adding remaining chapters

06-building-trustworthy-agents/README.md

@@ -183,7 +183,7 @@ Building trustworthy AI agents requires careful design, robust security measures
 
 ## Additional Resources
 
-- [Responsible AI overview](https://learn.microsoft.com/azure/ai-studio/responsible-use-of-ai-overview){target="_blank"}
--[Evaluation of generative AI models and AI applications](https://learn.microsoft.com/azure/ai-studio/concepts/evaluation-approach-gen-ai){target="_blank"}
-- [Safety system messages](https://learn.microsoft.com/azure/ai-services/openai/concepts/system-message?context=%2Fazure%2Fai-studio%2Fcontext%2Fcontext&tabs=top-techniques){target="_blank"}
-- [Risk Assessment Template](https://blogs.microsoft.com/wp-content/uploads/prod/sites/5/2022/06/Microsoft-RAI-Impact-Assessment-Template.pdf?culture=en-us&country=us){target="_blank"}
+- <a href="https://learn.microsoft.com/azure/ai-studio/responsible-use-of-ai-overview" target="_blank">Responsible AI overview</a>
+- <a href="https://learn.microsoft.com/azure/ai-studio/concepts/evaluation-approach-gen-ai" target="_blank">Evaluation of generative AI models and AI applications</a>
+- <a href="https://learn.microsoft.com/azure/ai-services/openai/concepts/system-message?context=%2Fazure%2Fai-studio%2Fcontext%2Fcontext&tabs=top-techniques" target="_blank">Safety system messages</a>
+- <a href="https://blogs.microsoft.com/wp-content/uploads/prod/sites/5/2022/06/Microsoft-RAI-Impact-Assessment-Template.pdf?culture=en-us&country=us" target="_blank">Risk Assessment Template</a>

07-planning-design/README.md

@@ -43,7 +43,7 @@ This modular approach also allows for incremental enhancements. For instance, yo
 
 ### Structured output
 
-Large Language Models (LLMs) can generate structured output (e.g. JSON) that is easier for downstream agents or services to parse and process. This is especially useful in a multi-agent context, where we can action these tasks after the planning output is received. Refer to this [blogpost](https://microsoft.github.io/autogen/stable/user-guide/core-user-guide/cookbook/structured-output-agent.html){target="_blank"} for a quick overview.
+Large Language Models (LLMs) can generate structured output (e.g. JSON) that is easier for downstream agents or services to parse and process. This is especially useful in a multi-agent context, where we can action these tasks after the planning output is received. Refer to this <a href="https://microsoft.github.io/autogen/stable/user-guide/core-user-guide/cookbook/structured-output-agent.html" target="_blank">blogpost</a> for a quick overview.
 
 Below is an example Python snippet that demonstrates a simple planning agent decomposing a goal into subtasks and generating a structured plan:
 
@@ -192,12 +192,12 @@ e.g sample code
     # .. re-plan and send the tasks to respective agents
     ```
 
-For a more comprehensive planning do checkout Magnetic One [Blogpost](https://www.microsoft.com/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks){target="_blank"} for solving complex tasks.
+For a more comprehensive planning do checkout Magnetic One <a href="https://www.microsoft.com/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks" target="_blank">Blogpost</a> for solving complex tasks.
 
 ## Summary
 
 In this article we have looked at an example of how we can create a planner that can dynamically select the available agents defined. The output of the Planner decomposes the tasks and assigns the agents so them to be executed. It is assumed the agents has access to function/tools that are required to perform the task. In addition to the agents you can include other patterns like reflection, summarizer, and round robin chat to further customize.
 
 ## Additional Resources
 
-* AutoGen Magentic One - A Generalist multi agent system for solving complex task and has achieved impressive results on multiple challenging agentic benchmarks. Reference: [autogen-magentic-one](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-magentic-one){target="_blank"}. In this implementation the orchestrator create task specific plan and delegates these tasks to the available agents. In addition to planning the orchestrator also employs a tracking mechanism to monitor the progress of the task and re-plans as required.
+* AutoGen Magentic One - A Generalist multi agent system for solving complex task and has achieved impressive results on multiple challenging agentic benchmarks. Reference: <a href="https://github.com/microsoft/autogen/tree/main/python/packages/autogen-magentic-one" target="_blank">autogen-magentic-one</a>. In this implementation the orchestrator create task specific plan and delegates these tasks to the available agents. In addition to planning the orchestrator also employs a tracking mechanism to monitor the progress of the task and re-plans as required.

08-multi-agent/README.md

@@ -169,5 +169,5 @@ In this lesson, we've looked at the multi-agent design pattern, including the sc
 
 ## Additional resources
 
-- [AutoGen design patterns](https://microsoft.github.io/autogen/stable/user-guide/core-user-guide/design-patterns/intro.html){target="_blank"}
-- [Agentic design patterns](https://www.analyticsvidhya.com/blog/2024/10/agentic-design-patterns/){target="_blank"}
+- <a href="https://microsoft.github.io/autogen/stable/user-guide/core-user-guide/design-patterns/intro.html" target="_blank">AutoGen design patterns</a>
+- <a href="https://www.analyticsvidhya.com/blog/2024/10/agentic-design-patterns/" target="_blank">Agentic design patterns</a>

10-ai-agents-production/README.md

@@ -58,7 +58,7 @@ This is currently the last lesson of "AI Agents for Beginners".
 
 We plan to continue to add lessons based on feedback and changes in this ever growing industry so stop by again in the near future.
 
-If you want to continue your learning and building with AI Agents, join the [Azure AI Community Discord](https://discord.gg/kzRShWzttr){target="_blank"}.
+If you want to continue your learning and building with AI Agents, join the <a href="https://discord.gg/kzRShWzttr" target="_blank">Azure AI Community Discord</a>.
 
 We host workshops, community roundtables and "ask me anything" sessions there.