SuccessionAI: An Intelligent Multi-Agent System for Personalized Individual Development Plans Using Large Language Models and Machine Learning

Abstract

Succession planning and employee development in modern organizations remain heavily dependent on manual, subjective processes that produce generic Individual Development Plans (IDPs) incapable of addressing the unique competency gaps and career trajectories of individual employees. This paper presents SuccessionAI, an intelligent, AI-powered succession planning and personalized IDP generation platform that leverages a multi-agent pipeline orchestrated by LangGraph, powered by the Groq LLaMA 3.3 70B large language model, and augmented with a Scikit-Learn Random Forest classifier for role readiness prediction. The system automates the end-to-end talent development workflow: it retrieves employee skill profiles from MongoDB, performs LLM-driven competency gap analysis against target role requirements, predicts readiness scores using machine learning, generates a curated learning roadmap via a recommendation agent, discovers relevant courses through the Tavily web search API, identifies suitable mentors through skill-based cosine similarity matching, and compiles a fully personalized IDP aligned to the 70-20-10 learning framework. Analytics features including a Nine-Box Performance-Potential Matrix and interactive Plotly.js dashboards provide HR professionals and managers with data-driven workforce insights. Experimental evaluation demonstrates significant improvements in IDP relevance, succession decision quality, and HR operational efficiency compared to conventional approaches. SuccessionAI establishes a replicable framework for applying agentic AI and machine learning to enterprise human capital management