Welcome to My AI Learning Blog
This is the first post on RZ AI Learning. I’ve spent years as a product data scientist, and I’m now transitioning into using AI for analytics. This blog documents what I’ve learned while building a multi-agent system for analytics.
What to Expect
The main series covers the end-to-end journey of building an AI agent team for analytics:
- Multi-Agent Systems — Architecture, agent roles, and the lifecycle from rough question to validated deliverable
- Harness Engineering — Hooks, warm-rules injection, spec placement, and why enforcement mechanism matters more than rule content
- Agent Discussion — Structured agent-to-agent debate as a quality layer for judgment-heavy rules
- Analytics Quality Improvement — Catching wrong data sources, flawed reasoning, and misleading patterns before they reach the final output
- Agent Performance & Stability — Compliance metrics, self-improvement loops, and lessons from real-world projects
Why This Blog
There’s no shortage of AI content on the internet, but I find that writing my own explanations is the best way to solidify understanding. If my notes help someone else along the way, even better.
What’s Next
The series is already underway — check out the first post to see the full architecture. More posts are coming on lessons learned and performance metrics.
Thanks for reading!