"He now uses end-to-end AI automation to build software. His AI workflow retrieves issues from our ticketing system, creates an execution plan, writes the code, adds or updates automated tests, checks the code for correctness and formatting, and presents the code for his review in a pull request. John reviews the work at each step. Once he approves the pull request, that kicks off more automation, testing, then deploys the new software to production, and finally updates and closes the ticket."
"With automation, my colleague John is moving 3 to 4 times faster and with similar or higher quality. He's also having more fun. AI automates the drudgery that would have given him a mental break. The work that's left is all heavy thinking. He has to meticulously review everything the AI creates, and he often goes home exhausted. Like the rest of us, he worries that AI might make us dumber in the long run."
An end-to-end AI workflow retrieves tickets, creates execution plans, writes code, updates automated tests, checks correctness and formatting, and produces pull requests for human review. Human engineers review each step and approve changes before automation proceeds to testing, deployment, and ticket closure. This approach can increase development speed by three to four times while preserving or improving quality and making work more enjoyable. The approach removes repetitive tasks but concentrates cognitive effort on heavy thinking and meticulous review, often causing exhaustion. Newsrooms and developers can apply similar automation to publishing pipelines and data-visualization workflows, though tooling and CMS integration remain experimental.
Read at Nieman Lab
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