SEZ9 commented on issue #10651:
URL: https://github.com/apache/seatunnel/issues/10651#issuecomment-4147761410
Thanks @yzeng1618 for the very detailed explanation of the engineering
workflow. Let's THINK BIG!
We can certainly leverage community-shared knowledge to transform
AI-generated configurations into a knowledge base ready for production
deployment.
Following the discussion in #10651 and PR #10585, I'd like to propose a more
concrete phased approach for AI-assisted config generation, inspired by two
key concepts:
1. **SeaTunnel Skill** — Claude Code AI integration is already available in
[seatunnel-tools](https://seatunnel.apache.org/docs/tools/seatunnel-skill/),
which encapsulates SeaTunnel-specific knowledge and operations as AI
capability modules.
2. **Harness Engineering** — A methodology from
[ArtemisAI/Harness_Engineering](https://github.com/ArtemisAI/Harness_Engineering)
for coordinating AI agents with shared memory, structured specs, and
feedback loops. Core insight:
> "The best way to use AI agents isn't to make them smarter — it's to
> give them better tools, clearer specs, and a shared memory."
I believe combining these two concepts with SeaTunnel's existing
infrastructure
can create a powerful, community-driven AI config generation system.
Phase 1: Config Format Generation & Validation (Skill as Hands 🖐️)
AI can generate syntactically correct, constraint-compliant HOCON
configs and validate them automatically.
Phase 2: Community Production Recipes (Harness as Memory 🧠)
Build a shared, structured knowledge base of production-proven
configs that AI can retrieve and learn from.
Phase 3: AI-Powered Validation & Continuous Learning (Harness as Feedback
Loop 🔄)
AI not only generates configs but also validates them against production
patterns, and the system continuously improves.
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