TRK-001In production
Doing More With Less in QA
Companion to: the entry course
The compression problem, leverage gap, Last 20 Bugs, regression drag audit, AI leverage vs theater, the 90-day plan.
- 6 modules
- ~3.5 hours
- 5 worksheets
- 1 capstone plan
Coming soon · Track 01 in production
Self-paced courses for QA leaders.
The same operating model Loop runs in private engagements, packaged as on-demand tracks you can take at your own pace. Track 01 ships first; the rest land in waves through 2026.
Catalog · 4 tracks planned
The four-track curriculum.
Every track is a companion to one of the published books. Take them in sequence or only the ones that match what you're doing this quarter.
TRK-002Drafting
Quality Engineering Operating Model
Companion to: AI-Native Quality Engineering
Layered tests, named owners, leverage metrics, and the 5-bucket maturity framework. Applied to your team.
- 8 modules
- ~5 hours
- Maturity assessment
- Adoption plan
TRK-003Planned
AI-Driven Test-Driven Development
Companion to: AI-Driven TDD
Tests define intent, AI accelerates implementation, engineers own verification. Translated to your stack.
- 7 modules
- ~4 hours
- Reference repo
- Pairing sessions
TRK-004Planned
Bespoke Agentic Pipelines
Companion to: Bespoke Agentic Pipelines
Roles, permissions, observability, project-specific operating rules. For teams running AI-generated code.
- 9 modules
- ~6 hours
- Pipeline starter kit
- Cohort review
While you wait
Three ways to start now.
Live · WS-001
Doing More With Less in QA. Public Course
Take the live cohort version now. Same curriculum, with the working sessions and Q&A.
See the syllabus →BooksThe bookshelf
Four published methodology books. Every course track is a companion to one of them.
Open the bookshelf →ResourcesTemplates and worksheets
The 90-day plan, the QA Leverage Scorecard, the Regression Audit. All free, all from the same operating model.
Browse the hub →Watch · Companion videos
Talks behind the courses
May 4, 2026
Inside a Real Agentic Pipeline (Step-by-Step Breakdown)
Agentic pipelines sound great in clean demos, but what do they actually look like in production? In this video, I break down one of the real AI development pipelines I use almost every day: how it starts from a prompt, creates its own branch and worktree, runs research, builds a plan, gets reviewed by a second agent, writes failing tests, implements until green, runs policy checks, and produces receipts at the end. I also cover what’s worked, what’s been over-engineered, where deterministic checks matter, and why “just run more agents in parallel” is not always the right answer. Sorry for the lower-energy video, I hadn’t eaten all day before recording this one 😅 Links: Newsletter: https://tinyideas.ai/#newsletters QA work at Loop: https://www.workwithloop.com/ LinkedIn: https://www.linkedin.com/in/ben-f-44778426/ X: https://x.com/FellowsBen
Watch on YouTube →
May 1, 2026
Are Agentic Pipelines Actually Worth It?
Are agentic pipelines actually worth the extra time, tokens, and complexity? My honest answer: it depends. Agentic pipelines can improve accuracy, visibility, governance, and control, but they also add real cost. They often take longer to run, use more tokens, introduce more orchestration, and create another layer of abstraction around your development process. So the question is not “do pipelines work?” The better question is: did this pipeline earn its cost? In this video, I walk through the framework I’m using to evaluate whether an agentic pipeline is actually worth running. That includes measuring the pipeline tax, tracking run receipts, comparing quality improvements, and using a ledger system to understand whether a pipeline is making the work better or just making it more complicated. I also share an example of a pipeline that looked good on paper but probably wasn’t worth it in practice. That’s an important part of the lesson: not every task needs a pipeline. Sometimes a single Claude Code or Codex session, guided by a strong engineer, is enough. The goal is to use pipelines surgically. Start simple. Measure what happens. Add complexity only when the pipeline is solving a real problem. And when a pipeline gets too large, use the data to make it smaller. If you’re experimenting with agentic development, this video is about how to think about ROI, accuracy, governance, and cost before building complex AI workflows everywhere.
Watch on YouTube →
Apr 30, 2026
Stop Doing AI “Factory Work” - Own Your Agentic Pipeline #agenticai #agentic #agenticcoding #coding
Stop treating AI like factory work. Rigid, assembly-line workflows break down with complex codebases. Owning your agentic pipeline means customizing every step and refreshing context each time—leading to better accuracy, flexibility, and scalability where it actually matters. #agenticai #agentic #agenticcoding #coding
Watch on YouTube →Common questions
Before you sign up.
- When does Track 01 open?
- Final cut is in production. Beta enrollment opens first to anyone on the notify list. We'll send a date as soon as we have one.
- Will the courses cost the same as the live workshop?
- The on-demand pricing will be lower than the live cohort. Live cohorts include the working sessions and Q&A. Courses are pure curriculum.
- Can my team buy seats together?
- Yes. Team-license pricing is on the roadmap. Email and we'll set up early access for your team.
- Will it be just video?
- Each module ships with the video, the worksheet templates, a printable summary, and a community thread for the cohort.