Building a Self-Evolving Data Engineer — 7 Lessons from the CleanLoop
A seven-part map of the CleanLoop architecture, from bounded mutation and genome design to observability, reranking, and production safety.
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AI Systems · Data Workflows
Posts in Agentic Data Engineering
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Production Safety: Why Sandboxing, Trust, and Reset Are Non-Negotiable
Why sandboxing, trust ladders, reset controls, and tripwires are mandatory before self-rewriting code touches production.
Test-Time Search & Re-Ranking: Why One Candidate Is Usually Not Enough
Why best-of-N search outperforms one-shot mutation proposals when the search space is wide and the judge is stable.
The Judge & Self-Challenging Loops: Why Harder Data Beats Easier Grading
Why a fixed judge needs a separate challenger that makes the data harder without moving the goalposts.
Observability & The Feedback Signal: Why Your Loop Needs External Memory
Why durable artifacts, row-level traces, and operator-facing dashboards turn mutation loops into auditable systems.
The Orchestrator: Why Your Loop Needs a Control Shell Around the LLM
Why the orchestrator must read evidence, request one bounded mutation, verify it, and revert failure cleanly.
The Genome: Why Your Pipeline Needs a Bounded Mutation Surface
Why self-improving systems need one mutable genome, one fixed judge, and clean rollback boundaries.
The Mutation Engine: Why Your Pipeline Needs a Bounded Contract Before the First Mutation Runs
Why bounded mutation contracts matter before the first AI-generated repair attempt touches your pipeline.
CleanLoop: A Bounded Mutation Loop for Self-Improving Data Pipelines
A repo-first walkthrough of CleanLoop, the bounded mutation loop that repairs finance pipelines with one mutable genome and one fixed judge.
Building a Self-Evolving Data Engineer — 7 Lessons from the CleanLoop
A seven-part map of the CleanLoop architecture, from bounded mutation and genome design to observability, reranking, and production safety.