In the rapidly evolving field of artificial intelligence, agentic AI systems are making significant strides. These systems use large language models (LLMs) to make decisions, plan, and collaborate. When an LLM is given a role, tools, and a goal, it becomes an agent capable of autonomously navigating complex tasks. Agents can access APIs or external tools like search engines or databases to achieve their objectives. This approach allows multiple agents to handle multi-step workflows efficiently. Recent discussions by AI experts John Carmack and Andrej Karpathy highlight that AI assistants can interact with applications through text-based interfaces, bypassing the need for vision-based navigation. This method simplifies the interaction process, making AI navigation more efficient and less reliant on visual cues.
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