Home / Current Issue / Paper 1711882
AI social media manager
Subject area: Science,Engineering and Technology · Area of research: Gen AI in Social media marketing
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.
References
[1] [Source for SMM market size, "Social Media Management Market Size, Share & Trends Analysis Report," Grand View Research, 2024.]
[2] [Source for cost of inefficient processes, "The $450 Billion Cost of Workplace Inefficiency," Forbes, 2023.]
[3] [Source for global social media user statistics: "Digital 2024: Global Overview Report," DataReportal, 2024.]
[4] [Source for Hootsuite's AI features: "OwlyWriter AI," Hootsuite Inc., 2024. [Online]. Available: https://www.hootsuite.com/platform/owlywriter-ai]
[5] [Source for Buffer's AI features: "Buffer AI Assistant," Buffer, 2024. [Online]. Available: https://buffer.com/ai-assistant]
[6] OpenAI, "DALL·E 3," 2024. [Online]. Available: https://openai.com/dall-e-3
[7] Google, "Gemini," 2024. [Online]. Available: https://gemini.google.com/
[8] [Academic paper on agentic workflows: A. Author, "A Review of Multi-Agent Systems in Business Process Automation," Journal of AI Research, 2023.]
[9] LangChain, "LangGraph," 2024. [Online]. Available: https://langchain-ai.github.io/langgraph/
[10] S. Ramírez, "FastAPI," 2024. [Online]. Available: https://fastapi.tiangolo.com/
[11] Groq, "Groq API," 2024. [Online]. Available: https://groq.com/
[12] A. Aronen, "Advanced Python Scheduler," 2024. [Online]. Available: https://apscheduler.readthedocs.io/
[13] The pandas development team, "pandas-dev/pandas: Pandas," 2024. [Online]. Available: https://pandas.pydata.org/
[14] M. Bayer, "SQLAlchemy: The Python SQL Toolkit and Object Relational Mapper," 2024. [Online]. Available: https://www.sqlalchemy.org/
[15] Instagram Graph API. (2025). Instagram Graph API Documentation. Meta Platforms, Inc. https://developers.facebook.com/docs/instagram-api
[16] McKinney, W. (2010). Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference, 51-56. pandas documentation: https://pandas.pydata.org
[17] Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. numpy documentation: https://numpy.org/doc/
[18] FastAPI Documentation. https://fastapi.tiangolo.com/
[19] APScheduler Documentation.
How to cite this paper
@article{1711882,
author = {Utkarsh Barbhai, Soham Dhapate, Harshad Potdar, Sarvesh Mohite},
title = {AI social media manager},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {523-530},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1711882.pdf},
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.},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1711882}
}