International Journal For Multidisciplinary Research

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AI-Powered Career Planning Using Multi-Agent Systems and Large Language Models

Author(s) Mr. Sanat Nanasaheb Ladkat, Dr. Manisha Prakash Bharati
Country India
Abstract Traditional career counseling methods often fail to keep pace with evolving job markets, leading to misaligned guidance and decision fatigue. This paper introduces a comprehensive, AI-powered system that integrates a multi-agent architecture with locally hosted Large Language Models (LLMs) via Ollama to deliver personalized, privacy-preserving career planning. Key system components include:
• Profile Agent: Structures user inputs such as academic background, interests, and constraints into a machine-readable profile.
• Career Agent: Generates context-specific career suggestions with justification, demand forecasting, and comparative pros/cons.
• Skills Agent: Maps chosen career paths to prioritized skill sets, certifications, and curated learning resources.
• Roadmap Agent: Synthesizes actionable, timeline-based roadmaps tailored to individual goals and resource constraints.
Built with Streamlit for an interactive web interface, the system was evaluated with 20 users across diverse backgrounds. Results show 85% high relevance in career suggestions, 90% alignment in skill mapping, and an average end-to-end latency under 20 seconds. The prototype demonstrates significant gains in adaptability, explainability, and user satisfaction compared to traditional tools. Future work includes multilingual support, dynamic labor-market integration, and gamified progress tracking to further enhance user engagement and real-world applicability.
Keywords : AI in Career Planning, Multi-Agent Systems, Local LLMs, Ollama, Streamlit, Decision Support Systems
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-31
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.46587
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