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AI Agent Gateway vs AI Agent Assurance

A gateway governs traffic routed through it. AI agent assurance governs every agent action at the endpoint, before execution, even when traffic bypasses the network.

In short

An AI agent gateway routes configured agent, tool, and MCP traffic through a network control point. AI agent assurance runs where agents execute and evaluates each action before it occurs. The structural difference is coverage, execution context, and availability: gateway visibility depends on routing, while endpoint assurance follows the agent. Organizations can use both for complementary traffic management and runtime control.

LLM Gateway vs AI Agent Gateway vs AI Agent Assurance

An LLM gateway manages model API traffic. It centralizes model routing, key custody, rate limits, cost controls, caching, and logs for requests sent through it.

An AI agent gateway, also called an MCP gateway, extends that network-chokepoint pattern to agent tool and MCP traffic. It can authorize, route, and audit requests that agents send through the configured proxy.

AI agent assurance runs on the endpoint. It discovers agents and evaluates tool calls, shell commands, file access, MCP requests, and outbound API calls before execution. The comparison below focuses on AI agent gateways versus endpoint-native AI agent assurance.

Capability
Certiv
AI Agent Gateways
Primary job
Govern agent behavior before execution
Route and govern configured agent traffic
Vantage point
Runs in the agent's endpoint execution path
Runs at a central network chokepoint
Coverage guarantee
Applies regardless of model, framework, or destination
Covers only traffic configured to use the gateway
Shadow agents and local models
Discovers and governs shadow agents and local models
Misses agents and models that send no traffic through it
Execution context for decisions
Uses files, processes, environment, and session history
Primarily sees request and response payloads in transit
Enforcement timing and granularity
Acts before tool, shell, file, MCP, and API actions
Acts before forwarding proxied tool and MCP requests
Off-network and network failure
Keeps enforcing when the endpoint is off-network
Must fail open or fail closed when its path is unavailable
Traffic management features
Does not replace centralized traffic management
Handles key custody, routing, and rate limits well

Where Agent Gateway Coverage Ends

AI agent gateways handle centralized traffic management well. They can hold shared keys, route requests, apply rate limits, and audit MCP or tool traffic that passes through them. That is useful for sanctioned server-side agent fleets with controlled network paths.

Gateway coverage is opt-in. An agent can change a base URL, read a personal key from an environment variable, call an API directly, or use a local model. None of that traffic reaches the gateway. Guaranteeing gateway routing requires something on the endpoint, and that component becomes the actual enforcement point.

A gateway sees the payload in transit. It may see an agent request an MCP tool or run main.py, but it cannot see what main.py reads, which credentials are available beside it, or what the agent changed earlier in the session. AI agent assurance evaluates the action with the working environment and session history behind it.

Network-dependent enforcement also inherits the availability of its network path. If the gateway cannot be reached, it must fail open and let work continue without governance, or fail closed and stop work. AI agent assurance stays in the execution path and keeps applying policy when the machine is off-network.

The controls can compose. A gateway can manage keys, routing, and proxied traffic for a server-side fleet. Certiv delivers AI agent assurance on endpoints, including shadow AI discovery, AI agent inventory, continuous authorization, and pre-execution policy enforcement wherever an agent sends traffic.

FAQ

Frequently Asked Questions

Expand to view common questions.

What is the difference between an LLM gateway and an AI agent gateway?
An LLM gateway manages model API traffic, including routing, key custody, rate limits, caching, cost controls, and logs. An AI agent gateway extends the network-chokepoint pattern to agent tool calls and MCP requests. Both depend on traffic being configured to pass through them. Neither automatically governs actions that execute locally or bypass the configured route.
Do I need an MCP gateway?
Maybe, if you need centralized key custody, routing, rate limits, or audit records for sanctioned MCP servers. An MCP gateway can govern requests that pass through its network path. It is not sufficient by itself for complete agent security, since direct calls, personal keys, shadow agents, and local models can bypass it. AI agent assurance supplies coverage and execution context where the agent runs.
What is the AI agent gateway alternative to routing agent traffic through a gateway?
The alternative is endpoint-native AI agent assurance. It runs in the agent's execution path instead of requiring every request to traverse a network proxy. It can evaluate shell commands, file access, tool calls, MCP requests, and outbound API calls before they execute. Coverage does not depend on a configured base URL or network destination.
How does endpoint-resident vs network-dependent agent enforcement differ?
Network-dependent enforcement sees only traffic routed through its control point. AI agent assurance sees the agent's actions on the machine, including local execution and direct connections. It can use files, processes, environment state, and session history when making a decision. It also continues enforcing policy when the endpoint is off-network.
Can Certiv and an AI agent gateway run together?
Yes. An AI agent gateway can manage centralized keys, routing, rate limits, and audit for proxied traffic. Certiv can govern what agents do on developer machines and employee workstations before each action executes. Together, they provide traffic management at the network layer and AI agent assurance at the endpoint.

See AI Agent Assurance in Action

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Keep reading

Pre-execution policy enforcement

The mechanism behind AI agent assurance: intercept, evaluate, and enforce policy on every agent action before it executes.

Our approach

Why the endpoint is the only vantage point where an AI agent’s full decision loop is observable.

AI Gateway vs AI Agent Assurance

The traffic-management side of the gateway question: routing, keys, and cost vs governing agent behavior.

What is AI Agent Assurance?

The full definition of the category: the endpoint control layer for AI agents.