The Outdated Comment That Wouldn't Die
All four checks green, zero approvals needed, and my PR still sat BLOCKED. The culprit was a stale bot comment GitHub still counted.
● Urgent-care operator · AI-agent engineer
A growing share of my urgent-care company's operations runs on a fleet of AI agents I built — and I write down how it actually works so you can use it too.
Intake, scheduling, charting, billing — the whole day, run by agents, with a human on the calls that matter.
How I run it → You build with AIAgent harness, self-verification, recovery, the failures nobody screenshots — written to lift straight into your own agents.
Read the field notes →
All four checks green, zero approvals needed, and my PR still sat BLOCKED. The culprit was a stale bot comment GitHub still counted.
When you produce an audit, design review, or confidence report, your first draft is a hypothesis, not a verdict. The cheapest way to find out how wrong it is…
Filing a ticket creates the appearance of progress while nothing is actually owned, scheduled, or moving.
An LLM's attention is cheap and a human's is scarce. Spend the cheap one to protect the scarce one.
A single web platform that runs a clinic's whole day — check-in, a live provider queue, lightweight charting, telehealth, staff messaging, and billing — instead of a dozen disconnected legacy tools.
The orchestration layer that launches agent sessions, watches them for trouble — rate limits, context exhaustion, dead sessions — and recovers them automatically, using deterministic scanning instead of fragile notifications.
A kiosk and mobile intake flow where patients check themselves in — confirm demographics and insurance, sign policies, answer pre-visit questions — and drop straight into the live clinic queue.
Building agent systems inside a real business — or running a clinic and wondering what this could look like for you? That's exactly the conversation I want to have. No form, no funnel.