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.

Dive into the essential ingredients for building a genuinely enterprise-ready agentic analytics platform. You’ll learn why AI agents alone aren’t enough—why convergence with a robust semantic layer, rigorous governance, scalable compute, and clear explainability matters. Explore how Tellius’s architecture weaves together conversational AI, domain-aware knowledge models, multi-agent orchestration, and trust-first design to enable autonomous analytics workflows that deliver accurate insights and measurable business outcomes at scale.

AI agents aren’t just hype—they’re the engine behind the next generation of self-service analytics. This post breaks down how agentic AI enables multi-step, contextual analysis workflows that automate the grunt work of business intelligence. Learn what makes agentic systems different, what to look for in a true AI-native platform, and how Tellius is pioneering this transformation.