AI Agent Gateways: the control plane for agentic AI
As enterprises move from single LLM calls to fleets of autonomous agents talking to models, tools, and each other, a new infrastructure layer has emerged to sit in the middle of it all. AI agent gateways route, secure, govern, and observe traffic across LLM providers, MCP servers, and agent-to-agent (A2A) communication — the same job an API gateway does for microservices, adapted for a world where the "APIs" are AI agents.
What an AI agent gateway actually does
Five capabilities show up again and again across the category, regardless of vendor.
Routing & Load Balancing
Directs requests across LLM providers and models, with automatic failover, retries, and semantic caching to control latency and cost.
Governance & Policy
Enforces access policies, budgets, and compliance rules across every agent, model, and tool call in the organization.
Security & Guardrails
Blocks prompt injection, PII leakage, and toxic content, and verifies agent identity before actions are executed.
Observability & Cost Control
Traces requests end to end, tracks spend per model or team, and surfaces usage analytics across the AI estate.
MCP & A2A Protocol Support
Applies the same control plane to Model Context Protocol tool calls and agent-to-agent traffic, not just LLM API calls.
The AI agent gateway directory
Open-source, cloud-provider, and enterprise-security offerings — search or browse below.
A routing-and-safety proxy layer offering sub-100ms PII detection, toxicity blocking, and prompt-injection defense alongside multi-provider routing, failover, semantic caching, and cost tracking.
Visit site →A control plane built into Kong AI Gateway for standardized security, governance, and observability across agent-to-agent (A2A) communication, with real-time RPC tracing and policy enforcement.
Visit site →An open-source gateway that unifies access to 140+ LLM providers and nearly 1,900 models behind a single OpenAI-compatible API, with spend tracking, budgets, and self-hosting support.
Visit site →Centralized governance, observability, and cost controls spanning agents, models, MCP servers, and tools within the Databricks Unity Catalog ecosystem.
Visit site →An open-source HTTP/gRPC gateway unifying traditional application traffic with AI-native protocols: LLM provider calls, MCP servers, and agent-to-agent traffic in a single data plane.
Visit site →An enterprise-grade, open-source AI gateway providing routing, governance, guardrails, and observability across multiple LLM and agent providers through one control plane.
Visit site →A Kubernetes-native API gateway built on Envoy Proxy and the Gateway API, paired with the agentgateway project for securing and governing inference, agentic, and MCP traffic.
Visit site →An open-source proxy between applications and LLMs offering model routing, load balancing, retries and fallback, token rate limiting, security, and observability.
Visit site →An open-source project built on Envoy Gateway for routing GenAI traffic to multiple providers, with multimodal support, MCP routing, and enterprise observability.
Visit site →An enterprise AI gateway for deploying, governing, and scaling LLMs and agents across on-prem, cloud, or hybrid environments, with unified routing, access control, and audit logging.
Visit site →An AI Agent Management platform unifying API management, event streaming, and agent governance, including identity management, access policies, and MCP tool authorization.
Visit site →Brings tool-level authentication, access control, per-tool rate limiting, and observability to remote MCP servers, alongside existing REST and GraphQL API management.
Visit site →A single control plane for both outbound LLM consumption and inbound MCP tool exposure, applying guardrails, usage monitoring, and cost optimization across AI traffic.
Visit site →A unified endpoint for accessing hundreds of AI models across providers with transparent, no-markup pricing, intelligent routing, automatic failover, and unified billing.
Visit site →Observability and control for AI applications at the edge, including analytics, logging, caching, rate limiting, and automatic model fallback.
Visit site →Choosing a gateway
A few questions narrow the field quickly.
Open-source or managed?
LiteLLM, agentgateway, kgateway, Apache APISIX, and Agent Router (formerly Envoy AI Gateway) are open-source and self-hostable. Most cloud-provider and enterprise-security offerings are managed services.
Kubernetes-native?
kgateway and Agent Router build directly on the Kubernetes Gateway API — a natural fit if your platform is already Envoy- or K8s-centric.
Do you need MCP support?
Tyk MCP Gateway, agentgateway, WSO2, Gravitee, IBM ContextForge, Docker MCP Gateway and Cequence explicitly govern MCP tool traffic — important once agents start calling internal tools, not just LLMs.
Security- or cost-first?
Gravitee and Cequence lead with agent identity, least-privilege access and governance; Vercel and Cloudflare lead with unified billing and multi-model cost control.
Frequently asked questions
What is an AI agent gateway?
An AI agent gateway is a control layer between agents, models and tools. It routes and load-balances requests, enforces access and spending policies, applies guardrails, records traces and cost data, and increasingly governs Model Context Protocol (MCP) tool calls and agent-to-agent (A2A) traffic.
How is an AI agent gateway different from an AI or LLM gateway?
The two overlap heavily. An LLM gateway focuses on traffic between applications and model providers. An agent gateway extends the same control plane to the tools agents call through MCP and to agent-to-agent communication, with added emphasis on agent identity, tool-level permissions and policy enforcement. See also the broader AI and LLM Gateways directory on this site.
Why do agents need a gateway?
Agents act autonomously and can call many models and tools. A gateway gives one place to authenticate agents, limit what they can do, cap spend, block prompt injection and data leakage, and audit every action. Without it, each agent or team ends up implementing these controls differently.
Should I choose an open-source or a managed gateway?
Open-source options such as LiteLLM, agentgateway, kgateway, Apache APISIX and Docker MCP Gateway can be self-hosted and customised, but you operate them. Managed and enterprise products from vendors and cloud providers add support, governance dashboards and compliance features. Kubernetes-centric teams often start with kgateway or agentgateway.
What should I look for in an MCP-aware gateway?
Check for tool-level authentication and authorization, per-tool rate limits, a registry or catalog of approved servers, audit logs and tracing of tool calls, and support for the identity systems you already use. Cequence, Tyk, WSO2, Gravitee and IBM ContextForge all address MCP traffic in different ways.