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A list of articles on how data teams built their analytics agents

A curated GitHub list from GetCassis compiles articles on how data teams built analytics agents, led by Anthropic's June 2026 post on enabling self-service data analytics with Claude, OpenAI's January 2026 writeup on its in-house data agent architecture, and Ramp's October 2025 guide to building an analyst agent on an existing dbt project. The collection organizes further reading notes by problem area — giving agents context, testing answers and model changes, fixing mistakes, reducing cost and latency, and driving adoption — and names Gorgias, AngelList, BlaBlaCar, LinkedIn, Uber, GitHub, Replit, Vercel, Alan, and Faire among the teams covered. Vendor implementations from Cube, Omni, and Cassis are also included.

read2 min views5 publishedSep 10, 2026
A list of articles on how data teams built their analytics agents
Image: Michielbdejong (auto-discovered)

A list of articles on how data teams built their analytics agents.

Recommended first reads · Internal builds · Browse by problem · Related reading · Contribute

  1. Anthropic — How a team makes self-service analytics work.Skills, metric definitions, documentation, and evaluations (Jun 2026), including how the team keeps them current as data models change.
  2. OpenAI — Inside an in-house data agent.The architecture behind context preparation, retrieval, permissions, and evaluations (Jan 2026).
  3. Ramp — Building one around an existing dbt project.SQL and Jinja examples for context tables and domain documentation (Oct 2025).

Read an article directly from the right column, or click a company for its full reading notes. Dates are publication dates; stacks reflect what the authors used at the time.

Product engineering, technical studies, and benchmarks that complement the internal builds.

Cube: Building an Agentic Analytics Harness ·Vendor implementation . Errors, result limits, and permission-aware tools.Stack and notes . #

Omni: Benchmarking Omni’s agentic analytics harness ·Vendor implementation . Testing quality, latency, and cost on a vendor’s analytics workload.Stack and notes . #

Cassis: A blank beats a guess ·Vendor implementation . Building the first context from existing data assets, with an open-source bootstrap kit.Stack and notes . #

Cassis: Context engineering for analytics agents ·Vendor implementation . How we structure tables, metrics, and business rules so an agent can find what it needs.Stack and notes . #

Benchouse: The Analytics Agent Benchmark ·Benchmark . Compare accuracy, completeness, restraint, and cost per question across analytics agents.Notes .

Know a team that has written up its internal build? Open an issue or pull request with the link and a sentence about what makes it useful. See the contribution guide for the format. Corrections and broken-link reports are welcome too.

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