Anyone who has built the software side of a connected product knows the integration job. The device keeps its readings and settings in one format and the gateway or cloud service wants those same values as rows in a SQL database. Somebody writes a converter. Then somebody has to keep that converter alive for as long as the device stays in the field, which for industrial equipment or embedded systems can easily be ten years. The converter has to handle offline periods, retries, out-of-order updates, and the device’s own data format changing over time.

Most sites run a thin slice of nginx in front of their application: TLS termination, serving static files from a folder, a couple of routes to a backend service, health checks, and the occasional reload. The configuration grows anyway, one regular expression at a time, until nobody is quite sure which block handles a given URL, and a change that looks safe to one person breaks something in production that another person didn’t know existed.

Most dashboards ask the same few questions all day long: requests per endpoint, month-to-date usage for one customer, error rates by status code. The usual setup recounts raw rows from scratch on every refresh, or ships every row to a hosted analytics service that bills by the gigabyte. A dashboard that needs to answer quickly on demand either waits for a database query to finish, or pays for a service that stores everything.

Cloud coding agents start each task on a fresh machine. Before the agent can run a single test, something has to install the right runtime, start a database, configure environment variables, and download dependencies. Every platform that runs agents wants those instructions in its own file: Copilot expects a setup workflow, Cursor builds a Dockerfile, other platforms want shell scripts or configuration in other formats. A team that uses two or three agents writes the same setup two or three times, and mistakes usually show up later as an agent session that stalls because a database port wasn’t open or a runtime was the wrong version.

A coding agent spends its day reading text written by strangers: issues on your issue tracker, snippets pasted into prompts, code from repositories, instructions on web pages. One malicious instruction planted in that text can be enough to make an agent send a deploy token somewhere it shouldn’t. On a machine-wide VPN, that request leaves the same way as everything else, and afterwards nobody can say which program sent it.