. For organizations in regulated industries — finance, healthcare, government — this complete audit trail is not optional. The agent gateway makes compliance tractable by ensuring every agent action is captured at the infrastructure layer, not left to individual agent implementations.
Agent Discovery
In large organizations, knowing what agents exist and what they can do is itself a governance problem. An agent gateway includes a registry where teams can publish their agents with names, descriptions, and invocation URLs. Other teams can browse the registry to discover and invoke approved agents — enabling reuse without ad hoc coordination.
How LiteLLM Agent Gateway Works
LiteLLM built its agent gateway on Google's open A2A (Agent-to-Agent) Protocol standard — the emerging inter-agent communication protocol that enables agents built on different frameworks to communicate with each other through a standardized interface.
Architecture
Agents are registered in the LiteLLM Admin UI with a name and an invocation URL. Once registered, any agent or application can invoke them through a standardized endpoint:
Feature | Detail |
Protocol | Google A2A (Agent-to-Agent) — open standard |
Endpoint format | POST /a2a/{agent_name}/message/send via JSON-RPC 2.0 |
SDK compatibility | OpenAI SDK with a2a/ model prefix — zero migration friction |
MCP support | Dedicated /mcp endpoint for tool calls |
Supported frameworks | Vertex AI Agent Engine, LangGraph, Azure AI Foundry, Bedrock AgentCore, Pydantic AI |
Load balancing | Across multiple instances of the same agent |
Streaming | Supported for long-running agent workflows |
Iteration budgets | Configurable per agent to prevent runaway loops |
Observability | Full trace grouping and spend tracking per agent |
Agent registry | Browse and discover agents across the org in Admin UI |
Full documentation is available at docs.litellm.ai/docs/a2a. For enterprise deployment options including SSO, RBAC, and audit logging, see LiteLLM Enterprise.
LiteLLM is trusted at Netflix and federal agencies, with over 1 billion requests served. It was the first enterprise AI gateway with native A2A agent gateway support.
Get Started with LiteLLM Agent Gateway
LiteLLM was the first enterprise AI gateway to ship native support for the A2A Protocol, giving platform and ML engineering teams a production-ready agent gateway without building routing, auth, and observability from scratch.
Whether you're routing between LangGraph agents, Vertex AI Agent Engine, Azure AI Foundry, or Pydantic AI, the LiteLLM agent gateway provides the unified control plane your multi-agent architecture needs.
Start for free → litellm.ai | Book a demo → litellm.ai/sales
Frequently Asked Questions
What is the difference between an agent gateway and an AI gateway?
An AI gateway controls LLM model calls — routing requests to different models, enforcing rate limits, and logging prompt/completion pairs. An agent gateway controls agent-to-agent and agent-to-tool traffic — routing agent invocations, enforcing access policies, and logging full agent workflow traces. They operate at different layers and are complementary.
What is the difference between an agent gateway and an MCP gateway?
An MCP gateway handles individual tool calls within an agent's reasoning loop using the Model Context Protocol. An agent gateway handles coordination between multiple agents using the A2A Protocol — a higher-level layer that encompasses full multi-step agent workflows, not just single tool invocations.
What is the A2A Protocol?
A2A (Agent-to-Agent) is an open protocol created by Google that standardizes how AI agents communicate with each other across different frameworks and infrastructure. It defines a JSON-RPC 2.0 based interface for agent invocation, streaming, and task delegation — enabling interoperability between agents built on different platforms.
Which agent frameworks does LiteLLM Agent Gateway support?
LiteLLM Agent Gateway supports Vertex AI Agent Engine, LangGraph, Azure AI Foundry, AWS Bedrock AgentCore, and Pydantic AI. Any agent that can communicate via the A2A Protocol or the OpenAI SDK (using the a2a/ model prefix) can be registered and routed through the gateway.
How do I set up an agent gateway with LiteLLM?
You register your agents in the LiteLLM Admin UI with a name and invocation URL. Agents are then callable via POST /a2a/{agent_name}/message/send using JSON-RPC 2.0. Full setup documentation is at docs.litellm.ai/docs/a2a.
Does an agent gateway add latency to agent calls?
The overhead of routing through the gateway is minimal — typically sub-millisecond for the gateway layer itself. The latency of an agent invocation is dominated by the agent's own processing time, not the routing layer. For long-running agentic workflows, the governance and observability benefits significantly outweigh the negligible routing overhead.
Related Resources
A2A Protocol Documentation — docs.litellm.ai/docs/a2a
LiteLLM Enterprise (SSO, RBAC, Audit Logging) — docs.litellm.ai/docs/proxy/enterprise
LiteLLM AI Gateway Overview — litellm.ai/ai-gateway
What Is an MCP Gateway? — litellm.ai/blog/what-is-an-mcp-gateway (Article 2 in this series)