But here's the honest part, building agents is hard, expensive and most of the scaffolding we make would be obsolete with a new labs release. at the same time models are not quite there and they are not keeping up with "coding is dead" and "knowledge workers are doomed". Pushing them beyond what they can actually do reliably just boils the ocean. And we don't need to.
Staying in sync with Labs' release pace is already super mega photonic speed. So to sum up: we need to use what they are capable of, keep our options open and quickly improve our agents. How to do it w/o a dedicated person for AI innovation?
That problem is what we kept running into at Agentplace.
We wanted a builder that handles the full stack for an internal agent: backend, database, any MCP-ish integrations, and a real custom UI.
A side note: The zero-UI autonomous agent dream is completely oversold. The communication loop between models and humans is still tight, and a purpose-built interface that fits that loop makes agents dramatically more reliable in practice. Custom UI is underrated.
The other thing we learned is that you only understand what an agent is missing once you're actually working with it. So we built two modes: Work mode, where you and your team use the agent in real workflows, and Edit mode, which you can jump into the moment something breaks or a better model ships. This has been our most impactful design decision. You spot the gap during actual work, fix it in minutes, and you're back. No ticket, no dev request, no deploy cycle. That unlocks something real for small teams.
We've built this system and it was a productivity unlocker for everyone. Everyone on the team managed to build exactly the agent they wanted using our AI builder and start using it immediately. Over the weeks the agents really grow into complex automation tools but adding what's missing during the work. That said, a lot of "cool agent ideas" died like in an evolutionary process. Useful ones kept growing while less frequent ones just stayed at the end of the agent list.
We took it further, we wanted to give our clients an agent to gather requirements. These requirements our team then queries using their own agents. So we added a public publish option. Client-facing and internal workflows can share the same database, foundation but with diff permissions.
We can share agents with specific teammates and deliver them anywhere: web, Claude Code, Cursor, ChatGPT, or as a tool called by other agents.
We use Agentplace for our own startup every day. Every internal automation we rely on lives here. We're giving any early-stage team $1k in credits to start building. It costs us real money, so no public link. If you're genuinely building, DM me on X: @fortune_vy
become a real 20x company!