Answer analytics questions with a governed multi-agent assistant: orchestrate retrieval, analysis and reporting on approved metrics only, under each user's own permissions: AI agent prompt

phData's Nov 2026 case study describes a two-person pod that put a governed multi-agent analytics assistant into production in 10 weeks for a $9B+ enterprise software company (~21,000 employees, ~150 certified data products). A Claude Opus orchestrator splits each natural-language question across specialized sub-agents on Strands and Amazon Bedrock AgentCore (Sonnet for heavy reasoning, Haiku for faster analysis). Answers come only from governed Snowflake semantic views and Atlan metadata via MCP—not free-form SQL on raw tables—with Okta user-scoped tokens and read-only access. A Golden Query Bank of 100+ verified queries gates releases (~93% governed-metric grounding vs a 90% threshold). Result: analysis turnaround fell from about 1 hour to about 15 minutes (75%).

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ResearchGoverned multi-agent analytics on semantic metricsadvanced

The prompt

Design a governed multi-agent analytics assistant for [team]. Question types to start: [e.g. CX / sales funnel / product usage]. Data stack: [Snowflake semantic views, Atlan, Okta, warehouse]. Models: [Claude via Bedrock or similar]. Hard rules: no free-form SQL on raw tables; every metric must resolve to a governed definition; read-only; queries run as the requesting user; ship only if eval accuracy ≥ [90%]. 1) Map orchestrator + sub-agents (retrieve, analyze, chart/report, insight). 2) List MCP tools and permission boundaries. 3) Sketch a 20-query Golden Query Bank and pass/fail rubric. 4) Draft a 10-week build plan with the go-live accuracy gate. Do not run live warehouse queries here.

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Works with: Claude, Amazon Bedrock AgentCore, Strands Agents, Snowflake, Atlan, Okta

Real-world example: phdata.io/case-studies/governed-multi-agent-analytics-assistant/

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Do not run live warehouse queries here.

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<blockquote style="border:1px solid #e4e4ee;border-left:4px solid #0f6b50;border-radius:10px;padding:14px 16px;margin:16px 0;font-family:system-ui,sans-serif;max-width:640px"><p style="margin:0 0 6px;font-weight:700">Answer analytics questions with a governed multi-agent assistant: orchestrate retrieval, analysis and reporting on approved metrics only, under each user&#x27;s own permissions</p><p style="margin:0 0 10px;color:#5c6078;font-size:14px">phData&#x27;s Nov 2026 case study describes a two-person pod that put a governed multi-agent analytics assistant into production in 10 weeks for a $9B+ enterprise software company (~21,000 employees, ~150 certified data products). A Claude Opus orchestrator splits each natural-language question across specialized sub-agents on Strands and Amazon Bedrock AgentCore (Sonnet for heavy reasoning, Haiku for faster analysis). Answers come only from governed Snowflake semantic views and Atlan metadata via MCP—not free-form SQL on raw tables—with Okta user-scoped tokens and read-only access. A Golden Query Bank of 100+ verified queries gates releases (~93% governed-metric grounding vs a 90% threshold). Result: analysis turnaround fell from about 1 hour to about 15 minutes (75%).</p><a href="https://agentic-library.netlify.app/prompt/al-695-answer-analytics-questions-with-a-governed-multi-agent-assis/?utm_source=embed&amp;utm_medium=referral&amp;utm_campaign=prompt_embed" style="color:#0f6b50;font-weight:600">Get the free AI prompt on AgentPlaybook Real Estate →</a></blockquote>

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