Robert Ta presents the Clarity Harness, a framework that gives product managers personalized AI agents for interviews, requirement synthesis, and user story writing. Each artifact traces back to its source transcript, and feedback on any piece can regenerate downstream work, keeping humans in control of the loop.
So if you're a product manager, this is how we're evolving the work. Let me show you our Clarity Harness. The thinking here is that each person has their own harness. You can have one for a specific domain, and then you can have harnesses at the department or company level. If I'm a product manager, I might have a story writer agent. At the top, there's a Clarity Orchestrator. I've got an interview agent capturing the input and a requirement synthesizer working through it. Each of these has a part in getting from the conversation to something I can actually use. I can open this up and see where the work came from. Okay, this was from this transcript. The story writer worked with the requirement synthesizer to pull out the requirements and write them as a story. I can actually follow that chain instead of just being handed a finished document. Then I'm the judge. This was quoted from here. Here's the transcript. What did we add? What changed from the previous version? And what does the full artifact look like? I can look at the requirement and the evidence together and decide whether it makes sense. Cool, I can approve it. I can annotate it. I can say, this requirement is too narrow, let's make it broader. I save that feedback in the harness, and then I can regenerate the work. So that judgment becomes part of the process. If you're using Claude Code, it's going to spin up subagents. If you're using your own agents, you can point them to a skill. The orchestrator calls the agent, the agent calls the skill, that kind of thing. And you're still in the flow of your work, writing stories, making prototypes. You've got agents doing things alongside you, but now you can look at those runs and ask, Did the input I gave it produce the output I expected? What happened along the way? And how do I make that better next time?
