If you are still using AI only to write code, you are probably working somewhere around 2022 or 2023
I don’t mean that Cursor or ChatGPT are useless. They are obviously useful. But opening a chat, pasting a task, generating a function and then reviewing it is a very small part of what an engineer actually does
Most of the work is not writing code
It is understanding what the client actually asked for, finding what was discussed in Slack, checking whether the ticket still reflects the current requirements, reading old decisions in Notion or Google Docs, looking at logs, finding the relevant code, checking existing pull requests, understanding who owns the next step and making sure the result can actually be shipped
If your AI only has access to the repository, it knows almost none of this
My setup is closer to having one entry point into the whole engineering environment
It has access to the codebase, GitHub, Linear, Slack, Notion, Google Docs, Grafana and logs. The permissions are separated where they need to be separated, but the important part is that the agent can combine information from all of these systems
So instead of asking it to write some isolated piece of code, I can give it a real task
A client mentioned a problem during a meeting. The agent can find the notes, compare them with the current Linear ticket, look through the relevant Slack discussions, check the logs, trace the problem in the codebase and prepare a fix with tests and a pull request
You can also split the work between multiple agents. One investigates the logs, another checks product context, another reviews the code, another looks for previous decisions. They do not even have to use the same model
You can build your own skills and workflows around this as well. For example, how incidents should be investigated, how tickets should be reviewed, what checks are required before release or how documentation should be prepared
After some time, this becomes an environment built specifically around the way you work
And honestly, none of this requires some huge internal AI platform. In many cases it is just integrations, permissions, a few reusable skills and the willingness to automate things that you currently do manually across fifteen different tabs
The gap between this and simply asking ChatGPT to write code is enormous
It is roughly the difference between using AI as autocomplete and using it as an interface to the company’s actual engineering context