Athsara Fernando

This is solid 🔥, good to see SL builders tackling the “actual problems” layer instead of just wrappers

How do you decide which Slack discussions actually become enforceable rules without creating noise?

Something about this is just fun to open and check, nicely done

If a product gets reformulated or varies by region, how do you keep the data accurate over time?

Trying to understand the real output quality here, how interesting are these agent conversations to actually listen to over time?

If I iterate multiple times on the same project, does it maintain consistency or drift in design decisions?

What happens when the AI misunderstands intent, is there a quick way to correct without breaking flow?

When AI suggests references, what’s your approach to ensuring they’re not hallucinated or slightly off?

Feels like a great first experience, what have you found keeps users engaged beyond the first few weeks?

Can Agentplace plug into existing pipelines without restructuring, or do teams inevitably have to adapt their stack?