Abstract
The management of social media for brands and individuals is a critical but highly inefficient process. Professionals rely on a fragmented suite of tools for content creation, scheduling, and performance analysis , a disconnection that hinders the ability to translate analytical data into effective content strategy. This paper introduces the AI-Powered Social Media Manager, a unified platform designed to solve these challenges. The system's core contribution is a novel, stateful agentic workflow built with langgraph, which dynamically manages the entire content lifecycle. This architecture integrates a generative AI for image and text creation , an automated scheduling engine powered by apscheduler , and a comprehensive analytics dashboard. By creating an integrated feedback loop, the system analyzes post engagement and current trends to provide data-driven strategic recommendations for future content. Results demonstrate that this unified system significantly streamlines the management workflow, reducing the time-to-post from over 8 minutes to under 3. The successful implementation of this proof-of-concept demonstrates that unifying these core functions through an agentic AI represents a notable improvement over traditional, multi-platform management methods.
Keywords
Social Media Management, Generative AI, Agentic Workflow, LangGraph, Workflow Automation, Social Media Analytics, Content Strategy.
Citations
IRE Journals:
Utkarsh Barbhai, Soham Dhapate, Harshad Potdar, Sarvesh Mohite "AI social media manager" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 523-530 https://doi.org/10.64388/IREV9I5-1711882
IEEE:
Utkarsh Barbhai, Soham Dhapate, Harshad Potdar, Sarvesh Mohite
"AI social media manager" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1711882
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