Pathway 02 / Orchestrate
Agentic AI Automation Engineer
Go deeper on tools, queues, approvals, and supervised process automation.
Explore agentic automation →Career pathway / Application engineering
Build AI features people can use, evaluate, and trust—from a first product brief to a production-ready domain assistant.

The study plan
Learn to choose a useful AI surface before choosing a model.
Deliverable: an application brief with user need, workflow, risk notes, and measurable success criteria.
Turn model calls into predictable, versioned application components.
Deliverable: a reliable extraction service with typed outputs and failure cases documented.
Give a model the right context and make its evidence inspectable.
Deliverable: a cited knowledge assistant with a small evaluation set and freshness note.
Replace vibes with a repeatable way to see whether the application is improving.
Deliverable: an evaluation dashboard and release gate for a defined quality bar.
Make an AI feature feel coherent, accessible, and honest inside a product.
Deliverable: a polished multi-turn web app with a short product decision log.
Prepare a useful system for real constraints without pretending risk is gone.
Capstone: a production-ready domain assistant with evaluation gate, runbook, and portfolio case study.
By the end
You will be able to scope an AI feature, choose an appropriate pattern, test it against a defined quality bar, and explain the tradeoffs in a way a product or engineering team can use.
Write a brief that connects user need, context, workflow, and model behavior.
Build an evaluation set and use traces to find quality, latency, and cost regressions.
Protect secrets, expose only deliberate tools, and communicate uncertainty to users.
Package the capstone as a case study another engineer can inspect and extend.
“I stopped describing AI as magic and started describing the decisions around it.”
Mentor profile
Former applied AI lead focused on evaluation, human-centered product design, and the translation from prototype to durable service.
Before you begin
No. The pathway is for builders who can work comfortably in Python or JavaScript and want to learn the application patterns around modern models. It does not assume advanced mathematics.
A completed capstone, supporting module artifacts, and a written case study. The certificate recognizes pathway completion; it does not represent a license or guarantee a job.
Related pathways
Pathway 02 / Orchestrate
Go deeper on tools, queues, approvals, and supervised process automation.
Explore agentic automation →Pathway 04 / Operate
Learn the release, reliability, and platform patterns behind durable AI services.
Explore MLOps →