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AI Agent Integration: Gartner Research Confirms Need for AI Control Layer

Three-quarters of enterprises are now piloting or deploying AI agents. But here’s the problem: actually integrating those agents with enterprise applications is proving to be one of the hardest parts of the whole endeavor. The research doesn’t mince words about the challenge. And it maps directly to the infrastructure gap Kong was built to address..

Building a Secure, Scalable AI Infrastructure with Kong and Akamai: A Technical Introduction

As organizations transition from experimental AI to production-grade systems, they often face a fragmented landscape of unmanaged LLM providers, complex tool integrations, and escalating security risks. This infrastructure gap leaves AI applications vulnerable to sophisticated threats like prompt injection and data exfiltration, necessitating a unified stack that secures the edge while streamlining the data plane..

From Kafka Chaos to Control: A Practical Guide to Governing Real-Time Data

Most engineering teams adopt Apache Kafka for one simple reason: it works. It scales effortlessly, it is incredibly reliable, and it powers real-time systems across almost every industry. But as your Kafka usage expands across different teams, regions, and external consumers, success creates a brand new problem. Kafka is a massive data firehose, and without the right nozzle, it quickly becomes unmanageable.

Dynamic Kafka ACLs: Implementing Identity-Aware Policies with Kong Event Gateway

Modern Kafka deployments struggle with a familiar tension. You want fine-grained access control per client, per team, and even per request. However, traditional ACLs force you into static, cluster-level configurations that are brittle, hard to scale, and painful to maintain. Administrators are often forced to manage massive, hardcoded lists of topics and users. But what if you could dynamically craft these ACLs using identity context?

Introducing Kong A2A and MCP Metrics: Visibility and Governance for AI Tool Adoption at Scale

Scaling LLM and agentic AI adoption from pilot programs to enterprise-wide deployments is a massive logistical rollout. As AI and agentic usage grow, so does a nagging question for leadership: **Are agents using the right tools to get the job done?** While raw infrastructure metrics might tell you if a server is "up," they fail to tell you if your AI investment is being leveraged.