Jul 2026· Indonesian Journal of Data and Science· Vol 7· 0 citations
TL;DR
An AI Task Agent system based on a Telegram chatbot, integrated with n8n as a workflow automation platform and Supabase as the database is developed that can improve the effectiveness of students’ task management through more practical, responsive, and structured interactions.
Abstract
Task management is one of the common challenges faced by students when dealing with demanding academic activities and multiple overlapping deadlines. Existing task management applications are generally passive and are not yet capable of providing proactive assistance in determining work priorities. This study aims to develop an AI Task Agent system based on a Telegram chatbot, integrated with n8n as a workflow automation platform and Supabase as the database. The developed system allows users to record tasks with attributes such as task type, description, scheduled time, deadline, urgency level, and the lecturer’s level of strictness. In addition, the system can respond interactively to user commands, display task lists, provide priority recommendations, and send automated reminders. The research employed a system development method consisting of problem identification, requirements analysis, system design, implementation, and testing. The results indicate that the system can improve the effectiveness of students’ task management through more practical, responsive, and structured interactions.
ChatSeven is an agentic AI-based multi-agent platform for customer conversation management and campaign automation that integrates retrieval-augmented generation, Lang Graph ReAct agents, visual workflow automation, a unified inbox, and multi-channel campaign delivery.
Dr. Tariq Siddiqui, Mr. Afnan Shaikh· International Journal of Sci...· 0 citations
: The modern recruitment landscape is characterized by a growing asymmetry between job seekers and employers, largely driven by the widespread use of Applicant Tracking Systems (ATS). Candidates often submit 50–100 applications before receiving an interview opportunity, while a significant portion of applications is filtered out before reaching human recruiters. This paper presents JobPilot, an end-to-end automated system based on an AI vs AI approach, which leverages artificial intelligence to optimize job applications for automated screening systems. The system integrates job data acquisition, requirement analysis, and AI-based generation of personalized cover letters within a unified workflow. A key component of the system is an AI-driven process that performs semantic alignment between candidate profiles and job requirements, enabling context-aware personalization of application content. The evaluation was conducted over a three-week period involving seven users who actively used the system for job search and application submission. The results indicate a significant reduction in application time, with an average of 8.2 minutes per application, compared to traditional approaches that require substantially more time. Users reported improved efficiency and successful interview invitations, confirming the practical value of the system. These findings suggest that AI-driven automation can improve the efficiency of the job application process and support candidates in highly automated recruitment environments.
Maintaining open-source repositories demands continuous attention to issue triage, code review, build monitoring, and community support—tasks that overwhelm individual maintainers when existing automation tools operate in isolation without shared context. Static analyzers check code quality, CI/CD systems run automated tests, and simple bots handle basic labeling, yet none of these tools share information with each other, leaving maintainers as the sole integration point between disconnected systems. We present Repogent, a multi-agent system where four specialized AI agents—Issue Manager, Pull Request Reviewer, CI/CD Maintainer, and Community Assistant—collaborate through event-driven coordination and persistent cross-task memory. Our GitHub webhook-driven architecture processes events through a priority queue, routes them to specialized agents via intelligent orchestration, and stores decisions in a persistent context layer that enables cross-task reasoning. Events are assigned CRITICAL, HIGH, or NORMAL priority, while a semantic memory module based on sentence-transformer embeddings supports code-level search, and all agents share a common LLM service using Qwen 3 32B accessed through the Groq API. Evaluation on a dataset of 150 issues, 100 pull requests, 80 CI/CD workflow executions, and 120 community queries collected from five active open-source Python repositories demonstrates that Repogent achieves 93.3% accuracy in issue classification, 69.0% combined review coverage for pull requests, 86.7% accuracy in CI/CD failure categorization with a macro F1-score of 0.884, and an average quality score of 3.96 out of 5 for community responses. These results establish a foundation for sustainable open-source maintenance through context-aware, multi-agent workflow automation. Furthermore, the proposed framework demonstrates that multi-agent orchestration with persistent shared memory can serve as an effective and scalable solution for automating repository maintenance activities, improving coordination across heterogeneous development workflows, reducing maintainer overhead, and enabling more consistent decision-making across repository events.
Venkata Satya Santhi Somisetti, Vijaya Bhaskar Santhuluri, Sai Teja Pathivada et al.· International Journal of Lat...· 0 citations
Proactive agents are expected to anticipate user needs and provide autonomous assistance by perceiving environmental context without explicit instructions. A fundamental capability of such agents is to identify and track users'upcoming events, enabling continuous and event-specific assistance. For example, by recording the time and location of a planned hike, an agent can deliver weather reminders in advance or provide navigation support before departure. However, existing works on proactive agents largely overlook event-centric assistance, and the open-ended nature of proactive assistance poses challenges for reliable evaluation. To bridge these gaps, we introduce ProEvent, the first event-centric benchmark designed to assess an agent's ability to proactively maintain a user's timetable based on ongoing instant messaging chats. ProEvent provides synthesized yet realistic chats that consider the dynamic interaction among users, concurrent chat threads, and noise in the real world, and evaluates proactive agents on response timing, single-step response correctness, and multi-step response correctness. Experiments on eight LLMs and pipelines reveal that current agents frequently overact and struggle with event cancellation. Notably, even GPT-5.1 only reacts correctly in 26.7% of scenarios. Further qualitative analysis reveals fundamental limitations of current LLMs as proactive agents, particularly in detecting implicit events and reasoning from the user's first-person perspective.
Pilot test results indicate adequate average response times, thematic and temporal diversity of authors and robust data structuring, confirming its viability as an applicable prototype and advancing the integration of generative AI into library services.
Manuel Blázquez-Ochando, J. Prieto-Gutiérrez, María-Antonia Ovalle-Perandones· Library hi tech· 0 citations
ATWZ (Agent Team Work Zone), a filesystem-based operations layer built around Claude Code's native Agent Teams that addresses problems and substantially mitigate the agentic"technical debt" described above.