Agentic Analytics Playbook
Learn how to choose your harness, build your context layer, plan your rollout, measure success, and get examples from 7 real-life companies.
How this playbook helps
Every data team wants to set up agentic analytics. But there is still no industry consensus on how to do it. Which tool to use? Build or buy? What context to give your agent? How to measure reliability? How to maintain it through time?
This guide takes you step by step through those decisions, with a complete context engineering strategy you can apply to your own stack. Learn how to build, test, and iterate on your agent's context layer - the part that actually makes analytics agents reliable.
The goal is not just production-level - it's trust-level. An agent your business team actually uses, that decreases your data team's workload for good.
What you get in this playbook
A step-by-step roadmap
From scoping your first use case to rolling out company-wide in 5 phases. Each phase has clear exit criteria so you know when to move forward.
A context engineering method
The 5-step iterative process to build, test, and improve your agent's context layer. The same method that took our agent from 17% to 86% accuracy.
A benchmark of 20+ tools
Every agentic analytics tool compared on reliability, cost, speed, and data team fit. From Snowflake Cortex and Databricks Genie to LangChain and nao.
7 real companies examples
OpenAI, Anthropic, Lyft, Gorgias, Ramp, Vercel, and nao. Their stack, their results, their key learnings.
Who this playbook is for
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53 pages - PDF - Free download