The Agentic Design System
How I use AI agents to research, draft, QA, and document learning — without handing over the judgment.
The Dream Team — 8 AI agents I orchestrate across the pipeline below.
The bottleneck isn't the idea — it's everything around it.
Good scenario design is slow, and the slow part usually isn't the concept. It's the surrounding work: gathering and checking research, drafting variations, catching inconsistencies across branches, and keeping documentation current.
Done by hand, that work is real and unavoidable — and it crowds out the actual design thinking. The hours go to upkeep instead of to the judgment calls that decide whether a scenario lands.
A repeatable pipeline, not one prompt at a time.
Instead of using AI a prompt at a time, I run a pipeline: research → draft → build-support → QA → document. Each stage hands off clearly to the next, and I stay the decision-maker at every gate.
The agents do the legwork; I own the calls.
Where the pipeline shows up in the real work.
AI media pipeline
I generate image and video assets for learning content with tools like Higgsfield. Production doesn't stall waiting on stock or a designer to free up.
POC design + audit
I rapidly design learning proofs-of-concept. Each one is audited against my own standards before it goes any further.
Canonical source + rules database
A single source of truth holds every project's standards and decisions. It keeps the work consistent and current across projects.
Knowledge base (Obsidian)
A research repository that feeds design directly. Findings live in one place instead of scattered across notes.
Multi-tool build
I built the flagship in both Storyline 360 and Rise 360. Each tool was chosen for the job it did best rather than out of habit.
Learner pledge → shareable post
I built a flow that turns the pledge a learner writes at the end into a share-ready post, so the commitment continues beyond the screen. Public commitment is a known behavior-change lever — it's instructional design, not vanity.
Human judgment leads; AI leverages.
The agents never decide what's true, what's ethical, or what a learner should feel. They compress the busywork so I spend more time on the parts only a designer should own.
I'm transparent about where AI helps, because that's where the ethics live. Naming the boundary is part of the design — not a footnote to it.
The question every hiring team is asking.
Hiring teams keep asking one thing: how do you actually implement AI in a learning workflow, beyond a chatbot? This system is my answer, in practice.
Every asset, POC, and standard on this page came out of this pipeline — in a workflow a team could adopt, with the judgment kept where it belongs.
AI media pipeline. Generated image and video assets for learning scenes, produced on demand. Production never stalls waiting on stock or a designer to free up.
POC audit / QA gate. An agent panel votes on each design decision before it ships — approve, revise, or block. Every REVISE comes with concrete action tasks, not vague notes.
Canonical source + rules database. One living record of the standards every project is checked against. Decisions get made once, then enforced everywhere.
The flagship this system produced: First72Hours.
First72Hours — a scenario-based simulation for new dog adopters, built end-to-end with this pipeline.