The concept of "agentic" AI refers to systems that can act autonomously and make decisions independently, marking a shift from passive to active AI applications. This post explores how agentic AI can enhance real-time analytics by allowing users to interact dynamically with data through various tools and agents, ultimately improving decision-making processes.

Takeaways:
- "Agentic" AI systems can operate autonomously, making decisions and taking actions similar to human agents.
- Traditional dashboards limit user interaction; agentic AI allows for more complex queries and insights beyond preset metrics.
- Hybrid search capabilities combine keyword-based and similarity searches for more accurate results.
- Identifying the right tools is crucial for developing effective agentic applications, enabling flexibility and adaptability to future needs.
- Leveraging AI and LLMs streamlines the process of data analysis, reducing the need for manual coding and enhancing efficiency.

Technologies:
- AI Agents
- Llamaindex
- Vector Databses
- OLAP technologies
- RAG pattern