1. User Interaction:
The user inputs a request or question.
2. Data or Code Handling:
The AI agent can pull from any data or document or trigger a code executor to run custom scripts.
3. Task Output:
Once the AI agent understands the task, it orchestrates the process across different systems to produce a clean, optimized output.
4. Model and LLM Connection:
AI agents interact with any ML model for tasks like forecasting, optimization, and predictions, and can connect with any LLM (Large Language Model) such as OpenAI, Meta, or Google for deeper natural language understanding.
In short, it’s a full-circle automation system — from understanding your needs to executing complex workflows across different platforms.