Using AI Agents for Real Network Testing

I Asked an AI Agent to Test My Network. It Did.

Talking about AI in 2026 is tricky. Some people are still amazed by what these systems can do. Others are already tired of hearing about them.

But every now and then, AI still does something that makes even experienced engineers stop and say: “wait, it can do that already?”

Did you know that you can ask Codex or similar AI agents to perform a professional network test for you, simply by describing what you want in plain English? Not consumer-grade “run a speed test” stuff, but actual network-engineering tests with separate TCP and UDP measurements, packet loss, jitter, and throughput analysis.

Here’s what happened when I asked Codex to install Tessabyte:

It found the installer, verified the digital signature (a very prudent move), and installed the software silently in well under two minutes.

You can then ask an AI agent to install Tessabyte on a second computer as well, because Tessabyte measures performance between two endpoints, one acting as a server and the other as a client. You can even ask the agent to do this on a remote Linux VM or cloud instance, assuming you have the appropriate credentials. Or, don’t install the server part for now, just play with our public servers, test-us-ipv4.tessabyte.com (for IPv4) or test-us-ipv6.tessabyte.com (for IPv6).

Then you ask this:

I have the Tessabyte app installed on this computer. Run it in console mode and test my network connection performance. Connect first to the local server running at 192.168.1.161, and then connect to the cloud server running at test-us-ipv6.tessabyte.com. Perform 5 testing cycles for each server using the “Voice” QoS traffic type. Then show me the results for both the first and second server.

What impressed me was not that AI managed to run a network test. Any decent script could do that years ago. The impressive part was everything that happened before the test even started.

Codex found and read the Tessabyte manual, figured out the command-line syntax, understood the client/server workflow, executed the tests, interpreted the output, and presented the results in a readable form — all without a human walking it through the process step by step.

A few years ago, this would have required an engineer reading manuals, installing tools manually, configuring endpoints, and troubleshooting command syntax. Now you can simply describe the goal in plain English and let the agent handle the mechanics.

The first run took less than five minutes because Codex had to discover the workflow and understand the command structure. Future runs are much faster.

And once the AI understands the workflow, you can go much further than a single test. You can ask it to schedule recurring measurements, aggregate historical results, detect anomalies, compare TCP and UDP behavior over time, or generate reports automatically.

We are entering the era of intent-driven network testing.