Agents Getting Real Things Done - Breakthrough - Combine RPA with CREWAI or Autogen and GPTVision
AI Summary
Summary: Workflow for Personalized Outreach Automation
Workflow Overview:
- Goal: Automate personalized outreach campaigns using AI agents.
- Tools Used:
- Agentic framework (Qi and Autogen)
- RPA tool (Microsoft Power Automate)
- Excel for database management
- OpenAI’s GPT-4 and GPT-3.5
Steps in Workflow:
- Data Scraping:
- Scrape leads from Facebook, LinkedIn, etc.
- Use data scraper to export CSV with profile links and names.
- Data Aggregation:
- Copy profile URLs to a master Excel file.
- Scrape profile intros, cover photos, and recent posts.
- AI Analysis:
- Use GPT-4 Vision to analyze cover photos.
- Aggregate data to include name, intro, cover photo description, and recent posts.
- Personalization:
- Send aggregated data to GPT-4 to craft personalized outreach messages.
- Validate messages using Qi agents for scoring and risk assessment.
- Automation Execution:
- Use Microsoft Power Automate to loop through prospects and execute tasks.
- Tasks include fetching URLs, scraping data, and running AI analysis.
- Validation and Scoring:
- Qi agents analyze the personalized message for reputational risks and quality.
- Score messages based on professionalism, offensive language, commonalities, etc.
- Further Development:
- Refine prompts for more accurate personalizations.
- Add more parameters for Qi agents to assess.
- Consider using the process for dynamic variables in cold outreach emails.
Challenges and Solutions:
- JSON Formatting: Encountered issues with JSON format in API calls; used tools to validate and sanitize JSON.
- Scripting: Utilized JavaScript and Python scripts for tasks like clicking “See More” buttons and sanitizing text.
Conclusion:
- The workflow is production-ready and offers a high level of control.
- It allows for personalized outreach that doesn’t appear automated.
- Future improvements include refining prompts and expanding validation parameters.