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@article{186347,
        author = {Harsh Pawar and Chirag Patil and Ashutosh Pharande and Anas Shaikh and Prof. Deepa Agrawal},
        title = {Job Application Automation Using Agentic AI: A Multi- Agent Framework for Automated Job Application},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {6},
        pages = {1730-1733},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186347},
        abstract = {This project presents an Automated Job Application System built using Crew AI, a multi-agent orchestration framework that automates the entire job application process. It employs specialized agents—such as the Resume Parser, Job Scraper, Job Matcher, Tailor, Application, Tracker, Compliance, and Supervisor Agents—to collaboratively handle tasks like extracting resume data, retrieving and ranking job postings, generating tailored application materials, and submitting applications through APIs or for manual review. By integrating Natural Language Processing (NLP), Large Language Models (LLMs), and agent- based coordination, the system achieves scalable, personalized, and transparent automation that reduces candidate effort, improves application quality, and enables progress tracking. The project ultimately transforms the job search into a structured, efficient, and intelligent process, with future enhancements planned for job recommendations, multilingual support, and broader ATS integration.},
        keywords = {Automated Job Application System, Crew AI, Resume Parsing and Tailoring, Multi-Agent Framework, Job Matching and Application Automation, NLP and LLM’s.},
        month = {March},
        }