agentic analytics
For years the playground stopped at BI dashboards and SQL pipelines I could reach from Tableau and MicroStrategy. Agents removed that ceiling. This hub shares build-time and run-time experiments across the analytics cycle — from dbt pipelines and context layers to what lands on the viewport.
Core systems
The load-bearing pieces of the stack — what the rest of the lab runs on.
Run-time
What stakeholders and readers see — data stories, dashboards, tours, and operator surfaces on the viewport.
People tend to focus on the final polish step. The run-time wing is the full path from dataset to analysis to data storytelling: turning accumulated visualization expertise into decision trees an LLM can follow, with a human still making the taste calls.
- 01dataset
- 02ensemble
- 03beats
- 04chart
- 05polish
Dashboards & instruments
Not every useful artifact is a public data story. Some are tools for seeing, steering, or physically correcting the craft itself.
Build-time
Data conduits that make the viewport trustworthy — including context layers, agents, anti-hallucination resources, and community tooling.
Semantic layer & meta context
Open schemas and landscape work that teach agents to interpret metrics, not just query them.
Warehouse trust
Empirical grounding — query-log mining, join patterns, and knowledge graphs built from real warehouse work.
Domain agents
Builder, analytics manager, and ensemble workflows — including deep reads on how agent memory actually behaves.
Shareable workflows
Token-spread tricks and pipeline commands — some published, some still workspace-only.
Context Engineering for Analytics Engineers
article ↗The Substack piece that started the conversation — what happens when your semantic layer needs to explain itself to an LLM.
Adoption
Self-executing walkthroughs and shareable skill pages for onboarding others to the same toolchain.