docs: HITL consolidation (#5192)

* HITL consolidation, minus server

* Fix links

* Fix server page

* remove extra page

* nits

* updates based on feedback

* Update docs/docs/concepts/human_in_the_loop.md

Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>

* edits based on feedback

---------

Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>

docs/docs/concepts/human_in_the_loop.md

@@ -11,21 +11,23 @@ hide:
 
 # Human-in-the-loop
 
-LangGraph supports robust **human-in-the-loop (HIL)** workflows, enabling human intervention at any point in an automated process. This is especially useful in large language model (LLM)-driven applications where model output may require validation, correction, or additional context.
+To review, edit, and approve tool calls in an agent or workflow, [use LangGraph's human-in-the-loop features](../how-tos/human_in_the_loop/add-human-in-the-loop.md) to enable human intervention at any point in a workflow. This is especially useful in large language model (LLM)-driven applications where model output may require validation, correction, or additional context.
+
+<figure markdown="1">
+![image](../concepts/img/human_in_the_loop/tool-call-review.png){: style="max-height:400px"}
+</figure>
 
 ## Key capabilities
 
-* **Persistent execution state**: LangGraph checkpoints the graph state after each step, allowing execution to pause indefinitely at defined nodes. This supports asynchronous human review or input without time constraints.
+* **Persistent execution state**: LangGraph allows you to pause execution **indefinitely** — for minutes, hours, or even days—until human input is received. This is possible because LangGraph checkpoints the graph state after each step, which allows the system to persist execution context and later resume the workflow, continuing from where it left off. This supports asynchronous human review or input without time constraints.
 
 * **Flexible integration points**: HIL logic can be introduced at any point in the workflow. This allows targeted human involvement, such as approving API calls, correcting outputs, or guiding conversations.
 
-## Typical use cases
+## Patterns
 
-1. [**🛠️ Reviewing tool calls**](../how-tos/human_in_the_loop/add-human-in-the-loop.md#review-tool-calls): Humans can review, edit, or approve tool calls requested by the LLM before tool execution.
-2. **✅ Validating LLM outputs**: Humans can review, edit, or approve content generated by the LLM.
-3. **💡 Providing context**: Enable the LLM to explicitly request human input for clarification or additional details or to support multi-turn conversations.
+There are four typical design patterns that you can implement using `interrupt` and `Command`:
 
-## Implementation
-
-* `interrupt` function: Pauses execution at a specific point, presents information for human review.
-* `Command` primitive: Used to resume execution with a value provided by the human.
+- [Approve or reject](../how-tos/human_in_the_loop/add-human-in-the-loop.md#approve-or-reject): Pause the graph before a critical step, such as an API call, to review and approve the action. If the action is rejected, you can prevent the graph from executing the step, and potentially take an alternative action. This pattern often involves routing the graph based on the human's input.
+- [Edit graph state](../how-tos/human_in_the_loop/add-human-in-the-loop.md#review-and-edit-state): Pause the graph to review and edit the graph state. This is useful for correcting mistakes or updating the state with additional information. This pattern often involves updating the state with the human's input.
+- [Review tool calls](../how-tos/human_in_the_loop/add-human-in-the-loop.md#review-tool-calls): Pause the graph to review and edit tool calls requested by the LLM before tool execution.
+- [Validate human input](../how-tos/human_in_the_loop/add-human-in-the-loop.md#validate-human-input): Pause the graph to validate human input before proceeding with the next step.