http://localhost:1313/chapter1/s7/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Introduction Throughout this chapter, we’ve explored the foundations of AI systems, from understanding their core architectures to examining deployment strategies, technical underpinnings, crafting effective prompts, and implementing inference techniques. Now, we turn our attention to what many consider the next frontier in AI evolution: agentic systems. While current LLMs excel at generating content and retrieving knowledge, agentic AI goes further by autonomously pursuing goals, making decisions, and taking actions without constant human guidance. This shift from passive tools to proactive agents represents a fundamental transformation that will redefine how organizations leverage AI and has profound implications for security, governance, and the future of work. Hugo en http://localhost:1313/chapter1/s7/s7_activity/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s7/s7_activity/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Practical Activity Overview In this activity, we’ll extend our RAG system to create a simple autonomous agent that can search the web when needed. Our agent will: Detect when a query requires information beyond our document knowledge base Autonomously decide whether to use local RAG or web search Perform web searches for current or specialized information Combine information from documents and the web as needed By the end of this activity, you’ll have a hybrid system that leverages both your local document knowledge base and real-time web information.