Systems | Development | Analytics | API | Testing

Kong AI Gateway already gives platform teams a central place to route, observe, and enforce policy across AI traffic. Security teams working alongside those platform teams often need another set of answers: Which agents are connected? Which attack paths actually succeed against them? What happened across the full session or tool chain? And if an agent is compromised, can its next action be stopped before it reaches an enterprise system? The integration is designed to be complementary.

Kong is named **a Leader** in *The Forrester Wave: API Management Software, Q3 2026*. APIs used to be about connecting systems to each other. Now they're also how AI agents connect to everything: tools, data, other agents. That shift changes what good API management looks like, and it's where we at Kong have shifted our focus. Forrester's take on where we land.

We said you'd see the evidence quickly.. Here it is. **Kong AI Gateway 2.0 is now generally available**, and it isn't arriving quietly.

You've mapped the architecture. You know your agents need to retrieve context from external tools, coordinate with other agents, and propagate mutations through your systems. The model logic is solid. What you haven't fully solved is what sits between those agents and everything they're trying to reach. That's the gap Kong was built to close. Multi-agent workflows live and die on context.

Most teams building multi-agent systems hit the same wall at roughly the same point. The prototype works. Agents chain together, tasks complete, the demo impresses the room. Then someone asks: "What happens when this runs a thousand times a day? What happens when an agent calls an external API that's down? How do we know what the agents actually did?" That's when the architecture conversation starts. Here's the framing that clarifies most of these questions.

Every team running production LLMs has had the same idea: not every request needs the frontier model. Intelligent model routing (or LLM routing)— choosing a model per request on criteria such as task complexity, cost, latency, or quality — enables more efficient model usage.

You've probably seen the demos. An AI model kicks off a task, hands pieces of it to other AI models, and somehow the whole thing gets done. Emails drafted, code reviewed, reports summarized — all without a human in the loop. While a single agent doing one thing is impressive, the true paradigm shift occurs when transitioning from single-agent to multi-agent AI systems. It looks like magic. It isn't.

You Can't Govern What You Can't See A decade ago, cloud and API sprawl got ahead of governance, and enterprises spent years trying to account for costs they'd never tracked. Today, we're seeing the same pattern around AI, with hundreds of customers proxying traffic via Kong AI Gateway, which includes LLM, MCP, and agent connectivity. *AI spending will reach $2.59 trillion in 2026.* I regularly like to share what we're seeing in production at Kong.

You've diagnosed the problem. Your agentic AI initiatives are stalling — not because the models are wrong, but because the integration layer underneath them wasn't built for this. Batch data, rigid schemas, fragmented governance, no real-time event delivery. Now the question is: what do you actually build, and how do you build it without tearing down the infrastructure you already have?

If you've been around long enough to remember when iPaaS was the answer to everything, you know the pattern. New paradigms arrive. Someone realizes that wiring it into existing infrastructure is harder than the demos suggested. An integration layer gets built. That layer slowly becomes load-bearing. Eventually, the integration layer becomes the bottleneck. We're at that moment again — except this time, the new paradigm is agentic AI, and the bottleneck is forming faster than usual.