AI Agent Firewall vs AI Gateway vs MCP Proxy

Security and platform teams are deploying AI gateways, MCP proxies, and AI agent firewalls in overlapping ways, often without a clear map of what each layer actually enforces. Part of the confusion comes from vendor positioning. AI gateway vendors have extended traffic-management products into the AI space. MCP proxy implementations have emerged alongside the Model Context Protocol as it has been adopted for tool-calling architectures. Agent firewall is a newer term used across the industry to describe policy enforcement aimed specifically at agent behavior, but it has no formal technical standard behind it. No government agency or standards body has published a definition for an AI agent firewall as a distinct product category. This means capability claims in that category should be evaluated on specific enforced functions, not on category labels.

What an AI Gateway Actually Enforces

An AI gateway operates at the network and API traffic layer. Based on cloud provider API gateway documentation, its core functions are authentication, rate limiting, request routing, schema validation, and logging for requests moving to and from LLM APIs. These are transport and format-level controls. A gateway can confirm that a request is authenticated, that it conforms to an expected schema, and that it has not exceeded a rate threshold. It does not evaluate the semantic content of a tool call, the intent behind a multi-step agent action, or whether a specific agent identity should be permitted to take a specific action in a specific context. Gateways are necessary infrastructure for AI traffic management, but they were not designed to answer questions about agent behavior.

What an MCP Proxy Handles Within Tool-Call Flows

The Model Context Protocol is an open standard, originally published by Anthropic, defining a client-server architecture for connecting AI applications to external data sources and tools through standardized JSON-RPC messages. The specification defines distinct roles for Hosts, Clients, and Servers, along with primitives for tools, resources, and prompts exposed to models at runtime. An MCP proxy sits specifically inside this tool-call flow, mediating discovery of available tools, invocation of those tools, and handling of responses. The MCP specification includes security and trust guidance recommending user consent, authorization checks, and validation of tool inputs and outputs before execution, and the MCP authorization specification defines an OAuth 2.1-based framework for authenticating and authorizing MCP clients to MCP servers. This places some security responsibility directly on implementers. MCP compliance alone does not guarantee that every proxy implementation enforces these recommendations consistently, which is a gap worth verifying rather than assuming.

Where Agent Firewall Enforcement Differs

Industry usage describes agent firewall functionality as behavioral and policy-layer enforcement, evaluating an agent's intended action, its permissions, and the context of the request rather than only the format or transport-level legitimacy of that request. This distinction matters because OWASP's Top 10 for LLM Applications identifies excessive agency, insecure plugin or tool design, and improper output handling as key risk categories specific to agentic tool-calling systems. NIST's generative AI profile (AI 600-1) similarly identifies risks tied to increased agency and autonomous action-taking that are distinct from traditional API-level risks. Neither an API gateway nor a compliant MCP proxy is architecturally positioned to answer the question of whether a given agent, with a given identity, should be allowed to chain a specific sequence of tool calls. That question sits at the policy layer, which is what agent firewall functionality is intended to address, even though the category itself remains undefined by any standards body.

Enforcement Layer Comparison

The table below summarizes where each layer's responsibility begins and ends.

Runtime Governance Gaps When Relying on a Gateway Alone

CISA and NSA joint guidance on securing AI systems recommends layered defenses, including access control, monitoring of AI system behavior, and validation of data provided to AI systems, rather than reliance on a single product category. When enterprises deploy only an AI gateway, they gain visibility into request metadata such as authentication status, rate, and schema conformance, but they do not gain visibility into what an agent is actually attempting to do once a request passes those checks. Gateway logs will show that a call was made and authenticated. They will not necessarily show whether that call represented an agent exceeding its intended scope, chaining tools in an unauthorized sequence, or acting outside the permissions assigned to its identity. NIST's AI Risk Management Framework's governance, mapping, measuring, and managing functions are built to address exactly this kind of gap, treating tools and plugins as components requiring their own risk visibility rather than assuming traffic-layer controls are sufficient.

How These Layers Combine in Practice

These enforcement layers are not mutually exclusive, and in most enterprise architectures they should not be treated as substitutes for one another. A gateway can sit in front of MCP traffic to handle authentication and rate limiting. An MCP proxy can implement authorization logic aligned with the MCP OAuth 2.1 specification for tool-call mediation. An agent firewall, or equivalent policy enforcement layer, can evaluate agent identity, scope permissions to least privilege, and assess whether a specific tool-call sequence is consistent with that agent's authorized behavior. The practical architecture question is not which single layer to choose, but which layer owns which responsibility, and whether logging and audit trails are unified across all three so that an incident investigation does not hit gaps between systems. Trussed AI focuses specifically on the runtime governance layer for AI agents, including agent identity, permission scoping, least privilege enforcement, tool approval workflows, and audit logging, positioned to work alongside existing gateway and MCP infrastructure rather than replace the traffic-layer controls those systems already provide.