04 / Shop · Retrospective
Owl Park
storefront.
A live mock e-commerce store with three reactive agents. Now retired — the writeup is below, the play demo is at the bottom.
Retired · 2026
01 / What it was
A reactive market in miniature.
The Owl Park storefront was a real working store backed by Supabase. Real product rows, real prices, real inventory, real cart and checkout flow.
Behind it sat three n8n agents specialized to be different parts of a live ticket market. A pricing agent watched demand and adjusted prices inside fairness bounds. A restock agent pulled units from a virtual warehouse when stock crossed a threshold. A fair-buyer agent compared prices to nearby comps and bought when the market was favorable.
Every action a visitor took — adding to cart, completing a checkout, abandoning at promo entry — fed back into the agents' state. In other words, your decisions directly influenced the agents and pricing!
02 / How it performed
The agents found their rhythm.
The pricing agent settled into a reasonable diurnal pattern within 48 hours — leaning prices up during morning peaks, easing down in the late afternoon as ride passes saturated. Without any explicit time-of-day rules — it just noticed.
The restock agent was the boring one, in a good way. Threshold checks fired on schedule, replenishments completed cleanly, never a stockout during the run. It was the bottleneck in the end, not restocking often enough to keep up with a high demand market.
The fair-buyer was aggressive during this project. I found that I could manually set prices that slowed it down, but it appeared my pricing agent was aiming for higher volume over price per ticket.
— Project notes, day 9Cart abandonment on the promo-code field might be interesting to explore in the future.
03 / What I learned
Three things I uncovered.
Reading the data afterward in Power BI was the part I'd repeat first. Watching the agents in real time was fun, but the post-run dashboard — basket size, abandonment funnel, agent-driven price elasticity — is what made this project useful.
Agents Perform Better with Checks
The dynamic pricing workflow benefitted from having an agent that checked the fairness and range of the pricing. Without it the pricing was volatile and unbounded.
n8n workflows can trigger each other.
One of the major improvements that was made during this project, was realizing the restocking agent couldn't keep up with demand on a schedule. By having it set to trigger anytime the consumer makes a purchase, it was able to monitor stock more closely without eating unnecessary n8n tokens.
System Oriented Thinking
This was a complete system, from the user to an OLTP database in supabase, to the OLAP warehouse in Fabric, and finally to the analytics in Power BI.
Play demo
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03 / Pipeline
How the agents were wired
Four-phase Supabase → Fabric → Power BI architecture behind everything you just read.
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05 / Resume
Resume + ATS
PDF preview, downloadable copy, and an ATS plain-text version.
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06 / Feedback
Tell me what landed
A quick chip selector + the real form. Closes the loop.
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