Frame the real bottleneck
See why human repair loops, not model quality, still block modern data pipelines.
Understand the bounded mutation contract
Learn the core shape: one editable genome, one fixed judge, and one visible artifact trail.
Understand the control loop
See how the orchestrator turns one failure into the next bounded repair attempt without letting the model own correctness.
This site now publishes the complete seven-lesson Self-Evolving Data Engineer course. The series frames the business problem, defines the mutation contract, locks the exact pipeline genome, shows how the orchestrator controls one bounded repair loop, makes that loop observable, raises pressure with a fixed judge and smarter challengers, adds best-of-N reranking before commit, and closes with production safety.
The scope stays narrow on purpose: one mutable surface, one fixed judge, one repeatable control path, one readable feedback surface, and one safety ladder for containment, reset, and trust. That keeps the public course auditable from Lesson 01 through Lesson 07 instead of widening into a vague platform story.
1What is live right now?
2What comes next?
Prerequisites
What you need before starting
Data platform engineers
You want a safer pattern for introducing AI into brittle data-cleaning and normalization pipelines.
Agent builders
You want to see how AutoGen fits into a bounded engineering loop without letting the model redefine correctness.
7 lessons · 58 mins
Each lesson builds on the previous one — follow them in order for the best experience.
The Mutation Engine
Defining the Pipeline Genome
The Orchestrator
Observability & The Feedback Signal
The Judge & Self-Challenging Loops
Test-Time Reranking
Conclusion & Production Safety
