International Journal of Artificial Intelligence and Agent Systems
Building robust, trustworthy, and autonomous artificial intelligence systems requires an integrated approach that recognizes the close interdependence between AI models, intelligent agents, ...
A diverse and international author and reviewer network
Real-World Impact
Research that advances theory and practice
Int. J. Artif. Intell. Agent Syst.
A global platform for research at the intersection of intelligent systems and data science.
Building robust, trustworthy, and autonomous artificial intelligence systems requires an integrated approach that recognizes the close interdependence between AI models, intelligent agents, computational infrastructure, human oversight, and real-world deployment environments. Advances in artificial intelligence, deep learning, generative AI, large language models, and agentic systems are increasingly transforming science, industry, governance, and society, making it essential to address not only model performance and autonomy but also safety, transparency, reliability, and long-term societal impact. This includes developing AI systems that can reason, learn, collaborate, and adapt under dynamic conditions while remaining aligned with human values, operational constraints, and ethical principles.
Achieving meaningful progress in artificial intelligence and autonomous systems demands attention to foundational challenges such as model robustness, explainability, generalization, alignment, computational efficiency, and responsible deployment. Equally important are broader considerations involving AI governance, safety evaluation, accountability, human-AI collaboration, access to computational resources, and the societal implications of increasingly capable intelligent systems. Addressing these challenges requires interdisciplinary collaboration that combines advances in AI theory, algorithms, architectures, and agent design with rigorous empirical validation and real-world applications.
The International Journal of Artificial Intelligence and Agent Systems (IJAIAS) provides a dedicated scholarly platform to advance these objectives. With a multidisciplinary focus, the journal brings together perspectives from artificial intelligence, deep learning, generative AI, foundation models, large language models, natural language processing, computer vision, reinforcement learning, agentic systems, multi-agent systems, autonomous systems, and trustworthy AI to deepen understanding of how intelligent and autonomous systems can be designed, evaluated, governed, and deployed responsibly. IJAIAS supports research that bridges foundational innovation and practical implementation, encouraging contributions that demonstrate both scientific rigor and real-world impact.
Through its editorial standards and publishing practices, IJAIAS actively promotes reproducibility, transparency, ethical responsibility, and rigorous peer review. The journal also seeks to contribute to broader global priorities by supporting research aligned with responsible AI development, digital transformation, human-centered innovation, AI safety, and trustworthy autonomous systems, recognizing the increasingly significant role that artificial intelligence and agent-based technologies play in shaping resilient, sustainable, and equitable futures.
Publisher: Who Supports This Journal?
The International Journal of Artificial Intelligence and Agent Systems (IJAIAS) is published and supported by Femington, an independent academic publishing organization committed to advancing open, ethical, and high-quality scholarly communication.
Femington ensures that IJAIAS operates on transparent, community-driven, and sustainable publishing principles. This open-source backend enables efficient manuscript management, rigorous peer review, and long-term digital preservation, while reinforcing the journal’s commitment to accessibility and academic integrity. The publishing model allows IJAIAS to prioritize editorial independence, research quality, and global knowledge dissemination rather than commercial publishing constraints.
IJAIAS aims to make academic research available:
Online
Immediately upon publication
Free from most copyright or licensing restrictions
Accepted Types of Articles
The International Journal of Artificial Intelligence and Agent Systems (IJAIAS) considers the following categories of scholarly contributions for publication:
Original Research / Research Articles
Review Articles
Case Studies
Research Notes
Editorials (by invitation or prior approval)
For detailed formatting requirements and the exact structure, authors are required to use the official journal manuscript template available below.
Manuscript Template (PDF):
for formatting guidance and Word-based manuscript preparation
LaTeX Template (ZIP):
includes complete LaTeX source files for manuscript preparation
Original Research / Research Articles
Original Research Articles report substantial and novel contributions to the field and are considered primary literature. These manuscripts should present original theoretical developments, methodological innovations, algorithmic advances, empirical analyses, or system-level implementations related to artificial intelligence, agentic systems, and autonomous technologies.
Submissions are expected to include clearly defined sections such as Introduction, Related Work, Methodology, Experiments or Evaluation, Results, and Discussion, along with a concluding section outlining implications and limitations. Articles are typically 6,000–8,000 words in length (excluding references). The research must demonstrate clear relevance to artificial intelligence, machine learning, deep learning, generative AI, foundation models, large language models, natural language processing, computer vision, reinforcement learning, agentic systems, or autonomous systems, with appropriate technical depth and rigor.
Review Articles
Review Articles provide a comprehensive and critical synthesis of existing research on a well-defined topic within artificial intelligence and autonomous systems. These articles should assess the current state of the field, identify gaps and challenges, and offer informed perspectives on future research directions. Review articles are regarded as secondary literature and are often widely read and highly cited.
Review manuscripts should generally be 6,000–8,000 words (excluding references) and must demonstrate analytical depth rather than a descriptive summary of prior work.
IJAIAS considers the following main types of review articles:
Critical Reviews:
Authors critically evaluate existing literature, highlighting theoretical, methodological, and empirical contributions, as well as unresolved challenges and limitations.
Systematic or Scoping Reviews:
Authors employ transparent, reproducible, and structured methodologies to identify, screen, and analyse relevant literature, with the aim of reducing bias and informing research or practice.
Meta-Analyses:
Authors use quantitative synthesis techniques to statistically aggregate findings from prior studies in order to derive robust conclusions about trends, effectiveness, performance, or capabilities across the literature.
Case Studies
Case Studies offer in-depth examinations of real-world applications, deployments, or implementations of artificial intelligence and autonomous systems. These manuscripts should focus on practical insights, lessons learned, and contextual factors influencing system design, performance, safety, adoption, and impact.
Authors are expected to follow key stages, including case definition, contextual background, data collection, system implementation, evaluation methodology, interpretation of findings, and implications for practice or research. Case studies are typically 3,000–4,000 words in length (excluding references) and must clearly demonstrate relevance to AI and intelligent systems in applied settings.
Research Notes
Research Notes are concise manuscripts presenting preliminary findings, exploratory analyses, novel datasets, benchmark evaluations, new agent architectures, emerging AI methodologies, or proof-of-concept systems that may not yet warrant a full-length article but are of clear scholarly interest.
These submissions should include a brief abstract and introductory section and may be written in a continuous format to maintain conciseness. Research Notes are generally 2,000–3,000 words (excluding references) and typically include a title, short background, methodology or approach, key results, and a brief conclusion. Research Notes are considered early-stage or proof-of-concept contributions.
Editorials
Editorials are typically commissioned by the Editors to address topical issues, emerging trends, policy developments, or strategic directions relevant to the journal’s scope. Authors interested in submitting an Editorial should contact the Editor-in-Chief (EIC) in advance to propose their idea for consideration.
Benefits to Authors
All articles published in the International Journal of Artificial Intelligence and Agent Systems (IJAIAS) are fully open access. This ensures that published work is available to read, download, and share worldwide.
Key benefits include:
Flexible publication fees for authors to ensure inclusivity
Rigorous double-blind peer-review process
Rapid editorial screening
Review decisions are typically communicated within six weeks
Fast and efficient online submission and review system
Authors retain copyright of their published work
The authors grant Femington a license to publish and identify itself as the original publisher.
3143-9977ISSN
3 daysSubmission to first decision (median)
Continuous PublishingFrequency
CC BY 4.0License
Announcements
Call for Papers – International Journal of Artificial Intelligence and Agent Systems (IJAIAS)
The International Journal of Artificial Intelligence and Agent Systems (IJAIAS) invites submissions for its inaugural issues from researchers, practitioners, and innovators working across artificial intelligence, generative AI, intelligent agents, autonomous systems, and related computational disciplines.
IJAIAS welcomes original research articles, comprehensive review papers, case studies, and applied or practice-oriented studies that present substantive contributions to AI theory, methodologies, architectures, systems, evaluation frameworks, or real-world deployment. Submissions may include, but are not limited to:
Novel algorithms, architectures, frameworks, or systems for artificial intelligence and intelligent automation
Research advances in machine learning, deep learning, reinforcement learning, and computational intelligence
Generative AI, foundation models, large language models (LLMs), and retrieval-augmented generation (RAG)
AI agents, agentic systems, multi-agent systems, and autonomous decision-making frameworks
Natural language processing, conversational AI, language understanding, and human-AI communication
Computer vision, multimodal AI, vision-language models, and perception systems
Research on autonomous systems, intelligent robotics, adaptive agents, and autonomous reasoning
Empirical studies supported by rigorous experimentation, benchmark evaluations, simulations, or real-world deployments
AI safety, explainability, fairness, robustness, alignment, governance, and responsible AI development
Human-AI collaboration, interactive AI systems, and augmented intelligence applications
Applied AI research addressing challenges in healthcare, finance, education, manufacturing, cybersecurity, public services, and other domains
Studies evaluating the deployment, performance, safety, or societal impact of AI and autonomous systems in real-world environments
Interdisciplinary research where artificial intelligence serves as a central component of scientific, technological, or organizational innovation
Critical reviews, systematic reviews, scoping reviews, and meta-analyses that synthesize and evaluate developments within the journal’s scope
The journal particularly encourages submissions that emphasize scientific rigor, methodological transparency, reproducibility, responsible AI practices, and real-world impact, while clearly articulating their contribution to advancing the state of the art in artificial intelligence and autonomous systems.
Contributions from both academic and industry research communities are welcome, provided they meet high scholarly standards and offer meaningful, verifiable, and reproducible insights.
Submission Status: Open Review Model: Double-blind peer review Publication Model: Open Access
Multi-turn Retrieval-Augmented Generation (RAG) agents are increasingly relied upon to integrate conversational history, external knowledge retrieval, tool use, and persistent memory. However, their failures extend beyond isolated hallucinations to a more systemic and gradual degradation we term state
Healthcare institutions produce enormous quantities of medical data daily. However, regulations such as HIPAA, GDPR, and DPDPA create strict barriers to sharing patient information for collaborative artificial intelligence development. This forces individual hospitals to develop models using only their local data,
This paper introduces CGMTorch, a novel framework leveraging gradient-based optimization for designing computational granular metamaterials. These materials, composed of granular particles with tunable properties, are optimized for dynamic responses to mechanical vibrations, enabling applications such as acoustic
Muskan Singh Pawar
September 2026 · Vol. 1 · Issue 2
CALL FOR PAPERS · VOLUME 4 / 2026
Publish research that moves intelligent systems forward
Share rigorous advances in autonomous agents, multi-agent coordination, and responsible AI with a global open-access research community.
