Authors : Tripti Singh; Dolly Verma; Parineeta Jha

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/56umawec

DOI : https://doi.org/10.38124/ijisrt/26sep1386

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : AI coding agents such as Claude Code, Cursor, Codex, and OpenCode have matured into capable multi-step software engineers, yet nearly all of them still assume a developer is sitting at a terminal. PromptBridge removes that assumption by connecting six messaging platforms, Telegram, Discord, Slack, Microsoft Teams, GitHub, and Email, to four coding agents through a single dispatcher. This paper presents the complete, matured architecture of PromptBridge: the four-layer design, the session and git-aware change tracking model, the security and configuration layer, and the packaged Electron desktop application. Beyond describing the current system, this paper evaluates it against related agent frameworks and bot toolkits, sets out a formal threat model, examines the structural checkpoints that keep a human in the approval loop for every agent-initiated change, and lays out a concrete future scope covering sandboxed execution, richer output formatting, session summarization, and controlled user studies. The goal is to give a single, self-contained account of what PromptBridge is today and where it is going next.

Keywords : AI Coding Agents, Multi-Platform Orchestration, Session Management, Git-Aware Automation, Conversational Software Engineering, Security, Future Scope.

References :

1. M. Chen et al., "Evaluating large language models trained on code," arXiv preprint arXiv:2107.03374, 2021, doi: 10.48550/arXiv.2107.03374.
2. A. Ziegler, E. Kalliamvakou, X. A. Li, A. Rice, D. Rifkin, S. Simister, G. Sittampalam, and E. Aftandilian, “Productivity assessment of neural code completion,” in Proc. 6th ACM SIGPLAN Int. Symp. Mach. Program. (MAPS), 2022, pp. 21–29, doi: 10.1145/3520312.3534864.
3. Y. Li et al., “Competition-level code generation with AlphaCode,” Science, vol. 378, no. 6624, pp. 1092–1097, 2022, doi: 10.1126/science.abq1158.
4. Anthropic, “Claude Code,” [Online]. Available: https://code.claude.com/docs/en/overview. Accessed: Sep. 21, 2026.
5. Anysphere, “Cursor: The AI Code Editor,” [Online]. Available: https://www.cursor.com/. Accessed: Sep. 21, 2026.
6. C. E. Jimenez et al., “SWE-bench: Can language models resolve real-world GitHub issues?,” arXiv preprint arXiv:2310.06770, 2023, doi: 10.48550/arXiv.2310.06770.
7. J. Yang, C. E. Jimenez, A. Wettig, K. Lieret, S. Yao, K. Narasimhan, and O. Press, “SWE-agent: Agent-computer interfaces enable automated software engineering,” arXiv preprint arXiv:2405.15793, 2024, doi: 10.48550/arXiv.2405.15793.
8. Cognition, “Introducing Devin, the first AI software engineer,” [Online]. Available: https://cognition.com/blog/introducing-devin. Accessed: Sep. 21, 2026.
9. S. Yao et al., “ReAct: Synergizing reasoning and acting in language models,” in Proc. Int. Conf. Learn. Representations (ICLR), 2023.
10. Q. Wu et al., “AutoGen: Enabling next-gen LLM applications via multi-agent conversation,” arXiv preprint arXiv:2308.08155, 2023, doi: 10.48550/arXiv.2308.08155.
11. H. Chase, “LangChain,” GitHub repository, 2022. [Online]. Available: https://github.com/langchain-ai/langchain. Accessed: Sep. 21, 2026.
12. J. Liu, “LlamaIndex,” GitHub repository, 2022. [Online]. Available: https://github.com/run-llama/llama_index. Accessed: Sep. 21, 2026.
13. Google Cloud, “Dialogflow,” [Online]. Available: https://cloud.google.com/dialogflow. Accessed: Sep. 21, 2026.
14. T. Bocklisch, J. Faulkner, N. Pawlowski, and A. Nichol, “Rasa: Open source language understanding and dialogue management,” arXiv preprint arXiv:1712.05181, 2017, doi: 10.48550/arXiv.1712.05181.
15. Slack Technologies, “Bolt for JavaScript,” Slack Developer Documentation, [Online]. Available: https://docs.slack.dev/tools/bolt-js/. Accessed: Sep. 21, 2026.
16. Microsoft, “Bot Framework SDK,” GitHub repository, [Online]. Available: https://github.com/microsoft/botframework-sdk. Accessed: Sep. 21, 2026.
17. T. D. LaToza, G. Venolia, and R. DeLine, “Maintaining mental models: A study of developer work habits,” in Proc. 28th Int. Conf. Software Eng. (ICSE), 2006, pp. 492–501, doi: 10.1145/1134285.1134355.
18. C. Bird, N. Nagappan, P. T. Devanbu, H. C. Gall, and B. Murphy, “Does distributed development affect software quality? An empirical case study of Windows Vista,” in Proc. 31st Int. Conf. Software Eng. (ICSE), 2009, pp. 518–528, doi: 10.1109/ICSE.2009.5070550.
19. D. Hilton, N. Nelson, T. T. D. Le, E. K. K. et al., “Usage, costs, and benefits of continuous integration in open-source projects,” in Proc. 31st IEEE/ACM Int. Conf. Automated Software Eng. (ASE), 2016, pp. 426–437, doi: 10.1145/2970276.2970358.
20. J. Liu, K. Wang, Y. Chen, X. Peng, Z. Chen, L. Zhang, and Y. Lou, “Large language model-based agents for software engineering: A survey,” arXiv preprint arXiv:2409.02977, 2024, doi: 10.48550/arXiv.2409.02977.
21. E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong, “CodeGen: An open large language model for code with multi-turn program synthesis,” arXiv preprint arXiv:2203.13474, 2022, doi: 10.48550/arXiv.2203.13474.
22. D. Guo et al., “DeepSeek-Coder: When the large language model meets programming,” arXiv preprint arXiv:2401.14196, 2024, doi: 10.48550/arXiv.2401.14196.
23. C. Packer, S. Wooders, K. Lin, V. Fang, S. G. Patil, I. Stoica, and J. E. Gonzalez, “MemGPT: Towards LLMs as operating systems,” arXiv preprint arXiv:2310.08560, 2023, doi: 10.48550/arXiv.2310.08560.
24. S. Kim et al., “gVisor: Container security through kernel API sandboxing,” Google, Tech. Rep., 2019.
25. P. Weißgerber, D. Neu, and S. Diehl, “Small patches get in!,” in Proc. 2008 Int. Working Conf. Mining Software Repositories (MSR), 2008, pp. 67–76, doi: 10.1145/1370750.1370767.
26. Slack Technologies, “Block Kit,” Slack Developer Documentation, [Online]. Available: https://docs.slack.dev/block-kit/. Accessed: Sep. 21, 2026.
27. R. Parasuraman and V. Riley, “Humans and automation: Use, misuse, disuse, abuse,” Human Factors, vol. 39, no. 2, pp. 230–253, 1997, doi: 10.1518/001872097778543886.