docs: Minor updates to docs to make them a little friendlier to new users (#1871)

# What does this PR do?
This PR modifies some of the docs to help them map to (1) the mental
model of software engineers building AI models starting with RAG and
then moving to Agents and (2) aligning the navbar somewhat closer to the
diagram on the home page.

## Test Plan
N/A Tested locally.

# Documentation
Take a look at the screen shot for below and after.
## Before 
![Screenshot 2025-04-03 at 10 39
32 PM](https://github.com/user-attachments/assets/c4dc9998-3e46-43b0-8425-892c94ec3a6a)

## After
![Screenshot 2025-04-03 at 10 38
37 PM](https://github.com/user-attachments/assets/05670fcd-e56b-42dd-8af2-07b81f941d40)

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

docs/source/building_applications/agent_execution_loop.md

@@ -1,6 +1,10 @@
 ## Agent Execution Loop
 
-Agents are the heart of complex AI applications. They combine inference, memory, safety, and tool usage into coherent workflows. At its core, an agent follows a sophisticated execution loop that enables multi-step reasoning, tool usage, and safety checks.
+Agents are the heart of Llama Stack applications. They combine inference, memory, safety, and tool usage into coherent
+workflows. At its core, an agent follows a sophisticated execution loop that enables multi-step reasoning, tool usage,
+and safety checks.
+
+### Steps in the Agent Workflow
 
 Each agent turn follows these key steps:
 
@@ -64,7 +68,10 @@ sequenceDiagram
     S->>U: 5. Final Response
 ```
 
-Each step in this process can be monitored and controlled through configurations. Here's an example that demonstrates monitoring the agent's execution:
+Each step in this process can be monitored and controlled through configurations.
+
+### Agent Execution Loop Example
+Here's an example that demonstrates monitoring the agent's execution:
 
 ```python
 from llama_stack_client import LlamaStackClient, Agent, AgentEventLogger