Kai-Kristian Kemell

Evil-AI Benchmark: An Evaluation Framework for Adversarial Security Assessment of LLM Agents in Smart Environments

Smart environments increasingly integrate artificial intelligence (AI) to process sensor data, coordinate Internet of Things (IoT) devices, and actuate cyber-physical services. Within these environments, large language models (LLMs) are emerging as decision-making and interaction components. However, LLM integration ex...

Checkpoints Are Not Enough: Trust Calibration in CoSLR, a Human-AI System for Systematic Literature Reviews

CoSLR is presented, a Human-AI collaborative multi-agent system that supports the SLR workflow through a modular three-phase pipeline using large language models and Retrieval-Augmented Generation, and that places explicit, mandatory human checkpoints on the path between generated output and its acceptance.

The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas

A research agenda is proposed that reorients IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational routines, societal values, and regulatory institutions – emphasizing hybrid human-AI ensembles, situated validation, design principles for probabilist...

CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology

While LLM-based multi-agent systems show potential for large-scale software development, successful integration requires addressing challenges such as memory limitations, hallucinations, and code smells, alongside a practitioner-centric perspective.

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned

This work designs and operationalizes a governance-aware, multi-tenant AI sandbox that supports structured experimentation and produces reusable evaluation evidence across stakeholders and yields lessons learned and practical considerations that inform deployment and future evolution of governance-aware sandbox platfor...

TERE4AI: A Knowledge Graph-Based Tool for Generating EU AI Act Compliant Requirements

AI Sandbox: Technical Report

This work presents the design and implementation of a governance-aware, multi-tenant AI sandbox for structured experimentation and the generation of reusable evaluation evidence across projects and stakeholder groups.

LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review

A Multi-Vocal Literature Review is conducted, combining insights from both academia and industry, including peer-reviewed studies and grey literature to systematically synthesize and analyze existing knowledge on LLM-based multi-agent systems for code generation.

Navigating Compliance

Agentic Frameworks for Reasoning Tasks: An Empirical Study

This study provides the first large-scale empirical comparison of agentic frameworks for reasoning-intensive software engineering tasks and shows that framework selection should prioritize orchestration quality, especially memory control, failure handling, and cost management.

Autonomous Agents in Software Development: A Vision Paper

The vision is to leverage the capabilities of multiple GPT agents to contribute to SE tasks and to propose an initial road map for future work, arguing that multiple G PT agents can perform creative and demanding tasks far beyond coding and debugging.