Stories from the Factory Floor: My own private software factory
Blog post from LaunchDarkly
LaunchDarkly engineer describes “Thousand Cuts,” an autonomous QA workflow that uses scheduled agents to explore the company’s web application through a real Chromium browser, identify customer-facing defects, file evidence-rich Jira tickets, deduplicate and prioritize them, and generate pull requests for approved fixes. Coordinated through Jira status changes and monitored in Slack, the system separates crawling, classification, and fixing tasks, while humans retain authority over reviews, product decisions, and releases. During its initial weeks, the workflow generated hundreds of tickets, identified many duplicates, opened dozens of pull requests, and resolved small but persistent usability, visual, and technical issues that conventional tests and code-review tools may miss because they are not tied to recent code changes. The author emphasizes that agent capacity is not the main constraint; human decision-making, review queues, design ambiguity, release coordination, and safe feature promotion remain the harder parts of an AI-assisted software delivery lifecycle. The post also compares the effort with a related automated operations-triage system and argues that platforms such as LaunchDarkly can help connect AI-generated code changes to accountable, human-approved feature flags and release plans.
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