If you, like me, have struggled to make sense of how AI is actually supposed to help you productive, there is hope. If you or your org has a basic subscription of any AI agent and you are being challenged to show your team to be AI native, following are the projects I started out that helped me accomplish two things: Escape the hell of metawork i.e. writing and uploading test cases in Jira, creating code blocks in automation that I could refine and build upon to achieve in-sprint testing and remove the gaps that are buried in the avalanches of zoom calls, confluence pages and “That person knows about it. They can help you out”.
But as you start out solving these two problems, something else happens: You start discovering many gaps that block your testing and can be smoothened. Doesn’t matter if the BA is occupied to update the defects or you don’t want to spend time updating Acceptance Criteria on the tickets.
To be clear, these do serve value. A test case is used to revisit when something goes awry on production to trace what was missed in testing. An Acceptance Criteria keeps all the stakeholders in the loop about what are the areas you are seeking to evaluate. But all those steps can be offloaded to AI to add the verbosity and details. You bring your human ingenuity to see the user behaviours that will break during a transaction and focus on dampening the places where you have been used to putting out the fires.
The upcoming blogs show you how to build those agents.
