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@article{196693,
        author = {THIRUMALASETTY LAKSHMANA PAVAN KALYAN and VADDARAPU YOGENDRA and SHAIK MEERAVALI and M NALINI},
        title = {An Agentic Multi-Modal Framework for Closed Loop Career Readiness and Adaptive Skill Synthesis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4658-4666},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196693},
        abstract = {Personalized interview preparation is essential for effective career readiness, yet most existing platforms rely on static question repositories and fragmented skill evaluation. Such systems lack adaptability to individual candidates and fail to capture real-time performance, communication confidence, and domain-specific requirements, resulting in generic feedback. This work presents an agentic, multi-modal career readiness framework that unifies resume understanding, adaptive interview orchestration, and voice based confidence analysis within a continuous feedback loop. Specialized autonomous agents dynamically regulate interview complexity, assess technical and behavioral responses, and track progression across sessions. The framework transforms detected skill deficiencies into personalized, domain-aware learning trajectories that evolve with candidate performance, enabling realistic interview simulations, actionable self-insight, and scalable career preparation for diverse job roles.},
        keywords = {Career Readiness; Mock Interview System; Multi-Agent Architecture; Adaptive Assessment; Resume Skill Gap Analysis; Voice-Based Confidence Analysis; Personalized Learning Pathways},
        month = {April},
        }