AI Agents are autonomous programs designed to perform tasks, make decisions, and communicate effectively. Unlike traditional chatbots, they utilize external tools for improved accuracy. The field is transitioning toward Agentic AI, which will engage proactively in problem-solving. Building these Agents has become accessible, similar to training machine learning models a decade ago. The tutorial provides step-by-step instructions on creating AI Agents using Python, emphasizing the use of the Ollama library to run LLMs locally, enhancing data privacy and control. Users can create custom Agents easily, with clear guidance throughout the process.
AI Agents are advancing from reactive responses to proactive problem-solving, allowing them to make independent decisions using external tools for enhanced accuracy.
Building AI Agents today is as easy as training basic machine learning models was a decade ago, thanks to simplified libraries like Ollama.
With AI Agents processing sequential reasoning and utilizing LLMs, users can run custom Agents locally, ensuring better data privacy and performance.
The tutorial aims to guide users in creating AI Agents, making it accessible for anyone to build and run these programs without significant infrastructure.
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