The part of working with AI agents I keep coming back to is everything that happens between the original task and the PR. Requirements get clarified, plans change, decisions get made. Then someone else opens the task and needs to figure out why things were done that way.

We use Cursor at Vaiz in two ways: a developer works through a task in the editor, or we hand a specific piece of work to a cloud agent. Thought I'd share two examples of how that actually works.

A bit of setup first. Through MCP, Cursor can access tasks, projects and docs in Vaiz. You connect the remote server, sign in through your browser and authorize access to your workspace. After that, you can reference a task by its ID instead of copying the description into every new chat.

Then comes the interesting bit: checking what the agent thinks you've asked it to do.

We had an epic for creating and configuring projects and boards through our built-in AI chat. The original plan also included a setup wizard, but we only wanted the chat functionality in that task. We cleared that up before implementation. The discussion also brought up how to apply multiple changes to a board: one at a time, or in a single transaction so a failure wouldn't leave it half updated.

That's a useful point to stop and talk things through with the agent. Ask what it's planning to change, what assumptions it's making and what still needs a decision. I think this is especially useful when several people have contributed to the task description and each has a slightly different idea of what “done” looks like.

We put that context back into the task. That epic ended up with six Memory entries: three decisions, a problem, a risk and a session log. They covered things like validation decisions, the risk of board templates drifting away from validation rules, and a log of a 50-case test run with the bugs found during it.

The second example was more straightforward: some changes to our website's pricing page. We handed the task to a Cursor cloud agent, specifying the repo, starting branch and permission to update the task.

In that run, the agent opened a PR, added a summary of the changes and checks it had run, and moved the task to Review. A person then shared a preview and tagged a teammate to take a look. The GitHub integration linked the branch, commit and PR back to the task, alongside the agent run and session log.

The main question for me is what the next person needs when they pick this up. A developer might need the reasoning behind a technical decision. Marketing might need a preview and a clear idea of what's ready to check. A PM might need the unresolved questions. A huge chat transcript probably isn't equally useful to all three.

So I'm curious how people are handling this:

* Where do you keep decisions from agent conversations: tasks, repo files or PRs?
* Do you ask the agent to record them as it goes, or write a summary at the end?
* Who checks whether that summary is actually accurate?
* What happens to old decisions when the requirements change?
* And does anyone besides the person who ran the agent actually use these notes?

We wrote up [these two examples with screenshots here](https://vaiz.com/blog/cursor-x-vaiz). Would love to see how others have set this up, especially when people outside engineering need to work with the agent's output.

How do you keep context from Cursor sessions accessible to the rest of your team?