From the start, we built Monogoto to be event-driven. At the time, we had no idea it would be perfect for AI agents. We were in the right place at the right time, and now that moment has arrived.
Let me share a prediction.
In the next two or three years, IoT connectivity providers will not be chosen by procurement teams. They will be chosen by AI agents, the same agents that increasingly run our fleets, our deployments, our day-to-day operations. And AI agents have very different tastes from humans.
A human evaluating a connectivity provider looks at a coverage map, a pricing sheet, and a PDF datasheet. An AI agent does not care about any of those. It looks for four things:
- Can I get all the data? Events, signaling, sessions, DNS, NetFlow, all of it, in a stream that the agent can consume.
- Is the data coherent enough to reason over? Not raw logs. Not fragmented across operator portals. Structured, semantically clean, joinable across layers.
- Can I act through APIs? Every operation, steering, routing, DNS, lifecycle, breakout, exposed and programmable. No phone calls. No NOC tickets.
- Can I talk to other agents through it? MCP-compatible. Agent-to-agent. Composable with the rest of the agent ecosystem.
That is the modern IoT connectivity stack. Not “hybrid coverage.” Not “global eSIM.” Those are table stakes now. The differentiator is whether the platform was built for AI agents to use.
A personal note: we got lucky, but it wasn’t an accident
I will be honest with you. When we designed Monogoto’s architecture, we were not thinking about AI agents.
We made one decision early on that has paid off in ways we could not have predicted: we built the platform event-driven from day one. Every signaling exchange, session, DNS query, and routing decision was captured as an event, queryable as a stream, and exposed through APIs. We did this because it was the cloud-native best practice, the architecture pattern hyperscale software companies had used for a decade. It was the right choice for observability, scale, and debugging.
What we did not realize at the time is that we were also building the perfect substrate for AI agents.
The legacy telecom stack was built around tickets, consoles, and human operators staring at dashboards. To make that AI-ready, the incumbents have to refactor twenty years of architecture. They have to retrofit event streams into systems built around CDR files. They have to expose APIs for operations that have always required a human in an NOC. They have to invent the agent layer on top of a substrate that was never designed to support one.
So what does migration look like for enterprises moving from legacy to AI-native stacks? Pragmatically, it often begins with adopting a hybrid approach: layering modern APIs and event streaming capabilities on top of existing systems, allowing agents and automation to gradually coexist with human-centric workflows. Over time, CTOs can shift more operational control and observability to the programmable layer, decoupling critical functions from legacy consoles and manual tickets. Forward-thinking teams will treat this as an evolution, not a rip-and-replace, ensuring the organization captures both the operational gains and the momentum of the AI-native wave.
We don’t have any of that technical debt. We never built the human-only version. The same event-driven, API-first architecture we built for cloud-native observability is exactly what an AI agent wants. It turns out that the cloud-native best practice from 2018 and the AI-agent best practice from 2026 share the same architecture.
We just got there first because we could not afford to do it any other way.
Vibe-configuring your network
There’s a meme going around called vibe coding, the idea that you describe what you want, and the AI writes the code. You don’t reason about syntax; you just describe the vibe.
Here is the thing: when your connectivity platform is software-defined and exposes APIs for every operation, you can vibe-configure your network the same way.
- “Make our European devices prefer Vodafone but fall back to anyone if Vodafone is unavailable, and break out in Frankfurt for low latency.” That is a configuration prompt.
- “Cut connectivity OpEx by 12% next quarter by switching all sub-100MB devices to the cheapest available carrier per region.” That is a configuration prompt.
- “Alert me only when more than 5% of devices in any region fail to attach within 30 seconds.” That is a configuration prompt.
In a legacy stack, each of those is a multi-week project involving an NOC, a contract amendment, and a deployment ticket. In a software-defined stack, each is a handful of API calls. In an AI-native stack, each is a single sentence to your operations agent.
This is what software-defined connectivity actually unlocks. Not “we can deploy faster” but “the configuration of your global IoT network is now a conversation.”
The platform AI prefers
Here is something worth sitting with: AI agents will, increasingly, choose their own infrastructure.
Already today, when I give an AI agent a task, it picks tools. It picks libraries. It picks APIs. It does this based on what is easy to learn from documentation, what is well-typed, what has clean error semantics, and what other agents are already using successfully.
That logic will apply to every layer of every stack, including connectivity. Imagine a fleet management agent deciding which connectivity provider to spin up SIMs on. It will choose the one whose API it can learn fastest, whose documentation is online and searchable, whose events are complete enough to debug from, and whose MCP server lets it chain operations with other agents in the customer’s stack.
The connectivity provider that wins in 2027 is the one that is most legible to AI.
That is not a soft criterion. It is a hard, measurable one:
- Are your docs online and machine-readable? Yes or no.
- Are your APIs OpenAPI-spec’d? Yes or no.
- Do you expose your event stream as a queryable surface? Yes or no.
- Do you support MCP, so an agent reasoning about connectivity can pass context to an agent reasoning about something else, a fleet manager, a power manager, a billing optimizer? Yes or no.
Monogoto says yes to all of these. Not because we are riding the AI hype, but because what AI wants from infrastructure is the same as what humans should have been asking for all along. And, the impact is real: AI-native connectivity translates directly into operational efficiency, reduced manual intervention, faster time-to-resolution, and measurable cost savings across your global deployments. These are not just technical improvements, but strategic advantages that show up on the bottom line.
What this means for builders
If you are deploying IoT at scale in 2026, robotics, autonomous vehicles, point-of-sale, industrial sensors, anything in the Physical AI category, you have a choice to make about your connectivity stack.
You can pick a legacy provider with a coverage map and a portal. That gives you a working deployment today.
Or you can pick a provider whose platform was built event-first, API-first, agent-first. That gives you a deployment that is still working in 2028, when most of your fleet operations run through agents, when configuration is conversational, and when the humans in the loop handle exceptions, not daily ops.
We built Monogoto for the second one. Not because we predicted the future. Because we built things the right way and got lucky about which future arrived.
