The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
A booking is not an island.
A nervous pup, a customer, a changed appointment, a reminder—these shouldn't become disconnected pieces of information.
This 54-second walkthrough shows the knowledge graph in The Wagging Wagon. Follow Teddy from his booking to his saved note, then inspect a demo agent journey: what it noticed, what changed and whether owner approval was needed.
Recorded actions and inferred relationships are labeled separately. That distinction matters when a person is trusting an automated workflow.
I'm Olanrewaju Agaun, founder of Ubunex. I enjoy building systems whose behavior people can understand—not just interfaces they can admire.
Explore the app: https://wagging-wagon.lovable.app/
Looking for a developer who cares about both product design and AI workflows? Message me here on Contra.
The Wagging Wagon was built with Lovable. Fictional demo data, seeded agent replay and simulated SMS.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
TOP!!
I’ve 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 we’d 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 stage’s 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.
It’s open source and still a work in progress. I’m 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 hasn’t been tested across every CLI or version.
If you try it, I’d be interested in hearing what works and where it gets in your way.
Did a motion for the Cockpit concept ⁕ 🔈 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:
👉 Autonomy
👉 Observability
👉 Human Approvals
👉 Context
👉 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.