Agent Gateway: The Next Evolution of the API Gateway

API gateways have long managed API traffic through routing, authentication, rate limits, transformations, and observability, but AI agents introduce a different challenge because they dynamically choose models, tools, actions, and workflows while pursuing high-level goals. LLM gateways address model-specific concerns such as provider routing, token costs, prompt security, and reliability, while MCP gateways centralize access to Model Context Protocol tools, credentials, permissions, discovery, and auditing. An agent gateway is presented as a broader control and enforcement layer spanning models, APIs, MCP tools, memory, other agents, and human approval processes, governing an entire stateful agent execution rather than isolated requests. Its proposed functions include identity propagation, least-privilege access controls, policy enforcement, sensitive-data redaction, budget and execution limits, loop detection, workflow-level tracing, and approval requirements for risky actions. The discussion argues that these capabilities are necessary because agents can take consequential actions, operate across delegated and multi-step workflows, encounter prompt-injection and tool-output risks, and create observability and cost-management challenges that conventional gateways cannot fully address. Postman describes its planned Fabric Gateway as a unified platform intended to combine API governance, model routing, MCP management, and agent-level policy controls for production agentic systems.

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