"Gateway" is doing double duty in this ecosystem right now, and almost nobody stops to notice that it means two completely different things depending on who's saying it.
Say "gateway" to a platform team and they hear Kong, Apigee, AWS API Gateway — the thing that's sat in front of every API they own for a decade, handling auth, rate limiting, and routing. Say it to someone building agents and they hear LiteLLM, Portkey, AgentGateway — a newer, AI-native layer that sits between an application and the model provider, doing cost tracking, fallback routing across OpenAI, Anthropic, and Bedrock, and prompt/completion logging.
Both are becoming necessary infrastructure, for genuinely different reasons. And neither one answers the question that actually matters once an agent is in production.
Why enterprise API gateways are showing up in agent conversations
Agents don't just call models — they call tools, and tools are usually internal APIs that already sit behind a gateway the platform team built years ago. As agent traffic grows, that existing gateway becomes the natural front door for agent-originated requests too, because rebuilding auth and rate limiting from scratch for "agent traffic" specifically would be redundant. This is why Kong, Apigee, and AWS API Gateway keep coming up in agent infrastructure conversations that, on paper, have nothing to do with them.
Why AI-native gateways exist at all
The moment an organization is calling more than one model provider — which is almost immediately, since fallback and cost optimization demand it — they need something that abstracts "call a model" away from "call OpenAI specifically." That's the whole AI gateway category: routing, retries across providers, spend tracking, response caching. It's solving a real problem, and it's solving it well. It is not, by design, trying to answer whether a specific agent should have been allowed to touch a specific piece of sensitive data on this specific call.
The gap both categories share
Here's the thing I keep coming back to: a gateway's job — either kind — is to move a request from one place to another correctly, quickly, and observably. That's necessary. It's also a different job from deciding whether the request should have been allowed in the first place, and producing evidence of that decision that survives a regulator asking about it eight months later.
An API gateway will tell you a request came from a valid, authenticated caller. It won't tell you whether that caller's agent was authorized, under the policy in force at that moment, to access a customer's SSN. An AI gateway will tell you exactly which model answered a prompt and what it cost. It won't tell you whether the context that got fed into that prompt should have been in the agent's hands at all.
Where AutoPIL actually sits
Not as a third gateway. As the thing both kinds of gateway are missing, plugged in at the point that matters to each of them differently.
Behind an enterprise API gateway, AutoPIL ships as the authorizer itself — a Kong plugin, an AWS Lambda Authorizer, an Apigee policy. The gateway keeps doing what it's good at — routing, rate limiting, TLS termination. AutoPIL is the thing it calls to get an actual ALLOW/DENY decision on the data being requested, with a cryptographic record of why.
Behind an AI-native gateway, AutoPIL isn't trying to out-route LiteLLM or out-cache Portkey. It enforces at a different point in the flow entirely — the moment an agent's tool call is about to pull real data into context, before that data reaches the model at all. The AI gateway can keep optimizing which provider answers the prompt. AutoPIL is the layer deciding whether the prompt should have had that data in it to begin with.
Why not just become a gateway
I get asked a version of this question often enough that it's worth answering directly, the same way I've had to answer "why not build the harness." The API gateway market is Kong, F5, Cisco, and AWS — infrastructure incumbents with a decade of head start. The AI gateway market is moving just as fast, with well-funded, fast-shipping players who are good at exactly what they do. Neither is a market AutoPIL should want to win by out-building the incumbents at their own job.
What nobody in either category is building — because it isn't their job, and there's a real conflict of interest if it were — is an enforcement point that's independent of the routing layer, sitting specifically at the boundary where an agent touches real data, producing a record of that decision nobody, including AutoPIL, can quietly rewrite afterward.
That's the same argument as the harness, just one layer over. The more infrastructure multiplies around agents — more gateways, more harnesses, more routing layers — the more valuable it becomes to have exactly one thing that isn't trying to be any of them, sitting at the one point that's the same regardless of which gateway, which harness, which provider a given team picked this quarter.
Anil Solleti is the founder of AutoPIL, a governance-first agentic AI platform for regulated enterprises, and a partner at VibrantCapital.ai.