The Future of Data Analytics: 30+ AI Agents Working as One

Future AI systems will function as autonomous collaborators that can handle real-world tasks with minimal oversight. These AI agents will operate proactively as "super-competent colleagues," handling simple tasks immediately while drafting solutions for more complex ones, only involving humans when necessary. This represents a significant evolution beyond today's chatbot experiences toward truly autonomous assistance.

Rather than relying on dashboards or static interfaces, these AI systems will work across workflows and platforms—connecting data, analyzing patterns, and initiating actions.

An agentic AI system is built from multiple AI agents, each designed to perform specific tasks and work collaboratively to solve complex problems. These agents leverage the power of Large Language Models (LLMs) but operate with individualized instructions, specialized tools, and defined roles to form a multi-agent system.

What Are Agentic AI Systems?

Most people are familiar with single-agent AI tools like ChatGPT. You ask a question and get an answer. However, real business problems are rarely solved in one step.

Agentic systems work differently.

Imagine a team of experts tackling a business question: one pulls the data, another runs the analysis, a third forecasts trends, a fourth checks for accuracy, and another summarizes the results. Now imagine that team is made of intelligent AI agents—each one working in parallel to deliver more comprehensive answers.

Each agent is designed to:

• Perform a distinct function, such as data extraction, forecasting, QA, or summarization
• Use its own set of tools and knowledge sources
• Collaborate within a larger workflow to improve outcomes

Why It Matters

Business questions aren’t just about what happened. They’re about why it happened, what’s influencing it, and what to do next. Agentic systems enable that level of investigation—at speed and scale—without requiring a team of human analysts to do all the work manually.

Soon, companies will manage these AI systems like digital workforces—onboarding, training, and optimizing performance across fleets of AI agents. Just as spreadsheets transformed accounting without eliminating accountants, agentic systems will augment human decision-making—automating routine tasks while empowering teams to focus on strategy and creativity.

Traditional Tools vs Agentic Systems

Most analytics tools fall short because they operate in isolation:

• BI tools require manual setup and interpretation
• Chat-based AI offers quick answers, but lacks verification and depth

Agentic systems solve this by combining automation, validation, and collaboration into a single, scalable solution.

Orion's Agentic System

Orion, developed by Gravity Foundation, is one of the first enterprise-ready platforms built entirely on an agentic architecture. At its core, it is a team of more than 30 AI agents, each built for a specific role in the analysis process.

These agents operate together to:

• Pull internal business metrics and performance data
• Analyze trends across segments and channels
• Incorporate external drivers like economic indicators, holidays, or weather
• Predict future outcomes and identify risk
• Check for anomalies and validate results
• Synthesize findings into actionable recommendations

So when a user asks a question like "What happened with revenue last week?", Orion doesn’t stop at a number. It delivers a story:

• What changed
• Why it changed
• What influenced it
• What you should do next

Agentic AI systems are more than just an innovation—they’re a fundamental upgrade in how we work with data.

Utilizing tools such as Orion, organizations can transition from simple reporting to automated, contextual, and strategic insight provision. This evolution isn't about replacing employees but enhancing their capacity to make quicker, better-informed decisions.

If your analytics tools aren’t asking deeper questions, maybe it’s time to upgrade the system behind them.