AI product workspace that tries to map product reality. What would you want it to understand?
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We’re building Athena as a system that tries to connect product intent with technical reality using AI subagents.
Instead of just managing tasks, it tries to understand how the product actually behaves - architecture, constraints, and decision history.
Before we go deeper:
If you could give an AI workspace one ability related to product understanding, what would it be?
Would love to hear different perspectives from PMs, engineers, and founders.
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Mapping product reality beyond just task management is a bold move. I'm particularly interested in how Athena handles the technical debt vs. new decision history, can the AI subagents actually audit existing codebases to align them with the product intent? This would solve a massive context gap for engineering teams. Great vision Maya!
Athena
@emre_yilmaz_easyparser Thanks, glad you liked the topic!
On your question - Athena’s subagents don’t just “audit” codebases, they build an understanding of how the system behaves and connect it to product intent.
The key part is the feedback loop - Athena learns from user input and decision history over time, so it gets better at understanding what the product is actually trying to achieve.
That’s what allows it to surface real misalignments between the code and the intent, instead of just pointing out generic issues.