Agentic artificial intelligence (AI), characterized by autonomous, goal-oriented systems capable of inter-agent collaboration, is rapidly gaining traction as a transformative paradigm in the context of small, medium, and micro enterprises (SMMEs). The conceptual foundations and applied models of agentic AI have been thematically reviewed, but the structural and intellectual evolution of this domain needs further exploration. This bibliometric review builds on a previously conducted systematic literature review (SLR) of 66 studies and expands the analysis with an updated dataset of 214 papers published between 2019 and 2025, sourced from Scopus, Google Scholar, and credible gray literature repositories. The study uses VOSviewer and the Bibliometrix R package to create scholarly trend maps, identify prominent contributors, thematic clusters, and existing gaps in the literature. Findings reveal a sharp post-2022 surge, with 162 publications from 2023 to mid-2025, driven by developments in large language models (LLMs) and multi-agent systems (MAS). Key themes include foundational AI techniques such as LLMs and reinforcement learning (RL), strategic applications of agentic AI in SMMEs, decision-support systems, simulation-based modeling, and industrial implementations including Industry 4.0 and cyber-physical systems. Geographically, the majority of agentic AI research originates in the United States, China, and the United Kingdom, indicating a concentrated intellectual influence in affluent nations. In contrast, contributions from regions such as Africa and Southeast Asia remain limited. Substantial gaps persist, including limited empirical implementations in micro enterprises, underdeveloped AI governance frameworks, a lack of region and sector-specific adaptations, insufficient analysis of adoption and, fragmented interdisciplinary collaboration, guiding future research and policy in resource-constrained SMME contexts.

Agentic artificial intelligence (AI) systems are increasingly deployed in retail and supply chain operations, yet the empirical evidence for their efficacy has never been systematically quantified. This meta-analysis synthesizes 19 studies (total $$N = 5{,}292$$ ) to quantify the impact of agentic AI on operational performance and examine how contextual factors shape outcomes. Using a random-effects model with restricted maximum likelihood (REML) estimation and the Hartung-Knapp (HK) adjustment for confidence intervals, we found near-total heterogeneity across the included studies ( $$I^2 = 99.5\%$$ , $$\tau ^2 = 7.18$$ ), with a 95% prediction interval $$[-4.20,\ 7.36]$$ indicating that the true effect in any new setting could range from substantial harm to exceptional benefit. Against this backdrop of extreme variability, the pooled Hedges’ $$g = 1.58$$ (95% CI $$[0.26,\ 2.89]$$ , $$p = 0.022$$ ) should not be read as a general implementation expectation but rather as an average masking fundamentally divergent context-specific effects. Subgroup analysis by operational domain (retail, supply chain, e-commerce, inventory) identified domain-level differences under the common-effect model ( $$Q = 935.45$$ , $$df=3$$ , $$p < 0.001$$ ), though these differences were not statistically significant under the more appropriate random-effects model ( $$Q = 6.73$$ , $$df=3$$ , $$p = 0.081$$ ); domain estimate based on three or fewer studies (e-commerce, $$k = 3$$ ) are descriptive only and do not support inferential conclusions. The limit meta-analysis adjusted the estimate downward to $$g = 1.26$$ ( $$p = 0.066$$ ). These findings show that the current evidence, while statistically promising on average, is highly heterogeneous and subject to meaningful selective reporting. Practitioners should prioritize domain-specific pilots, distinguish simulation-based evidence from real-world evidence, attend to AI architectural differences, and interpret published effect sizes with considerable caution. Future research must prioritize real-world field experiments, architecture-specific evaluations, and publication of null results, along with the adoption of validated quality frameworks, to strengthen the reliability and credibility of the evidence base.