Build autonomous agents, we need to mimic human thinking patterns and proactively plan for task execution. During planning, you can create LLM agents and break down large and complex tasks into smaller, manageable steps. These agents have to be capable of learning from their past actions and tracking successful results against mistakes. This data helps the overarching autonomous agent to optimize its future steps and improve final results.
To create an autonomous agent for the user, you will brazil whatsapp number data implement a complex system of different agents that work together. At the start, you will have an observer agent that takes in information or requests, then adds relevant context to the request, and then either pushes this request to its memory or task store. Tasks are then pushed to execution agents that carry out the specific task required and create the response or action that the user wants.
Alongside this, you will have other agents that carry out additional tasks. To continue with the previously used fraud detection example: The observer agent looks at credit card transaction data and decides whether to send the task to an execution agent, an observer agent, or other agents that might interact with the transaction. While a single credit card transaction on its own doesn’t mean a lot, the same card being used twice within a short time in different locations hundreds of miles apart could be an example of fraud.