Smol fixes

docs/10-ai-agents/2-when-to-use-agents.mdx

@@ -1,6 +1,6 @@
 # 📘 When to use AI agents?
 
-AI agents are best suited for complex, multi-step tasks that require integration of mutiple capabilities, such as question-answering, analysis, task execution etc. to arrive at the final answer or outcome. An active area of research is to have AI agents learn from their past interactions to build personalized and adaptive experiences.
+AI agents are best suited for complex, multi-step tasks that require integration of multiple capabilities, such as question-answering, analysis, task execution etc. to arrive at the final answer or outcome. An active area of research is to have AI agents learn from their past interactions to build personalized and adaptive experiences.
 
 
 Here are some examples of tasks/questions that **DO NOT** require an AI agent:

docs/60-create-agents/2-tool-calling-agent.mdx

@@ -1,6 +1,6 @@
 # 👐 Create a basic tool-calling agent
 
-Let's start by creating a basic tool-calling agent using the `create_tool_calling_agent` method in LangChain.
+Let's start by creating a basic tool-calling agent using the `create_tool_calling_agent` constructor in LangChain.
 
 Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 7: Create a basic tool-calling agent** section in the notebook to create a basic tool-calling agent.
 

docs/60-create-agents/3-react-agent.mdx

@@ -1,6 +1,6 @@
 # 👐 Create a ReAct agent
 
-Now let's try out an agent that uses ReAct prompting using the `create_react_agent` method in LangChain.
+Now let's try out an agent that uses ReAct prompting using the `create_react_agent` constructor in LangChain.
 
 Fill in any `<CODE_BLOCK>`s and run the cells under the **Step 8: Create a ReAct agent** section in the notebook to create a ReAct agent.