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, LLMs, 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