Why Agentic AI Matters?

Commercial Agentic AI aims at solving real-world problems by automating tasks, and enhancing workflows.

At its core Agentic AI autonomously aims to processing tasks using user input, databases, LLM — as explored in the intelligence factory race between AI labs — s, and feedback loops, enabling continuous learning and adaptability of these underlying agents.

An AI Agent flow would look like this:
1. User Input: The process begins with a user providing input or a query, which the AI agent processes.
2. AI Agent (think of it as the brain of the whole system): The central component managing the interaction. It retrieves, processes, and executes tasks by integrating multiple data sources and AI models.
3. Databases:

• Relational Databases: For structured data storage and retrieval.
• Vector Databases: For unstructured data, such as embeddings or similarity searches.
  4. LLM (Large Language Model): Acts as the reasoning engine to process data, understand tasks, and generate solutions.
  5. Action Execution: The AI agent performs specific actions, such as updating records, automating tasks, or providing actionable insights.
  6. Data Flywheel: A feedback loop that incorporates the results of actions back into the system for continuous improvement and learning.
  7. Model Customization: Tailoring the AI model to user-specific workflows or datasets for enhanced accuracy and efficiency.

Why does this flow is critical to the development of the next stage of AI development?

•   Autonomy: This architecture enables the AI agent to analyze, reason, and execute tasks autonomously.
•   Customization: With model customization, the system can adapt to domain-specific requirements.
•   Efficiency: The feedback loop (data flywheel) ensures constant optimization and faster task execution over time.
•   Integration: Combining structured and unstructured data enhances the system’s ability to process diverse information.

Image Credit: NVIDIA — as explored in the economics of AI compute infrastructure —