Ivo Tzanev

The "mission control" framing is exactly right for this problem. Once you move past single-agent experiments into multi-agent workflows, the operational overhead becomes the real bottleneck — not the agent logic itself. Most teams are cobbling together logs and dashboards from separate tools, which defeats the purpose of automation. > > How does AgentCenter handle runs where an agent spawns...

This is incredibly useful. When I was preparing our pitch deck, I spent days searching for real examples from companies at a similar stage. Having them in one place saves so much time. The ones I always learned the most from were the messy early-stage decks — not the polished Series B ones. Are you planning to tag them by stage so founders can filter?

The context engineering angle is what sets this apart from every other AI recorder. Most tools just transcribe everything — but knowing what the person found important during the conversation changes the output completely. As someone who takes a lot of founder calls and investor meetings, the signal-to-noise problem in notes is real. Smart approach.

The 30-page business plan in a couple of hours claim got my attention. I've written too many of those manually and it never gets easier. What I'm really curious about — does the AI challenge your assumptions or mostly structure what you feed it? Because the hard part of planning isn't the writing, it's figuring out where your thinking is wrong.

We're raising right now and the part that kills me is exactly what you described — you send the deck and then just... wait. Zero visibility into what landed and what didn't. The engagement insights before the first call could completely change how you prepare. How granular does the tracking get — can you see time spent per slide?