In this playbook, we will launch a personalization agent capable of generating revenue uplifts. The agent helps you map raw customer data to the recommended marketing experiences across email, website, and paid media.
We created an agent that could analyze customer data from Shopify and generate valuable insights through two different personalization modes.
However, insights alone don't drive business results. The real value comes when we can automatically apply these insights to take action.
In this part 2, we will launch the agent with an activation layer using the serverless deployment template that we developed in an earlier playbook to deploy the agent seamlessly:
Here's a breakdown of what we are going to accomplish in this playbook:
Add an activation layer to the agent that automatically allows us to take action in Shopify
Reorganize our code into a production-ready, modular structure
Deploy our agent as a secure, scalable API using Google Cloud Functions
Demonstrate how to generate measurable business uplift through automated personalization
Our enhanced agent will analyze customers and apply the proper tags, completing the analysis-to-action loop:
The addition of the activation functionality transforms our system from an analysis tool into a proper automation solution. This creates more business value since insights can be automatically applied without manual intervention.
Before we jump into making the agent business-ready, let's understand why tagging is such a powerful activation mechanism:
Targeted Activation: Customer tags in Shopify enable targeted email campaigns, personalized product recommendations, and precisely targeted paid media campaigns.
Operational Efficiency: Automating the tagging process eliminates the need for manual updates, saving hours of marketing team time.
Consistent Customer Experience: Automated tagging ensures all customer touchpoints reflect the same understanding of the customer's needs and preferences.
Measurable Results: Tagged segments make it easier to track the performance of different marketing strategies and calculate ROI.
Replacing Apps with Agents
Our goal for this and the previous two playbooks is twofold. First, we want to build and deploy an end-to-end personalization agent capable of generating business value.
Second, we want to re-use the LangGraph workflow and deployment templates to create a highway for replacing revenue-generating Shopify apps. We will replace the apps with more intelligent, configurable solutions utilizing agentic libraries.
These Shopify Apps will be replaced by agent workflows.
Once we have a template for replacing Shopify apps with valuable AI solutions, we can quickly modify these templates for other platforms.
The Business-Ready Agent
We are going to restructure our agent into a more modular design with a clear separation of concerns:
Main Module: Provides the API entry point for cloud deployment
Agent Module: Orchestrates the workflow using LangGraph
Retrieve Module: Handles data extraction from Shopify
Generate Module: Performs analysis to create segments and personas
Act Module: Our new addition that implements tagging functionality
This modular structure makes our code more maintainable, easier to test, and ready for production deployment.
The code for the LangGraph agent and the deployment scripts is made easily accessible in a Colab notebook and is available at the end of the playbook. You can build, run, and deploy the agent directly from the notebook - all you need is the appropriate API keys.
Building the Activation Layer
The core of our enhancement is the new tagging functionality. Let's examine how the act.py module enables our agent to apply its analysis.
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