What are AI gateways in 2026, and do you actually need one now?

Sam Richard — Tue, 14 Apr 2026 19:28:26 +0000

Six months ago, we wrote about AI gateways and whether you actually needed one. At the time, the pitch was straightforward: a middleware layer to manage API keys, handle failovers, and route prompts to the right model. Useful, but optional for most teams.

That advice aged fast. The rise of agentic AI (autonomous systems that plan, use tools, write code, and call other models on your behalf) has changed what AI infrastructure needs to handle. A single user request can now trigger dozens of LLM calls, tool invocations, and multi-step reasoning chains. The gateway isn't just routing prompts anymore. It's managing sessions.

Let's take a fresh look.

What is an AI gateway (2026 edition)?

An AI gateway is still a control tower for your AI traffic, a middleware layer between your applications and the AI services they rely on. That part hasn't changed.

What has changed is what "AI traffic" looks like. In 2025, it was mostly prompt-in, response-out. In 2026, it's agents calling Claude Opus for complex reasoning, then Haiku for fast classification, then hitting a Model Context Protocol (MCP) server to read from Slack, then writing to a database, then calling another model to verify the result—all from a single user request.

AI gateways now play a role similar to what ngrok does for production API workloads. ngrok creates a secure, observable interface between your services and the public internet. AI gateways do the same, but for the increasingly complex web of model interactions, tool calls, and agent actions flowing through your stack.

If ngrok is the gateway to your web traffic, an AI gateway is the gateway to your agent traffic.

Why AI gateways went from "nice to have" to essential

Agents changed the traffic pattern

A simple chatbot makes one API call per user message. An AI agent might make 20–50 calls to complete a single task—mixing reasoning models, fast models for classification, tool-use calls, and code execution. Without a gateway, you have no visibility into what your agents are actually doing, what they're costing you, or whether they're behaving correctly.

The old problem of "too many shovels (models), too little gold (control)" didn't go away. It got worse. Now the shovels are wielding themselves.

MCP made tool integration universal

MCP has emerged as the standard for connecting AI models to external tools and data sources. Your agents now talk to Slack, Notion, databases, browsers, and internal APIs through MCP servers. An AI gateway sitting at this boundary is the natural enforcement point for access control, rate limiting, and audit logging—the same role API gateways have played for REST traffic for over a decade.

Multi-model is now multi-everything

In 2025, "multi-model" meant switching between OpenAI and Anthropic. In 2026, a single workflow might use Claude Opus for deep reasoning, Haiku for fast triage, a fine-tuned open-source model for domain-specific tasks, and a local model for sensitive data that can't leave your network. Intelligent routing across this matrix, factoring in cost, latency, capability, and data residency, is exactly what gateways are built for.

How do they actually work in 2026?

The architecture has evolved from simple request proxying to session-aware orchestration:

As a category, we're converging on AI gateways that:

Intercept every LLM call, tool invocation, and agent action that passes through your stack
Route to the right model based on task complexity, cost budget, latency requirements, and data sensitivity
Track sessions across multi-step agent workflows, not just individual prompt/response pairs
Enforce guardrails like content filtering, PII detection, and compliance rules at the gateway layer rather than in each application
Give you full traces of agent behavior: what models were called, what tools were used, what data was accessed, and what it all cost

ngrok's AI gateway already handles several of these today: it intercepts LLM calls at the SDK level, routes across providers with automatic failover and cost-based selection, and manages API keys so your team doesn't have to. Guardrails like PII redaction, prompt injection detection, and compliance filtering are on the roadmap. If you've ever used ngrok's Endpoint Pools, the pattern will feel familiar: a pool of endpoints behind a single intelligent entry point that distributes requests for reliability and performance.

Do you actually need one now?

Our advice has shifted since 2025:

Scenario	2025 advice	2026 advice	Why it changed
Single model, simple chatbot	Skip it	Still probably skip it	No agent behavior means your SDK still handles the basics
Multiple models, production app	Consider it	Yes	Multi-model routing now spans cost, latency, capability, and data residency
Agentic workflows in production	Barely existed	Essential	A single request can trigger 20–50 LLM calls, tool uses, and reasoning chains
Regulated industry (healthcare, finance)	Recommended	Non-negotiable	Agents accessing tools and data via MCP need auditable access control
Internal tools with MCP integrations	N/A	Strongly recommended	MCP made tool integration universal, and gateways are the natural policy layer

The threshold has dropped. If you're running any agentic AI in production (and in 2026, most teams are), you need visibility and control over that traffic. An AI gateway gives you both.

The only teams that can safely skip an AI gateway are those making straightforward, single-model API calls with no agent behavior. If your AI does more than answer questions, if it takes actions, you want a gateway watching.

The future: agent-aware networking

The prediction from our 2025 post is already coming true. AI gateways are evolving into agent-aware networking layers that handle not just routing and security, but also semantic caching (why re-run an expensive reasoning chain for a query you've seen before?), cross-agent coordination, and workload balancing between providers the way CDNs distribute content globally.

Here's where things sit on the modern AI infrastructure stack:

The question is no longer whether you need an AI gateway. It's whether your current infrastructure can handle the agent traffic that's already flowing through it.

Be part of what's next

ngrok.ai is live, and we're building the next generation of AI-aware networking infrastructure. Follow along on X, LinkedIn, Bluesky, and YouTube for what's coming next.

How Shinobi Security gets access to apps with internal DNS names or redirect flows to run AI agents

Sam Richard — Fri, 18 Jul 2025 18:38:23 +0000

Shinobi Security is like renting a ethical hacker—but this one's AI and never sleeps, while also being trained alongside the devs that built the app they're testing.

Shinobi offers their customers teams of AI agents. These agents collaborate, escalate privileges, chain vulnerabilities, and think like real attackers, because their creators used to be ones.

Developers treat these agents just like they would security teammates. As soon as developers create a new version of their apps, they prompt the agent with a bit of context about the app and how it is supposed to work, and the agents get to work hacking at the new app and attempting to find vulnerabilities.

Shinobi flips the script of most security tools: instead of drowning you in dashboards and warnings, it proves your vulnerabilities with working exploits. You don’t get a random alert. You get told, “You will get hacked unless you fix this, and here’s how.”

How does Shinobi get access to customers' apps?
Customers didn’t want even Shinobi's AI hackers testing on production apps, so they often requested that Shinobi agents work within the confines of staging or dev environments. Shinobi’s product works wonderfully once it’s set up in a customer’s environment, but customer environments can be tricky.

Applications often required custom headers or authentication credentials just to honor requests from Shinobi's agent, which meant they would have to customize their solution for every nuance and oddity of those dev/staging environments.

Shinobi Security selected ngrok to create public endpoints for local machines within their customers' networks. ngrok's flexibility and ease of configuration were pivotal in their decision—Varun was able to get a PoC up and running in minutes.

Implementing ngrok reduced the setup time to run penetration tests on their customers' apps to just 15 minutes.

Even through ngrok simplified how Shinobi accessed customer networks and shrunk the time-to-test, they sometimes ran into other hurdles.

For example, some agents would need access to an app within a customer’s network that had internal DNS names or redirect flows that would break in an outbound tunnelled environment (e.g., 302 redirects to internal.corp.local).

To solve for this common issue (that devs usually tie a bunch of different tools together to fix) Shinobi wrote a unique policy unique traffic policy to intercept and rewrite 302 redirect headers coming from customer apps, preserving UX and agent behavior.

on_http_request:
  - actions:
      - type: add-headers
        config:
          headers:
            host: 127.0.0.1
      - type: forward-internal
        config:
          url: https://service-01.customer-abc.internal

on_http_response:
  - expressions:
      - "res.status_code == 302"
    actions:
      - type: set-vars
        config:
          vars:
            orig: "${url.parse(res.location).path}"
      - type: remove-headers
        config:
          headers:
            - Location
      - type: add-headers
        config:
          headers:
            Location: "$NGROK_DOMAIN/${vars.orig}"

As they expand their customer base, Shinobi also imagines writing other policies for ensuring their AI agents can properly authenticate themselves even in dev/staging environments.

Forem: Sam Richard