Back to Prompt Library
implementation

Implement Conditional Routing and Human-in-the-Loop with All Hands AI

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Orchestrate Dynamic Content Strategy Agents with LangGraph and OpenAI o4-mini

Format
Code-aware
Lines
5
Sections
1
Linked challenge
Orchestrate Dynamic Content Strategy Agents with LangGraph and OpenAI o4-mini

Prompt source

Original prompt text with formatting preserved for inspection.

5 lines
1 sections
No variables
1 code block
Enhance your LangGraph workflow by adding conditional routing. After content generation, introduce a 'HumanReview' node that, if content requires approval (e.g., based on a confidence score or keyword flag), routes to an All Hands AI simulation for human review. If approved, it proceeds to a 'PublishingAgent' node. If rejected, it loops back to the ContentGenerator for revision. Define the logic for the conditional edge. ```python
# Assume previous setup for workflow and nodes # Add a conditional edge from 'content_generator'
# Define a function 'should_human_review(state)' that returns a string representing the next node ('human_review' or 'publishing_agent') # Your task: implement should_human_review and add the conditional edge.
# Simulate All Hands AI interaction within the 'human_review_node'.
```

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.