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AI agents shouldn’t exist just because AI is trending.
They should exist because a business owner has a real problem that needs solving.
A customer calls after business hours.
Nobody answers.
The customer moves on to a competitor.
A lead comes in through a website.
Nobody follows up quickly.
The lead goes cold.
A team spends hours every week copying information between tools.
That time could have been spent growing the business.
This is where I’m focusing my work:
Building AI agents that solve real operational problems for businesses.
For example, an AI receptionist can:
→ Answer calls 24/7
→ Handle common customer questions
→ Qualify leads
→ Book appointments directly into a calendar
→ Transfer important calls to a human
→ Send follow-ups and call summaries automatically
The goal isn’t to replace people.
It’s to remove repetitive work and make sure valuable customers don’t get lost because nobody was available at the right moment.
I’m currently building and testing AI agent systems for businesses, with a focus on practical automation, measurable outcomes, and real-world workflows.
If you’re a business owner dealing with missed calls, repetitive tasks, or slow lead follow-ups, I’d be interested in hearing about the problem you’re dealing with.
Tell me the problem. I’ll figure out whether an AI agent can actually solve it.
Businesses don’t need more AI.
They need fewer missed opportunities.
Missed calls. Slow follow-ups. Repetitive admin work. Lost leads.
Those are problems worth building AI agents for.
how
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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.
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? 👀
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 agent’s work easier to inspect. I’d lean toward light mode for reading the table in daylight. How do you distinguish a verified source from one that still needs review?