Data topics

AI agents and workflow automation are often treated as competing approaches, but the real power emerges when they work together. As agents evolve from simple chatbots to autonomous decision-makers and automation platforms become more intelligent and flexible, organizations can combine both to achieve higher reliability, faster execution, and deeper insights. This post breaks down the agent and automation spectrums, explains why Mode 1 (Execute) and Mode 2 (Explore) are both essential, and shows how their convergence enables smarter, safer, and more scalable AI-driven operations.

In today’s data-rich world, asking questions is no longer enough—what matters most is taking action. This blog introduces Tellius Agent Mode, a transformative leap into agentic analytics where AI agents don’t just answer queries—they plan, analyze, explain, and act. You’ll learn how Tellius marries a semantic layer, multi-agent orchestration, and business context to create an analytics ecosystem that moves beyond dashboards. With Agent Mode, you can automate multi-step workflows like root-cause detection, forecasting, and scenario planning—all through conversational goals. The result? Analysts elevate their focus to strategy while operational teams get trusted insights when they need them. Whether you're scaling analytics across sales, finance, or supply chain, this blog shows why Agent Mode is the architecture ready for enterprise intelligence at the speed of decision.

This guide lays out a complete picture of where augmented analytics is headed in 2025. It goes beyond buzzwords to explain how AI agents, conversational interfaces, generative insights, and governed semantic layers combine to deliver more than just “what happened”—they show why things change and what to do next. Expect deep dives into agentic workflows, scalability, governance challenges, and real use cases across industries. Whether you’re redesigning your data stack or evaluating new analytics tools, this is your roadmap to making augmented analytics work in practice.

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.

As AI agents take over routine data tasks, the traditional analyst role isn’t disappearing—it’s evolving. In the “agentic era,” analysts transition from data cleaners and report builders into orchestrators who guide AI workflows, interpret results, and embed insights into business decisions. This blog explains the mechanics of agentic analytics, explores how AI handles repetitive work, and defines the new skills needed—like prompt engineering, bias review, and ethical oversight—to thrive alongside autonomous systems.

The 2025 Gartner Magic Quadrant is out—but the real story goes beyond the dots. Discover what it reveals (and misses) about the future of AI analytics and why Tellius is built for what’s next: agentic intelligence, faster answers, and smarter decisions.

Tellius has been named a Visionary for the 4th year in a row in the 2025 Gartner® Magic Quadrant™ for Analytics & BI Platforms. Why? Because we’re not just stopping at dashboards. We’re moving the industry forward with: - GenAI-powered conversational analytics - Agentic flows that connect insight to action - An end-to-end platform built for enterprise-grade governance The future of business intelligence isn’t just pretty charts. It’s intelligent decisions—made faster.

LLMs made it easier to talk to machines. But the next wave of value will come from machines that think with us, not just talk to us. That’s the future of Conversational AI—systems that deliver answers, actions, and impact. And it’s already here with platforms like Tellius. If you're done with dashboards that require you to swim through lots of graphics for answer, and copilots that need hand-holding, it’s time to meet your first agent.

Traditional self-service BI tools promised independence but often left users dependent on analysts and stuck with complex interfaces. AI agents are now transforming the analytics landscape by managing complexity, automating workflows, and reasoning behind the scenes. Paired with a semantic knowledge layer, these agents deliver accurate, business-aware insights. With Tellius, users don’t need to learn analytics—they just ask questions and get answers. True self-service has finally arrived.

The next era of analytics is goal-driven, adaptive, and intelligent. Tellius brings this to life by combining deep analytics with conversational intelligence, multi-agent reasoning, and business-aware feedback loops. It’s not about replacing analysts—it’s about scaling intelligence across your business with a system that gets smarter every day.

AI analytics has evolved from dashboards and copilot tools to agentic intelligence Business users now get autonomous recommendations, not just answers Platforms like Tellius combine conversational AI, agent orchestration, and memory The result: faster insights, better decisions, and less dependency on data teams. In 2025, AI analytics isn’t a feature. It’s a fundamental shift in how decisions get made.

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.