Answers with Receipts: 5 Frameworks for Governed Multi-Agent Data Analysis

Dive into how organizations can safely scale multi-agent analytics without losing control or accuracy. This post outlines five essential frameworks that ensure strong governance—from semantic layers that enforce consistent metric definitions, to auditing structures that verify agent decisions, and policy controls that manage permissions and risk. Learn how these frameworks help balance autonomy & trust, reduce ambiguity, and make data analysis reliable and repeatable in enterprise contexts.

10 Battle Scars from Building Agentic AI Analytics

This blog dives into the practical challenges (“battle scars”) encountered while moving agentic AI analytics from demo to production. Covering real-world engineering and product lessons, it highlights pitfalls like ambiguous language, mismatched definitions, performance tail‑latency, lack of observability, and unreliable multi‑step logic. For each scar, it provides strategies—governed semantics, demander/validator separation, deterministic planning, explicit feedback loops, and transparency—that help build robust, trustworthy analytics agents at enterprise scale. If you’re rolling out AI analytics agents, these lessons can help you avoid common traps and ship with confidence.

Beyond Toy Demos: How Tellius Makes AI Analytics Work at Enterprise Scale

Many AI-analytics demos promise magic: impressive visuals, slick dashboards, and smooth interactions. But in real business settings, those polished demos often break down. This post pulls back the curtain and explains what it really takes to make AI analytics work at enterprise scale. You'll learn about: Building agentic architecture that supports complex queries and multi-step insights Ensuring reliability with intent parsing, root cause detection, and strong validation layers Managing data quality, security, and governance so insights can be trusted Scaling models, visualizations, and workflows to support thousands of users and billions of rows of data If you're evaluating AI analytics tools or planning your own build, this is your guide to separating real capabilities from toy demos and focusing on what drives measurable performance.