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content/modules/ROOT/pages/03-01-ai-agent-frameworks.adoc

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 AI agent frameworks provide a foundation for building systems where intelligent agents can autonomously interact, make decisions, and perform tasks within a defined environment. These frameworks are becoming essential tools for developers, enabling them to create agents capable of reasoning, learning, and adapting to complex scenarios. Agents within these frameworks often act independently or collaboratively, fulfilling roles from simple task automation to complex decision-making in real-time.
 
-image::03/03-01-ai-agents-diagram.png[AI Agents Frameworks, scaledwidth="50%"]
+image::03/03-01-ai-agents-diagram.png[AI Agents Frameworks, scaledwidth="40%"]
 
 At a high level, AI agents can be designed to manage workflows, respond to human commands, and process vast amounts of data. They work autonomously yet are often configurable to align with specific tasks or goals. Common capabilities include data retrieval, natural language understanding, and integration with external tools or databases, allowing them to access and act on information from diverse sources. These frameworks support scalability, enabling systems to grow from handling individual tasks to managing multi-agent ecosystems in production environments.
 

content/modules/ROOT/pages/04-03-tools-usage.adoc

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 Large language models (LLMs) are powerful for natural language understanding and response generation, but they have limitations, especially when it comes to accessing real-time information or performing specific computations. By incorporating external tools, LLMs can extend their capabilities, enabling them to retrieve up-to-date information, perform complex calculations, and interact with structured systems like databases and APIs.
 
 image::04/04-02-tools-diagram.png[AI Agents Frameworks, scaledwidth="70%"]