Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)

<Previous Article In Volume
Next Article In Volume>

Aligno: A Model Context Protocol Enabled Collaborative Ecosystem for Agentic Project Management

Authors
Prince Jangra1, Aditya Kotnala1, Arindam Sharma1, Ayush Rawat1, Kshatrapal Singh1, *
1Department of Computer Science and Engineering, KCC Institute of Technology and Management, Greater Noida, 201308, India
*Corresponding author. Email: mekpsingh1@gmail.com
Corresponding Author
Kshatrapal Singh
Available Online 4 June 2026.
DOI
10.2991/978-94-6239-697-5_10How to use a DOI?
Keywords
Agentic Systems; Cognitive Load; Model Context Protocol (MCP); AI-Orchestrated Workflows; Software Engineering Collaboration; Autonomous Task Delegation; Human–AI Interaction
Abstract

The recent trend of employing digital productivity tools in software development has unintentionally led development workflows to become more fragmented and cognitively taxing. While AI tools are increasingly being leveraged to support development activities, project management tools are still largely static, reactive, and decoupled from actual development environments. This paper introduces Aligno, an agentic project management system that endeavors to transform the look of workflow coordination with the utilization of the proposed MCP. Its general aim, therefore, is to foster the production of tasks, the coordination of resources, as well as the synchronization of contexts in projects. It is an embodiment of the proposed Unified Agentic Workflow Theory (UAWT) since it has a structure that can be utilized by cognitive and organizational principles to nurture collaboration between humans and AI. Moreover, this paper introduces, for the first time, Cognitive Fragmentation Index (CFI) and Agentic Delegation Efficiency (ADE) as measures of effectiveness. The benefit of using agentic systems in improving project management practices is validated through experimental results that demonstrate lower administrative overhead and improved coordination.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
Series
Advances in Intelligent Systems Research
Publication Date
4 June 2026
ISBN
978-94-6239-697-5
ISSN
1951-6851
DOI
10.2991/978-94-6239-697-5_10How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Prince Jangra
AU  - Aditya Kotnala
AU  - Arindam Sharma
AU  - Ayush Rawat
AU  - Kshatrapal Singh
PY  - 2026
DA  - 2026/06/04
TI  - Aligno: A Model Context Protocol Enabled Collaborative Ecosystem for Agentic Project Management
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 98
EP  - 116
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_10
DO  - 10.2991/978-94-6239-697-5_10
ID  - Jangra2026
ER  -