Masterclass Blog #Post 2: Automating Follow-Up Emails with Agent Follow-Up Scheduler

In this second part of the LangGraph masterclass series, we’ll dive into Agent Follow-Up Scheduler. This agent will help automate follow-up emails for your business, ensuring that no leads or conversations go unanswered. By the end of this blog post, you'll have a fully automated follow-up system integrated with your LangGraph pipeline.

Why Automate Follow-Ups?

Follow-up emails are essential for maintaining engagement with leads, clients, or partners. However, manually sending follow-ups can be time-consuming, especially as your email list grows. Automating follow-ups helps streamline communication, ensuring timely responses and minimizing the risk of losing track of important conversations.

Step 1: Define the Agent

The Agent Follow-Up Scheduler will handle the task of sending follow-up emails based on user-defined conditions, such as no response within a set period or a specific action needed from the recipient.

Let’s break down the tasks this agent will handle:

1. Track Email Conversations: Monitor incoming emails to detect when a response is overdue or required.
2. Set Follow-Up Time Frames: Automatically define follow-up intervals (e.g., 24 hours, 48 hours, etc.).
3. Generate Personalized Follow-Up Emails: Use the data from the previous email exchanges to create contextually relevant follow-up messages.
4. Send Follow-Up Emails: Trigger the sending of follow-ups automatically when the time frame has passed without a response.
5. Send a Summary: After the follow-up is sent, the agent will generate a summary of all follow-up actions and send it to a specified email list.

Step 2: Build the Follow-Up Scheduler Agent

Now let’s build the agent. We’ll need to define it within LangGraph and ensure that it works seamlessly with the rest of the pipeline.

2.1: Initialize the Follow-Up Scheduler Agent

Start by defining the agent using LangGraph’s API:

from langgraph import LangAgent

class FollowUpScheduler(LangAgent):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        
    def process_email(self, email):
        """
        Check if follow-up is needed and schedule a follow-up.
        """
        if self.is_follow_up_required(email):
            follow_up_email = self.generate_follow_up(email)
            self.schedule_follow_up(follow_up_email)
    
    def is_follow_up_required(self, email):
        """
        Check if no response has been received within the defined timeframe.
        """
        response_time = self.calculate_response_time(email)
        return response_time > self.follow_up_threshold()

    def generate_follow_up(self, email):
        """
        Generate a personalized follow-up email.
        """
        return f"Hi {email.sender}, just checking in on my previous message. Looking forward to your response!"

    def schedule_follow_up(self, follow_up_email):
        """
        Schedule the follow-up email to be sent after a set time interval.
        """
        # Define the logic to schedule the email, using crons or cloud services
        pass
        

2.2: Set Follow-Up Conditions and Timeframes

Configure your follow-up rules. For example, if there’s no reply in 48 hours, a follow-up will be sent automatically.

You can set the following configurations:

class FollowUpScheduler(LangAgent):
    def __init__(self, follow_up_threshold=48, *args, **kwargs):
        self.follow_up_threshold = follow_up_threshold  # Follow-up after 48 hours
        super().__init__(*args, **kwargs)
        

Step 3: Deploying Agent Follow-Up Scheduler

Once we’ve defined the agent, it’s time to deploy it alongside the rest of your LangGraph pipeline. We’ll use AWS, Heroku, or Google Cloud to deploy the agent and set up automated tasks.

For deployment, you’ll follow the same steps as Agent Mailman. Here’s a quick recap:

• Deploy to AWS EC2: Set up the environment, upload the project, and configure the server to run the agent.
• Deploy to Heroku: Use Heroku’s simple deployment process and add a scheduler.
• Deploy to Google Cloud: Use App Engine for deployment and configure Cloud Scheduler to run the agent.

Step 4: Automate Follow-Up Workflow

After deploying the agent, the next step is automating the follow-up email process.

4.1: Automate the Follow-Up Task with Scheduled Jobs

Use cron jobs (on AWS), Heroku Scheduler, or Google Cloud Scheduler to automate the process of checking emails and sending follow-up messages at regular intervals.

For example, a cron job on AWS to check for overdue follow-ups every hour:

0 * * * * /usr/bin/python3 /path/to/your/project/follow_up_scheduler.py
        

4.2: Automate Email Sending

For email sending automation, integrate an email service provider (like SendGrid, SES, or Mailgun) with your LangGraph agent to send follow-ups automatically.

import sendgrid
from sendgrid.helpers.mail import Mail, Email, To, Content

class FollowUpScheduler(LangAgent):
    def send_follow_up_email(self, follow_up_email, recipient_email):
        sg = sendgrid.SendGridAPIClient(api_key='your-sendgrid-api-key')
        from_email = Email("your-email@example.com")
        to_email = To(recipient_email)
        content = Content("text/plain", follow_up_email)
        mail = Mail(from_email, to_email, "Follow-up on your previous message", content)
        
        response = sg.send(mail)
        return response.status_code
        

Step 5: Test the Follow-Up System

Once deployed, thoroughly test the system:

1. Send a test email.
2. Wait for the defined follow-up period (e.g., 48 hours).
3. Check if the follow-up email is sent automatically after the period has passed.

Step 6: Summary Reporting

Agent Follow-Up Scheduler will generate a summary of its actions and send it to an email list you define. This helps you track the performance and efficiency of the follow-up process.

For example, the summary might look like:

class FollowUpScheduler(LangAgent):
    def send_summary(self, summary_email, recipients):
        """
        Send the follow-up summary to a predefined email list.
        """
        sg = sendgrid.SendGridAPIClient(api_key='your-sendgrid-api-key')
        from_email = Email("your-email@example.com")
        to_email = To(recipients)
        content = Content("text/plain", summary_email)
        mail = Mail(from_email, to_email, "Follow-Up Summary Report", content)
        
        response = sg.send(mail)
        return response.status_code
        

Conclusion

With Agent Follow-Up Scheduler, you now have an automated system that ensures your emails never go unanswered. By automating follow-ups, you save valuable time, stay consistent in your communication, and increase engagement with leads and clients.

In the next blog post, we’ll explore how to automate newsletters using Agent Newsletter Generator.

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