docs: agents & callbacks fixes (#10066)

Various improvements to the Agents & Callbacks sections of the
documentation including formatting, spelling, and grammar fixes to
improve readability.

docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx

@@ -37,11 +37,11 @@ This agent is designed to be used in conversational settings.
 The prompt is designed to make the agent helpful and conversational.
 It uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions.
 
-### [Self ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html)
+### [Self-ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html)
 
 This agent utilizes a single tool that should be named `Intermediate Answer`.
 This tool should be able to lookup factual answers to questions. This agent
-is equivalent to the original [self ask with search paper](https://ofir.io/self-ask.pdf),
+is equivalent to the original [self-ask with search paper](https://ofir.io/self-ask.pdf),
 where a Google search API was provided as the tool.
 
 ### [ReAct document store](/docs/modules/agents/agent_types/react_docstore.html)
@@ -54,4 +54,4 @@ This agent is equivalent to the
 original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.
 
 ## [Plan-and-execute agents](/docs/modules/agents/agent_types/plan_and_execute.html)
-Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
+Plan-and-execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).

docs/docs_skeleton/docs/modules/agents/agent_types/plan_and_execute.mdx

@@ -1,6 +1,6 @@
-# Plan and execute
+# Plan-and-execute
 
-Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
+Plan-and-execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
 
 The planning is almost always done by an LLM.
 

docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_agent.mdx

@@ -1,13 +1,13 @@
-# Custom LLM Agent
+# Custom LLM agent
 
 This notebook goes through how to create your own custom LLM agent.
 
 An LLM agent consists of three parts:
 
-- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
+- `PromptTemplate`: This is the prompt template that can be used to instruct the language model on what to do
 - LLM: This is the language model that powers the agent
 - `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
-- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
+- `OutputParser`: This determines how to parse the LLM output into an `AgentAction` or `AgentFinish` object
 
 import Example from "@snippets/modules/agents/how_to/custom_llm_agent.mdx"
 

docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_chat_agent.mdx

@@ -4,10 +4,10 @@ This notebook goes through how to create your own custom agent based on a chat m
 
 An LLM chat agent consists of three parts:
 
-- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
-- ChatModel: This is the language model that powers the agent
+- `PromptTemplate`: This is the prompt template that can be used to instruct the language model on what to do
+- `ChatModel`: This is the language model that powers the agent
 - `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
-- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
+- `OutputParser`: This determines how to parse the LLM output into an `AgentAction` or `AgentFinish` object
 
 import Example from "@snippets/modules/agents/how_to/custom_llm_chat_agent.mdx"