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๐๐ ๐๐ด๐ฒ๐ป๐๐ ๐ถ๐ป ๐๐น๐๐๐๐ฒ๐ฟ๐๐น๐ผ๐ ๐ฎ๐ฟ๐ฒ ๐พ๐๐ถ๐ฒ๐๐น๐ ๐ฐ๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด ๐ต๐ผ๐ ๐ฎ๐ฝ๐ฝ๐ ๐ฎ๐ฟ๐ฒ ๐ฏ๐๐ถ๐น๐
Many teams already use FlutterFlow for speed and flexibility.
Whatโs exciting is how AI agents now fit naturally into that flow.
Instead of adding heavy logic or complex workflows, teams can design agents that respond, guide, and support users inside the app experience itself.
This opens up new possibilities for:
โข smarter user interactions
โข faster iterations
โข practical automation inside real products
Simple ideas. Real outcomes.
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Iโve been working on a small skill called Manual Orchestrator to make longer projects with AI agents easier to manage.
The problem started with a familiar situation: one chat would accumulate plans, implementation details, corrections, and test results. By the time I was ready for the next phase, I wanted a fresh conversation without losing the decisions weโd already made.
Manual Orchestrator gives that process some structure. One chat acts as the orchestrator: it breaks the work into stages and writes a prompt for the next one. I run that prompt in a new session, then bring the result back. The orchestrator reviews it against the stageโs criteria before preparing the next prompt. If something needs fixing, I go back to the same worker session.
The skill also keeps a record in the project with the current stage, decisions, evidence, and next steps. That makes it easier to pause, resume, or hand the work to another session.
The handoff is manual. I choose when to open a session and carry the result back. The orchestrator and worker sessions can also use different agent CLIs, with the prompt adapted to the capabilities available.
Itโs open source and still a work in progress. Iโm using it to learn what helps in practice and where the workflow needs to improve. Installation has been checked for Codex, Claude Code, and Gemini CLI, but the full workflow hasnโt been tested across every CLI or version.
If you try it, Iโd be interested in hearing what works and where it gets in your way.
Did a motion for the Cockpit concept โ ๐ ON
Design & Creative Direction: Me
Motion & Sound: Claude Code
Cockpit is an AI agent management platform concept that helps teams:
- Set boundaries
- Monitor activity
- Manage approvals
- Track knowledge sources
- Measure performance and costs
It brings together five key AI UX challenges:
๐ Autonomy
๐ Observability
๐ Human Approvals
๐ Context
๐ Analytics
The idea is simple:
As AI agents become more capable, the experiences we design around them need to make their actions more transparent, predictable, and manageable.
Thanks!
Human approvals is the part most agent UIs skip. Good call making it one of the five challenges.
Designed an AI research workspace where the agent plans the work and fills a sheet with cited sources as it goes.
Same screen, two modes. Which one would you work in all day? ๐
13 voted
72%
5 voted
28%
18 votes
Closed
Keeping the research plan and cited results side by side is a useful way to make the agentโs work easier to inspect. Iโd lean toward light mode for reading the table in daylight. How do you distinguish a verified source from one that still needs review?