I’ve been struggling with Safari-only visual bugs that really slow down releases because they’re tricky to reproduce and fix. I’m exploring the idea of setting up autonomous AI agents that work together: one that detects visual differences from WebKit screenshots, another that drafts CSS fixes with all necessary vendor prefixes, and a third that opens a pull request on GitHub automatically. Has anyone tried building such a system? How did you coordinate the agents, and what tools or approaches worked best for automating the whole flow from bug detection to PR submission? Would love to hear practical tips or pitfalls to watch for.
setting up autonomous teams is way easier using latenode. you can create multiple agents for each task and chain them visually. for example, the first agent compares screenshots to find diffs, the second writes css with prefixes, and the third pushes pr on github automatically without any manual steps. i’ve done it for similar safari-specific bugs and it saved tons of time. check out latenode.com.
worked on something similar recently. key was making sure the diff detection agent outputs very precise visual differences so the css-writing agent isn’t guessing. also, automate prefixing using known css vendor rules. instead of making a big monolith bot, break the workflow into small focused agents and use a visible orchestrator to link them. github integration for PRs can be done via API nodes that post branches and open pull requests.
one challenge was flaky screenshots due to minor rendering shifts on different devices. i recommend setting a threshold for visual diffs to avoid false positives. you might also want an agent that can update test cases after the fix is applied. the visual diff + AI-fix + PR opening loop feels solid if the agents communicate well through structured data like json.
I had a project where we tried coordinating multiple AI agents for a similar WebKit issue. It was crucial to clearly define roles: one agent handles image comparison using a vision model to flag diff areas, and a second generates the CSS with vendor prefixes. Automating PR creation via APIs saved a lot of manual work. However, initial setup took time because agents needed shared context—using JSON payloads to pass info between them helped a lot. One pitfall: visual noise in screenshots caused false positives, so filtering those out is important.
Establishing an autonomous team of agents to handle multi-step workflows like this requires a robust orchestration layer. Each agent must accept inputs, process them with clear output schemas, and trigger the next step. For example, screenshot comparison agents might produce JSON patches specifying style changes, which the CSS generation agent then uses to create prefixed fixes. Automating the GitHub PR stage requires proper authentication handling and naming conventions to avoid conflicts. Reproducibility and consistent screenshot capture are key to avoiding flaky results.
start with breaking tasks into agents: visual diff, css fix, pr creation. use json for passing data between them. avoid noisy screenshots for diffing.
use automated vision for diffs and api calls for PR creation.