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Scale least-privilege MCP access with caller API keys, environment and team policies, approval workflows, and deny-first controls in AISIX AI Gateway.

Learn how AISIX usage events and Prometheus metrics reveal MCP tool volume, latency, failures, limits, and guardrail blocks without storing sensitive payloads.

Managed OAuth helps agents authenticate, but production AI systems still need gateway-side authorization, policy, and audit for tools, MCP servers, and APIs.

New tools can turn HAR files, OpenAPI specs, and authenticated web apps into agent tools. Learn why API contracts for agents need gateway governance.

AI agents are getting real API access, cloud permissions, and tool credentials. Learn why gateway-level identity, short-lived credentials, and audit logs matter.

New Kubernetes AI-agent sandbox projects show why platform teams need gateway-level egress policy, identity, rate limits, and audit logs for agent workloads.

New browser-based AI agent projects show why teams need API boundaries, egress controls, audit logs, and gateway policies for agentic workflows.

Learn how MCP Gateway architecture secures AI agent tool calls with identity, policy, rate limits, audit logs, and observability.

Learn why AI agents need an AI gateway for model routing, tool calls, rate limits, access control, observability, and enterprise governance.

Learn how an AI Gateway secures agent workflows with routing, rate limits, auth, and observability.

Explore AI agent vulnerabilities and how API7/APISIX as an AI Gateway secures interactions, preventing exploitation.

Discover how to secure autonomous AI agent payments and manage API billing using Apache APISIX and API7 Enterprise.

Learn how API Gateway permission guards enforce context-aware policies, protecting sensitive files, production databases, and external APIs.