We had a client who'd create incredibly detailed Jira tickets. Their lead developer (also their only developer) would write exactly how he'd want us to implement a given feature, and what the expected output would be.

The guy is also a complete tool. I'd point out that what he described wasn't actually what they needed, and that there functionality was ... strange and didn't actually do anything useful. We'd be told to just do as we where being told, seeing as they where the ones paying the bills. Sometimes we'd read between the lines, and just deliver what was actually needed, then we'd be told just do as we where told next time, and they'd then use the code we wrote anyway. At some point we got tired of the complaining and just did exactly as the tasks described, complete with tests that showed that everything worked as specified. Then we where told that our deliveries didn't work, because that wasn't what they'd asked for, but couldn't tell us where we misunderstood the Jira task. Plus the tests showed that the code functioned as specified.

Even if the Jira tasks are in a state where it seems like you could feed them directly to an LLM, there's no context (or incorrect context) and how is a chatbot to know that the author of the task is a moron?

Who says an LLM can’t be taught or given a system prompt that enables them to do this?

Agentic AI can now do 20 rounds of lobbying with all stake holders as long as it’s over something like slack.

A significant part of my LLM workflow involves having the LLM write and update tickets for me.

It can make a vague ticket precise and that can be an easy platform to have discussions with stakeholders.

Claude Code et al. asks clarifying questions in plan mode before implementing. This will eventually extend to jira comments