CareerPath AI — Multi-Agent Career Growth Planning System 🚀
TL;DR ⚡
CareerPath AI is a fully orchestrated 4-agent system that transforms a user’s current role, skills, and target role into a complete career-transition plan. It performs skill-gap analysis, generates interview preparation materials, builds a personalized 12-week learning roadmap, and provides improvement feedback. The system uses LangGraph for orchestration, Groq’s Llama-3.3-70B for reasoning, and multiple custom tools for course recommendations, timeline estimation, and skill evaluation.
Abstract 📘
CareerPath AI is a multi-agent career-development assistant that produces structured and actionable career roadmaps. Instead of relying on a single LLM prompt, the system uses four cooperating agents—Role Analyzer, Interview Preparer, Learning Path Creator, and Feedback Analyzer—each performing a specialized stage of reasoning.
LangGraph provides sequential execution, shared state management, and deterministic orchestration. A custom tools layer supports course lookups, skill taxonomies, timeline estimation, interview-prep checklists, and practice suggestions, ensuring outputs are consistent, grounded, and reproducible.
The system delivers:
- A full skill-gap analysis
- A set of tailored interview questions
- A detailed 12-week learning plan
- An improvement and feedback strategy
This project demonstrates practical agentic AI engineering aligned with ReadyTensor’s Module-2 requirements.
Project Overview 🎯
CareerPath AI guides users through job transitions by combining structured agent reasoning, workflow orchestration, and tool-assisted decision-making. The system accepts three inputs—current role, skillset, and target role—and produces a comprehensive career-growth plan. It reflects modern agentic design principles: modular architecture, state-based orchestration, deterministic LLM calls, and reproducible tool usage.
System Architecture 🧠
CareerPath AI is built around four cooperating agents coordinated through LangGraph. A shared state object ensures information flows predictably between agents.
Agent 1 — Role Analyzer
Evaluates the user’s current skills versus target role requirements. Identifies critical gaps and timelines using get_role_requirements() and estimate_timeline().
Agent 2 — Interview Preparer
Generates structured technical, behavioral, and situational interview questions using the get_interview_prep_checklist() tool.
Agent 3 — Learning Path Creator
Constructs a detailed 12-week upskilling roadmap using curated learning resources via find_courses() and COURSE_DATABASE.
Agent 4 — Feedback Analyzer
Produces improvement strategies, common pitfalls, competency evaluation criteria, and practice suggestions using get_practice_tips().
Orchestration (LangGraph)
LangGraph manages:
- Sequential execution
- Shared-state updates
- Routing logic
- Error handling
Agent pipeline:
Role Analysis → Interview Prep → Learning Path → Feedback → Final Output
Tools Layer
Tools used include:
- Role requirements + skill taxonomy
- Course database
- Timeline estimator
- Interview-prep checklist
- Practice & feedback engine
LLM Layer
All agent reasoning is driven by Groq Llama-3.3-70B Versatile, ensuring fast, high-quality output with structured prompt templates for consistency.
Key Features ✨
- Four-agent modular architecture
- Deterministic LangGraph orchestration
- Multiple integrated reasoning tools
- Real course recommendations & learning paths
- End-to-end interview preparation package
- Detailed feedback & improvement strategy
- Reproducible repository with
.env.exampleand requirements - Architecture diagram + execution screenshots included
Use Cases 💼
CareerPath AI is ideal for:
- Students transitioning into tech/design
- Designers shifting into UX roles
- Developers moving into Data Science
- Job seekers needing structured interview prep
- Career counseling and learning platforms
Repository & Reproducibility 🧩
The GitHub repository includes:
- Full multi-agent system (
full_system_v2.py) - LangGraph orchestrator (
langgraph_orchestrator.py) - Example scenario generator (
example_scenarios.py) - Tools layer (
tools_enhanced.py) - Baseline demos (
simple_demo.py,full_system.py) - Architecture diagram
- Five system screenshots
requirements.txtand.env.example- Detailed
README.mdwith full setup instructions
Everything required for end-to-end replication is provided.
Supplementary Files & Demonstrations 📎
This publication includes five supporting screenshots:
- Architecture Diagram (PNG)
- Full system with tools (final output)
- LangGraph orchestrator (Steps Completed: 4/4)
- Example scenarios (multiple transitions)
- Tools output (course recommendations & skill mapping)
These files visually demonstrate the system’s architecture, execution flow, and tool integration.
Conclusion 🏁
CareerPath AI showcases a complete, production-grade multi-agent architecture using LangGraph, custom tools, and structured LLM reasoning. It delivers a comprehensive, actionable career-transition plan and reflects strong engineering practices in agentic AI design, tool integration, and workflow orchestration. This work fully aligns with Module-2 requirements of the ReadyTensor Agentic AI Developer Certification Program.