Gateways have long marked important boundaries in computing.
Network gateways connected systems that used different protocols. Secure web gateways inspected traffic moving between employees and the internet.
And API gateways gave organizations a central point for routing service requests, authenticating clients, applying policy, and recording activity. They continue to serve those functions in agentic systems, where agents often reach models, tools, and business services through conventional APIs.
Over the years, gateway functions have varied by use case, but their architectural core remains: place an intermediary at a consequential boundary, then use it to direct, inspect, translate, or control what crosses.
Agentic AI is producing several new boundaries at once.
Applications send requests to multiple language models. Agents connect to APIs, SaaS platforms, databases, internal services, and Model Context Protocol servers. They increasingly communicate with other agents as well.
The industry has attached the word “gateway” to infrastructure across all of these paths. AI gateway, LLM gateway, MCP gateway, and agent gateway now describe products with overlapping capabilities and distinct purposes.
That makes the label less useful than two practical questions: What traffic does the gateway handle, and what decision is it designed to make? Meanwhile, a third question cuts across every category: Whose authority does the request carry?
For practical purposes, AI gateway offerings can be grouped into three broad patterns.
1) What Does an LLM Gateway Control?
An LLM gateway, sometimes called a model gateway, sits between an application or agent and the language models it uses.
Its main purpose is to manage inference traffic across providers and deployments. Common capabilities include model routing, retries, fallback, rate limits, caching, token accounting, cost controls, logging, and performance monitoring. Some products also inspect prompts and responses, apply content controls, or prevent sensitive data from reaching a model.
These gateways are best suited to organizations using several model providers or seeking a common operational layer for model consumption. Among widely used LLM gateways, the products below differ mainly in deployment model and the capabilities they prioritize:
- LiteLLM is particularly useful for teams seeking an OpenAI-compatible proxy across many providers, with extensive routing, fallback, budget, and spending controls.
- Cloudflare AI Gateway is well suited to organizations that want rapidly deployed observability, caching, fallback, guardrails, and data-loss prevention within Cloudflare’s infrastructure.
- Kong AI Gateway applies model routing, semantic caching, prompt controls, token rate limits, and other AI functions through Kong’s established API gateway and plugin architecture.
Others exist mainly to centralize credentials, usage tracking, and budget controls for one tool or environment. Claude Code’s own documentation draws this line explicitly: its built-in Claude apps gateway centralizes developer credentials and usage tracking for the coding agent itself, while a separate LLM gateway a team already runs handles broader multi-provider routing. Even within one vendor’s docs, the same word covers two different jobs.
The defining decision at this layer is generally: Which model should receive this request, and under which operational constraints?
That decision governs inference. It does not necessarily determine what an agent may do after the model produces a tool call.
2) What Does an MCP Gateway Control?
Agents become useful when they can reach business systems.
The Model Context Protocol provides a standardized way for AI applications to discover and invoke tools – and, where implemented, access other server capabilities such as resources and prompts.
Depending on the implementation, it may aggregate servers, consolidate tool discovery, route requests, mediate protocols, apply traffic policies, authenticate connections, or record MCP activity.
This is useful for teams that would otherwise create a separate direct connection between every agent and every MCP server. The gateway can provide a common point for organizing which tools are exposed and how clients reach them.
MCP gateways are one form of a broader tool-gateway pattern. Agents also access capabilities through conventional APIs, SDKs, databases, and proprietary SaaS integrations. The protocol may differ, but the architectural question remains: how should agents find and reach the tools available to them?
Products in this area vary considerably. Some emphasize MCP server aggregation and routing. Others concentrate on tool discovery and developer experience. More security-oriented offerings add authentication, authorization, observability, policy enforcement, or credential handling.
MCP’s authorization model has advanced quickly. The specification now treats protected MCP servers as OAuth 2.1 resource servers, and the 2026-07-28 specification adds further authorization hardening, including issuer validation that mitigates an authorization-server mix-up hole.
These developments provide an important protocol-level authorization foundation, particularly for remote HTTP-based MCP deployments. They do not determine how every enterprise should verify agent workload identity, preserve represented-user context, govern access across many servers, or broker the downstream credentials required by individual tools. Those decisions remain part of the deployment architecture.
3) What Does an Agent Gateway Control?
As agent architectures expand, organizations may need a gateway that understands multiple traffic types rather than a separate intermediary for each protocol.
An agent gateway can sit across communication among agents, models, MCP servers, APIs, and conventional services. Typical functions include protocol-aware routing, load balancing, retries, rate limits, telemetry, authentication and authorization, and resilience.
The open-source agentgateway project illustrates this approach. It supports HTTP and gRPC services alongside LLM inference, MCP tool traffic, and Agent2Agent communication through a shared data plane.
Google’s Agent2Agent protocol (A2A), now governed by the Linux Foundation, standardizes communication and task delegation among independent agents. Kong, for example, governs LLM, MCP, and A2A traffic through the same AI Gateway rather than treating agent-to-agent communication as a separate gateway category.
This category shows why the terminology is difficult to standardize. A single product may function as an LLM gateway, MCP gateway, API gateway, and agent gateway at the same time.
The central questions at this layer concern communication:
- Where should the request go?
- Which protocol should carry it?
- Is the destination available?
- Which traffic rule or access decision should be enforced?
- How can the interaction be monitored and operated reliably?
Agent gateways may participate substantially in authentication and access enforcement. Buyers should examine which identity and policy context informs those controls, how granularly access decisions can be enforced, and whether identity context survives into the destination interaction.
The Control That Cuts Across Every Gateway: Identity and Access
The three gateway patterns above are classified primarily by the traffic they handle: model requests, MCP tool calls, or broader communication across agentic systems. Yet routing alone does not determine whether the caller should be allowed to act.
Identity and access describe a different dimension. They determine the authority carried by a request, and that question can arise at any of the three boundaries. A gateway may help enforce an access decision using identity and policy context supplied by an identity and access control plane, or it may simply route and authenticate traffic without that deeper context. In more advanced deployments, the gateway may also preserve identity context across the interaction and perform token exchange when required.
Effective access enforcement at the gateway depends on decisions made by the identity and access control plane, including:
- The attested workload identity of the agent making the request.
- The human or organizational identity represented in the interaction.
- The policy decision governing the requested resource and action.
- The credential issued for approved access.
The gateway can then help enforce that decision on the request path and, where appropriate, perform token exchange.
These identity and policy functions do not inherently belong to the gateway. An LLM gateway, MCP gateway, or agent gateway may integrate with a control plane that performs identity attestation, policy evaluation, and credential issuance, while the gateway helps enforce the resulting access decision on the traffic path.
Go Deeper: Why Identity and Trust Have to Sit Upstream of Gateway Enforcement
A gateway may authenticate an MCP client, for example, without receiving an attested workload identity from a control plane. It may enforce access without preserving the identity of the user directing the agent. It may route an approved tool call while leaving the agent responsible for storing and presenting a long-lived credential.
At enterprise scale, organizations typically need identity and policy capabilities that can attest workloads, evaluate delegated authority, and issue or broker destination-specific credentials. The gateway then helps enforce that decision on the request path.
Where Aembit and IAM Fit
Enterprises need to control how agents and workloads reach sensitive services, data, and tools without relying on shared accounts, long-lived credentials, or incomplete identity context.
Aembit addresses that problem through identity and access management for agentic AI. Its control plane attests the workload identity of the agent, preserves the identity of the user or organization represented in the interaction, evaluates access requests against centrally managed policy, and issues the credential required for approved access.
MCP is one of the request paths where those decisions must be enforced. Within the broader Aembit IAM for Agentic AI Platform, the MCP Identity Gateway helps enforce access decisions when MCP clients connect to MCP servers and can perform token exchange when required. It is an enforcement capability rather than the control plane that attests identities, evaluates policy, or issues credentials.
For an MCP request, Aembit’s control plane evaluates which agent is acting, which human or organizational identity it represents, which policy applies, and whether access should be permitted. Through blended identity, Aembit considers the agent’s workload identity and the represented user’s identity together. The MCP Identity Gateway then helps enforce that decision on the request path.
Aembit can route approved requests to multiple MCP servers while maintaining credential isolation. Each interaction can preserve both the agent and user context rather than collapsing activity into a shared service account or detached agent token. When access is approved, the control plane issues the credential required for the destination, while the gateway can perform token exchange and keep that credential from being distributed to the agent.
The access decision the gateway helps enforce remains:
Which agent is acting, whom does it represent, what is it permitted to do, and which credential may it use?
What About Semantic Gateways?
The term “semantic gateway” is also appearing in agent architecture discussions, although it does not yet describe a category as clearly defined as a model, MCP, or agent gateway.
Semantic gateways make routing or mediation decisions based on meaning rather than a fixed endpoint alone. They may interpret an agent’s intent, compare it with available tool or agent capabilities, filter large tool catalogs, normalize capability descriptions, or select the most appropriate destination for a task.
This is generally a capability that can appear within the gateway patterns described above. An LLM gateway may use semantic routing to select an appropriate model. An MCP gateway may use semantic search to expose only the tools relevant to the current task. An agent gateway may match a request to the agent or service best equipped to handle it.
For that reason, semantic gateways are better understood as a decision approach than as a separate traffic category. The important buyer question is not simply whether a gateway calls itself semantic, but what information informs the decision, how predictable that decision is, and which policy and identity controls constrain the resulting action.
Which AI Gateway Is Best?
There is no single best AI gateway because these products are designed to control different traffic and make different decisions.
An LLM gateway is the natural choice for managing model providers, inference costs, reliability and prompt traffic. An MCP gateway can organize how agents discover and invoke tools. An agent gateway is suited to environments that need broader traffic management across models, agents, APIs, MCP and A2A.
These functions do not necessarily require three separate products. Some gateway products can manage model inference, MCP tool calls, and agent-to-agent communication through one platform. For many organizations, that consolidation may simplify deployment, policy management and observability.
The question is whether the product provides sufficient depth across each function. A gateway may support several traffic types while offering stronger controls for some than others. Organizations should examine its routing, protocol handling, resilience and observability at each boundary, along with its identity and access capabilities.
The deciding factor should not be which vendor uses the broadest term. It should be whether the gateway handles the required traffic and performs the required function at that boundary.
As agents gain access to sensitive data and production systems, enterprises must be able to establish which agent acted, whose authority it carried, why the action was permitted and which credential enabled it.
Routing makes agentic systems operable. Identity makes their actions accountable.
To see how the Aembit IAM for AI Platform applies these controls to an MCP request path, explore the MCP Identity Gateway documentation.
