Analytics agents built by data teams

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

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

Recommended first reads

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).

Internal builds

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.

Browse by problem

• Giving the agent context: Ramp, Gorgias, AngelList, BlaBlaCar, OpenAI.
• Testing answers and model changes: Gorgias, Anthropic, Ramp, LinkedIn, Uber.
• Fixing mistakes and keeping context current: GitHub, Gorgias, Replit.
• Reducing cost and latency: Gorgias, Vercel.
• Getting people to use it: Alan, Faire, Ramp, LinkedIn.

Related reading

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.

Suggest an article

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.