Agentic AI Frameworks in SMMEs: A Systematic Literature Review of Ecosystemic Interconnected Agents

Abstract

This study examines the application of agentic artificial intelligence (AI) frameworks within small, medium, and micro-enterprises (SMMEs), highlighting how interconnected autonomous agents improve operational efficiency and adaptability. Using the PRISMA 2020 framework, this study systematically identified, screened, and analyzed 66 studies, including peer-reviewed and credible gray literature, published between 2019 and 2024, to assess agentic AI frameworks in SMMEs. Recognizing the constraints faced by SMMEs, such as limited scalability, high operational demands, and restricted access to advanced technologies, the review synthesizes existing research to highlight the characteristics, implementations, and impacts of agentic AI in task automation, decision-making, and ecosystem-wide collaboration. The results demonstrate the potential of agentic AI to address technological, ethical, and infrastructure barriers while promoting innovation, scalability, and competitiveness. This review contributes to the understanding of agentic AI frameworks by offering practical insights and setting the groundwork for further research into their applications in SMMEs’ dynamic and resource-constrained economic environments.

1. Introduction

Artificial intelligence (AI) has advanced rapidly in recent years with the emergence of large language models (LLMs) and large multimodal models (LMMs). The growing interest in agentic AI centers on autonomous, goal-driven systems using LLMs and LMMs for intelligent reasoning, decision-making, and interaction. Unlike task-specific AI, agentic AI creates decentralized ecosystems of collaborative agents that adapt with minimal human supervision. These frameworks enable complex problem solving and promote resilience across distributed systems, making them particularly relevant for SMMEs. Agentic AI frameworks allow SMMEs to implement scalable solutions through multi-agent systems (MAS) for inventory management, customer relationship management and financial forecasting which enable agents to perform specific tasks while optimizing processes through coordination.

The retail sector uses agentic AI to analyze historical data for customer preference prediction which results in customized marketing approaches that build customer loyalty [1,2]. The travel industry benefits from AI agents which predict flight delays to optimize rebooking procedures and enhance customer satisfaction [3]. The applications have generated cost reductions and enhanced service quality across different industries but results depend on both implementation methods and specific industry settings [4]. AI-powered accounting tools perform automated invoicing and financial reconciliation tasks which enable SMMEs to dedicate their time to growth-oriented activities [5].

Despite these benefits, SMMEs have particular difficulties implementing agentic AI due to limited resources, technical expertise, and scalable infrastructure [6,7]. Most studies focus on large enterprises, overlooking the unique needs of SMMEs. The responsible adoption of agentic AI depends on ethical considerations, which include both predictive model bias mitigation and data privacy protection, yet these aspects receive insufficient attention in this field. The barriers to agentic AI adoption need thorough understanding while ethical governance frameworks must be established to maximize its potential in SMMEs. Agentic AI enables SMMEs to transform their operational frameworks and business models, which leads to innovation and scalability and competitiveness in fast-evolving digital markets.

This systematic literature review (SLR) synthesizes existing research on agentic AI systems, analyzing their features, implementations, and impacts across automation, decision-making, and ecosystem collaboration. The findings demonstrate that agentic AI fosters innovation, scalability, and competitiveness in SMMEs while also facing technological, ethical, and infrastructural challenges. This study advances the understanding of agentic AI frameworks by delivering practical insights, thereby establishing a foundation for future research on their economic implications within constrained and dynamic environments. This SLR functions as a fundamental academic research tool to provide a thorough and unbiased evaluation of all available literature about the studied topic [8], based on the following research questions:

• RQ1: What are the recent advancements and trends in agentic AI research?
• RQ2: How does agentic AI differ from traditional AI in business contexts?
• RQ3: What frameworks are available for the implementation of agentic AI in SMMEs?
• RQ4: What are the barriers and enablers to adopting agentic AI in SMMEs?

The organization of the next sections of the study is as follows. We present the historical context of agentic AI in Section 2, and Section 3 discusses the research methodology, while Section 4 presents the findings. In Section 5, we discuss the significance of the findings from the study, and Section 6 provides a conclusion and considers further studies.

2. Background to the Study

This section explores how agentic AI emerged to support SMMEs by explaining its historical development and essential principles and its role in economic systems. The development of AI technology follows a path from traditional models to autonomous collaborative agentic systems which rely on decentralization and inter-agent communication and context awareness as core principles. Additionally, it examines the ecosystemic viewpoint on agentic AI for small, medium, and micro-businesses and their contribution to economic growth.

2.1. Evolution of AI and the Emergence of Agentic Systems

AI has developed from rule-based systems in the mid-20th century to agentic AI frameworks capable of autonomous reasoning and collaboration. The 2000s brought machine learning (ML) as a substitute for symbolic AI and expert systems because these systems operated through predefined rules, yet ML achieved pattern recognition without autonomy [9]. The 2010s brought MAS which coordinated simple tasks but needed extensive programming [10,11]. The modern agentic AI system uses LLMs together with LangGraph for modular architecture [12] and AutoGen for multi-agent coordination [13], to dynamically break down problems with minimal human intervention. The resource constraints of SMMEs make agentic AI a particularly suitable approach for achieving scalable automation, offering a level of flexibility and decentralization that traditional monolithic AI systems often lack.

The emergence of agentic AI based on the MAS framework results from advancements in decentralized architectures [11], LLMs [14], and reinforcement learning (RL) [15]. Agentic frameworks differ from traditional AI systems because they utilize coordinated multi-agent architectures to achieve ecosystemic interoperability [16,17]. The coordination ability addresses the critical integration challenges faced by SMMEs as they need to integrate technology efficiently with limited resources and infrastructure [6,18]. These systems demonstrate goal-driven autonomy and context-aware adaptation to dynamic SMME environments [19] and modular scalability for incremental implementation [20]. The implementation of agentic AI technology results in substantial operational cost reductions through automation [21], which enables SMMEs to construct expanding agent networks that match their growth requirements, thus changing how small businesses use AI in South African resource-limited environments.

2.2. Core Principles and Architecture of Agentic AI for SMMEs

The ecosystemic nature of agentic AI emerges from its modular LLM-based architecture [12,14] and SMME-relevant principles as described in [22,23]. The brain functions as the decision-making center, while perception handles environmental data processing, and action executes tasks including inventory management optimization, demand forecasting through predictive analytics [24] and automated customer engagement processes [25]. The capabilities of this system prove highly beneficial for SMMEs because they need efficient technological solutions due to their resource limitations [23]. The operation of this system in SMMEs follows four key principles:

• Autonomy: The ability of autonomous decision-making through agentic AI systems allows agents to modify their environment and enhance their strategies in real time. The system needs autonomy to perform tasks such as demand forecasting where agents can function independently and reduce the need for supervision [26].
• Inter-Agent Communication: The system functions through communication protocols which allow agents to work together effectively for their activities. The system enables agents to share information while creating shared goals and performing coordinated inventory management and procurement operations [24,27].
• Decentralization: The decentralized structure of agentic AI systems distributes processing and decision-making functions across various agents, which reduces infrastructure expenses and allows for better scalability [28]. The system enables SMMEs to handle resources efficiently while adapting to changing market requirements.
• Collaboration: The agentic AI system enables agents to form dynamic partnerships that allow them to redistribute workloads and optimize operations through changing circumstances [29]. The collaborative process serves as an essential requirement for reaching common targets and achieving maximum operational efficiency.

These principles establish a unifying theoretical model grounded in MAS theory to explain how autonomous agents using LLM components interact through decentralized platforms to form adaptive ecosystems. The MAS-based model presented in Figure 1 allows SMMEs to create scalable systems through the deployment of single-function agents which expand into integrated ecosystems to boost resource-limited operational efficiency.

2.3. Ecosystemic Perspective on Agentic AI

Agentic AI systems transform operations through their built-in ecosystemic coordination ability, which enables sales, inventory and procurement agents to work together using standardized protocols like LangGraph’s orchestration framework [12]. The MAS principles support decentralized coordination through shared protocols in preference to centralized control [11]. The connected system allows sales agents to modify prices through real-time inventory information, while procurement agents perform autonomous order rerouting during supplier disruptions [19,30]. The modular design enables SMMEs to start with single-function agents before expanding to complete ecosystems, which results in lower initial expenses than monolithic AI systems [31]. SMMEs can scale their operations through agentic AI implementation because agents independently handle dependencies between internal operations and external stakeholders.

The ecosystemic model provides equal access to automation through its solution of infrastructure challenges. Edge-computing agents [32] provide offline functionality in areas with limited connectivity, and AutoGen [13] serves as a lightweight framework that reduces computational requirements. These adaptations enable SMMEs to match larger enterprises through agile data-driven ecosystems. The agentic AI system breaks down traditional silos to establish self-optimizing networks, which enable specialized agents to work together for adapting to volatile conditions, thus achieving 4IR’s goal of scalable human-centric automation for small businesses.

2.4. The Role of SMMEs in Economic Development

SMMEs have been pivotal to global economic growth since the mid-20th century, driving job creation, innovation, and economic diversification [33], especially in Sub-Saharan Africa [34]. In South Africa, they account for 91% of formal businesses, 60% of employment, and 34% of GDP [35], with the National Development Plan (NDP) projecting they will generate 90% of new jobs by 2030 [36]. South African SMMEs face unique digital challenges because mobile-data costs are among the highest globally, while rural broadband access is limited and AI-ready talent is scarce [37]. The challenges faced by South African SMMEs match Wooldridge’s MAS theory [17], which shows how adaptive decentralized systems can handle fragmented resources through the principles now implemented in agentic AI modular structures [13].

Although national initiatives like the Department of Trade, Industry and Competition (DTIC) blended-finance scheme aim to bridge these gaps, uptake remains uneven due to complex eligibility criteria [38]. The OECD SME and Entrepreneurship Outlook 2023 [39], emphasizes that digital technology adoption by SMMEs remains essential for productivity growth because skill shortages and resource limitations prevent them from reaching their full potential. The introduction of agentic AI technology provides new solutions to overcome existing limitations. The ethical frameworks [40] provide equal access, while the MAS principles [11] help to create designs that withstand volatile operating conditions. Through the transformation of the digital divide into resilience opportunities, agentic AI strengthens SMMEs to promote inclusive growth in limited resource environments.

3. Methodology

Conducting an SLR is a cornerstone of academic research, as it ensures that new knowledge builds upon the existing body of literature [8]. This SLR is designed to provide a comprehensive, unbiased summary of research findings and to identify gaps in the field of agentic AI frameworks applied within the operational ecosystems of SMMEs. This review was performed following the PRISMA 2020 framework, which emphasizes transparency and replicability in the reporting process [41], to ensure a rigorous approach to identifying, selecting, and synthesizing existing studies. This methodology is particularly suited to addressing the interdisciplinary nature of research on agentic AI frameworks, where interconnected agents operate within the unique constraints and opportunities of SMMEs. The application of this approach enables the review to capture the nuances of ecosystemic interactions and technological advancements while adhering to academic rigor. This framework involves four key steps: (1) identifying research literature through database searches, (2) screening articles based on inclusion and exclusion criteria, (3) evaluating full-text articles for eligibility, and (4) including eligible studies, extracting pertinent data, and assessing their quality.

3.1. Protocol Registration

The SLR protocol was registered with the Open Science Framework (OSF) to ensure transparency and reproducibility. The protocol, detailing the search strategy, inclusion, exclusion criteria, and quality assessment framework, is available at https://doi.org/10.17605/OSF.IO/8RND9.

3.2. Identification

Search Strategy: The search strategy was carefully designed to identify and retrieve relevant academic articles, conference proceedings, and other scholarly materials. The selection process focused on the title, abstract, and keywords (TITLE-ABS-KEY) of potential publications. Boolean operators (AND and OR) were employed to refine the search process and ensure comprehensive coverage of the topic.

• The AND operator was used to ensure that all specified keywords in the search string were present in the search results, making the query more specific and targeted.
• The OR operator allowed flexibility by including records where at least one of the specified terms appeared, thereby broadening the search scope and capturing related terminologies.

To maintain consistency and rigor, the review targeted publications in English between 2019 and 2024 to capture recent advances in agentic AI, particularly following the rapid evolution of LLMs and MAS after 2018. Earlier research on RL and MAS, while foundational for agentic AI, primarily addressed theoretical or large-scale enterprise applications and lacks specificity to SMME contexts, which require scalable, cost-effective solutions enabled by modern AI frameworks.

All searches were conducted in December 2024. The search included journal articles together with conference papers and gray literature that included preprints, industry reports, and policy papers. The search included IEEE Xplore, ScienceDirect, Scopus, Springer (via SpringerLink), and Web of Science databases. The search for gray literature involved Google Scholar and institutional repositories. The literature search was further improved by using the snowballing technique [42]. The backwards search of key articles involved examining their reference lists, while the forward search identified subsequent articles that cited these key articles as shown in Figure 2. The same search string was used across all platforms to achieve uniformity, which resulted in the retrieval of extensive and relevant literature.

Search String: The search strings were carefully crafted using Boolean operators and domain-specific keywords to explore the relationship between agentic AI, autonomous systems, and small business contexts. These strings were applied across databases and gray literature sources to retrieve materials addressing the subject from various perspectives. The search strings included the following:

((“Agentic” OR “Autonomous” OR “Self-directed” OR “Independent”) AND (“Artificial Intelligence” OR “AI” OR “Machine Intelligence” OR “Intelligent systems”) AND (“SMMEs” OR “Small, Medium, and Micro Enterprises” OR “SMEs” OR “Small and Medium-sized Enterprises” OR “Micro, Small, and Medium Enterprises” AND (“Ecosystem” OR “Interconnected” OR “Network” OR “Collaboration”))

By employing the OR operator, the search also captured variations in terminology and alternate expressions of the concept. This approach ensured a balance between precision and recall, retrieving both highly relevant and contextually related studies.

Criteria for Search: Studies and gray literature published between 2019 and 2024 in peer-reviewed journals, high-impact conferences, or credible non-peer-reviewed sources, such as McKinsey, OECD, and others, were included.

3.3. Screening

Studies were selected based on predefined inclusion and exclusion criteria to ensure relevance to agentic AI frameworks in SMMEs.

Inclusion Criteria:

• Studies and gray literature published in English.
• Publications within the stipulated time frame (2019–2024), capturing agentic AI recent advancements.
• Studies or reports should focus on agentic AI and SMMEs, or incorporate agentic elements in these enterprises as a fundamental aspect of their approach.
• Peer-reviewed papers in conferences and journals, or credible gray literature.
• Full-text, open-access articles or accessible gray literature.

Exclusion Criteria:

• Non-English articles and industry reports.
• Studies lacking empirical or theoretical contributions.
• Non-credible gray literature. For example, promotional materials, blogs without a transparent methodology.
• Duplicate records.
• Articles that are inaccessible, have restricted material, or do not meet peer review or credibility requirements.

For example, a peer-reviewed study about AI in large enterprises failed to meet the criteria because it did not focus on SMMEs but a paper about LangChain for inventory management in small retail was included because it related to agentic AI in SMMEs. The Gartner report on AI automation in SMMEs was included because of its credible methodology while the vendor blog post was excluded because it lacked transparency. Non-English studies were excluded to ensure consistency in data extraction given the review team’s linguistic capabilities and resource constraints. The inclusion of gray literature addresses the limited peer-reviewed agentic and SMME-specific studies and mitigates publication bias by capturing practical implementations, such as industry reports on AI-driven efficiency. However, the non-English exclusion may introduce selection bias by underrepresenting research from non-English-speaking regions like Asia and Europe, as discussed in Section 5.5.

3.4. Eligibility Criteria

Study Selection: The screening and selection process adhered to PRISMA 2020 guidelines [41] to collect peer-reviewed studies and credible gray literature relevant to agentic AI frameworks in SMMEs. The database and gray literature search yielded a total of 5564 records. Specifically, IEEE Xplore contributed 938 records, ScienceDirect 473, Scopus 1412, Springer 1375, Web of Science 843, and gray literature sources, such as ArXiv, SSRN, Google Scholar reports, and policy papers, provided 450 records. In addition, backwards and forward reference searching using snowballing techniques resulted in 73 additional records.

Duplicate records were identified and removed using the Mendeley reference manager (version 2.128.0) and custom Python scripts (version 3.12.6). All flagged records were manually reviewed to eliminate false positives (studies misidentified as duplicates) and false negatives (undetected duplicates). A detailed evaluation of bibliographic details, especially volume numbers and pagination, together with keywords in each publication, helped identify duplicate research articles with precision. The duplicate removal process eliminated 1796 duplicate records from 3768 unique research articles. A temporal filter removed 2468 records before 2019 to focus on contemporary research, which left 1300 records available for screening.

The two reviewers screened all records against inclusion criteria outlined in Section 3.2, while maintaining a strong agreement in their results (Cohen’s kappa = 0.85) [43,44]. The reviewers reached consensus to resolve any disagreements that arose during screening. The initial 1300 records required removal of 1038 entries due to specific exclusion criteria, which included 570 irrelevant topics and 340 non-English text and 128 duplicate records that escaped initial deduplication, resulting in 262 articles and reports for full-text review. The full text assessment led to the exclusion of 196 articles because they did not meet the inclusion criteria, including 100 records that lacked SMME focus and 76 records without agentic AI content and 20 records with poor data quality originating from sources in the non-credible gray literature. Ultimately, 66 studies were included for the systematic review of the literature, which ensured a focused but comprehensive analysis. The multi-phase process is depicted in Figure 3, which details the number of records identified, screened, and included, with explicit reasons for exclusions at each stage. The database and gray literature search details and results are presented in

Table 1. Database search results.

Database	Initial Search Results	Screened Articles	Full-Text Assessed	Relevant Articles
IEEE Xplore	938	135	45	10
Science Direct	473	97	23	5
Scopus	1412	454	59	11
Springer	1375	307	42	5
Web of Science	843	126	31	7
Gray Literature	450	181	62	23
Snowballing	73	–	–	5
Total	5564	1300	262	66

. The research objectives received their targeted literature assessment through this structured method that followed PRISMA 2020 guidelines.

Study Quality Assessment: Quality assessment is a critical component of a systematic literature review, ensuring that findings are based on reliable, valid, and high-quality evidence. This study employed a structured quality assessment framework combining the Critical Appraisal Skills Programme (CASP) checklist [45] for peer-reviewed studies, and the AACODS (Authority, Accuracy, Coverage, Objectivity, Date, Significance) checklist [46] for gray literature, tailored to evaluate studies on agentic AI frameworks in SMMEs. The CASP checklist, consisting of five key questions (see

Table 2. Quality assessment checklist.

No	Question	Scoring Criteria	Example
QA1	Does the study satisfy the requirements for inclusion and exclusion?	Yes (3): Fully meets all criteria (e.g., English, 2019–2024, SMME-focused, peer-reviewed or credible gray literature). Partial (2): Meets most criteria but has minor deviations (e.g., partial SMME focus). No (1): Fails to meet key criteria (e.g., not SMME-focused).	Yes: A 2022 study on LangChain in small retail (meets all criteria). No: A study on AI in large enterprises (not SMME-focused).
QA2	Is the reporting comprehensible and consistent?	Yes (3): Clear, logical structure with consistent terminology and methodology. Partial (2): Minor inconsistencies or unclear sections (e.g., vague methods). No (1): Incomprehensible or highly inconsistent reporting.	Yes: Study with a clear methodology and consistent AI terminology. Partial: Study with unclear data collection methods.
QA3	What is the reliability of the findings?	Yes (3): Robust methodology (e.g., empirical data, statistical analysis, reproducible results or transparent gray literature methods). Partial (2): Methodology present but unclear or limited (e.g., small sample size). No (1): No methodology or unreliable findings (e.g., anecdotal evidence).	Yes: Empirical study or industry report with clear data sources. No: Theoretical paper with no data or methods.
QA4	Is the source credible?	Yes (3): Peer-reviewed in high-impact journal (Q1 quartile) or reputable gray literature source (OECD, Gartner, McKinsey, etc). Partial (2): Peer-reviewed in a less reputable journal (Q3 or Q4) or less authoritative gray literature. No (1): Not peer-reviewed or in a predatory journal.	Yes: Article in IEEE Transactions on AI (Q1 journal). Partial: Article in a Q3 conference or minor consultancy report.
QA5	Are the study’s findings in line with the primary objective?	Yes (3): Directly addresses agentic AI in SMMEs (e.g., implementation challenges, frameworks). Partial (2): Indirectly relevant (general AI in SMMEs, not agentic-specific). No (1): Unrelated to agentic AI or SMMEs.	Yes: Study on AutoGen’s application in SMME logistics. No: Study on traditional AI in large firms.

), assessed methodological rigor, relevance to SMMEs, and alignment with the review’s objectives for peer-reviewed studies. The AACODS checklist evaluated gray literature based on Authority, Accuracy, Coverage, Objectivity, Date, and Significance, ensuring credible non-peer-reviewed sources. This dual framework provided a standardized, objective evaluation, addressing potential subjectivity in quality appraisal [8].

Each peer-reviewed study was evaluated against the five CASP criteria: (1) adherence to inclusion and exclusion criteria, (2) clarity and consistency of reporting, (3) reliability of findings, (4) credibility of the publication source, and (5) alignment with the primary objective of exploring agentic AI in SMMEs. Gray literature was assessed for authority (reputable organization like Gartner), accuracy (clear methodology), and relevance to SMMEs. A scoring system was applied, where each question was rated as “Yes” (3 points), “Partial” (2 points) or “No” (1 point), with specific criteria that define each rating (see Table 2 for CASP; AACODS used similar scoring). For example, a peer-reviewed study scored "Yes" for QA3 (reliability of findings) if it used a robust methodology (e.g., empirical data with clear statistical analysis). In contrast, a gray literature report scored “Yes” for accuracy if it provided transparent data sources.

The total quality score for each study was calculated by summing the scores across all criteria, with a maximum of 15 points. A predefined inclusion threshold of 7.5 points (50% of the maximum score) was set to ensure rigor, and enhance the quality of evidence. Of 262 full-text articles and reports evaluated, 66 met or exceeded this threshold (43 peer-reviewed articles, 23 gray literature) and were included in the final analysis, as detailed in

Table 3. Quality assessment of selected studies.

Study ID	Author	Year	QA1	QA2	QA3	QA4	QA5	Total Score
S1	[14]	2023	3	2	2	3	2	12
S2	[47]	2024	3	3	3	3	3	15
S3	[27]	2024	3	3	2	2	3	13
S4	[48]	2024	3	3	2	3	2	13
S5	[49]	2023	2	3	2	3	2	12
S6	[50]	2021	3	3	3	3	3	15
S7	[51]	2023	3	3	2	3	3	14
S8	[52]	2022	2	2	3	3	2	12
S9	[53]	2021	2	2	3	2	2	11
S10	[54]	2024	3	3	2	3	2	13
S11	[55]	2022	3	2	3	2	3	13
S12	[15]	2022	3	3	3	3	2	14
S13	[56]	2023	3	3	2	3	2	13
S14	[57]	2024	3	3	2	2	3	13
S15	[32]	2021	3	2	3	2	2	12
S16	[58]	2019	2	2	3	2	2	11
S17	[59]	2024	3	3	2	2	2	12
S18	[60]	2023	3	2	3	2	2	12
S19	[61]	2024	3	2	2	2	3	12
S20	[62]	2023	3	3	3	2	3	14
S21	[63]	2021	2	2	2	2	1	9
S22	[64]	2021	2	2	2	2	2	10
S23	[65]	2019	2	2	1	1	2	8
S24	[40]	2023	3	3	3	3	3	15
S25	[66]	2021	3	2	3	3	3	14
S26	[67]	2022	3	3	2	2	3	13
S27	[68]	2023	3	3	3	2	2	13
S28	[30]	2024	3	3	3	3	3	15
S29	[69]	2024	3	3	2	1	3	12
S30	[70]	2019	3	2	3	2	2	12
S31	[71]	2024	3	2	3	2	2	12
S32	[72]	2023	3	3	3	3	3	15
S33	[12]	2024	3	3	3	3	3	15
S34	[13]	2023	3	3	3	3	3	15
S35	[20]	2024	3	2	3	2	3	13
S36	[73]	2024	3	3	3	3	3	15
S37	[74]	2023	3	2	3	2	2	12
S38	[75]	2023	3	2	2	2	2	11
S39	[76]	2023	3	3	2	2	3	13
S40	[77]	2024	3	2	3	2	2	12
S41	[78]	2024	3	3	2	2	3	13
S42	[79]	2024	3	3	2	2	2	12
S43	[80]	2024	3	3	2	2	2	12
S44	[81]	2022	3	3	3	1	2	12
S45	[82]	2024	3	3	2	2	2	12
S46	[83]	2024	3	3	2	3	3	14
S47	[84]	2024	3	3	2	2	3	13
S48	[85]	2024	3	3	2	3	2	13
S49	[86]	2024	3	3	3	3	3	15
S50	[87]	2024	3	3	3	2	3	14
S51	[88]	2021	3	3	2	2	3	13
S52	[89]	2021	3	3	2	2	3	13
S53	[90]	2024	3	3	3	3	3	15
S54	[91]	2022	3	3	2	2	3	13
S55	[92]	2024	3	3	2	2	3	13
S56	[18]	2020	3	2	3	2	3	13
S57	[93]	2024	3	3	2	2	2	12
S58	[39]	2023	3	3	3	2	3	14
S59	[94]	2022	3	3	2	2	3	13
S60	[95]	2024	3	3	3	2	3	14
S61	[96]	2024	3	3	3	2	3	14
S62	[97]	2024	3	3	3	2	3	14
S63	[98]	2024	3	3	2	2	3	13
S64	[99]	2022	3	3	2	2	3	13
S65	[100]	2019	3	2	2	2	3	12
S66	[101]	2024	3	3	3	2	3	14

. The assessment process involved two reviewers who independently scored each study, achieving strong inter-rater reliability (Cohen’s kappa = 0.82 for peer-reviewed, 0.80 for gray literature). Discrepancies were resolved through discussion and consensus, ensuring objectivity and minimizing bias. This rigorous process confirmed the high quality of the selected studies, establishing a robust foundation for synthesizing evidence on agentic AI frameworks and their applications in SMMEs. It also highlighted gaps in the literature, such as a lack of empirical SMME-specific implementations, addressed partly by gray literature inclusion. The transparent quality checklist and scoring outcomes, presented in Table 2 and Table 3, ensure the findings are credible and practically applicable, reinforcing the study’s validity, credibility, and relevance to the field of agentic AI and SMMEs.

3.5. Inclusion Criteria

Data extraction and Synthesis: Following the quality assessment in Section 3.4, 66 studies were included for data extraction and synthesis. Data extraction focused on gathering information relevant to the research questions (RQs), including study characteristics (publication year, methodology), agentic AI frameworks (LLMs, MAS), SMME applications (retail, logistics), and implementation challenges (cost, training). Two reviewers independently extracted data using a standardized template in Covidence, achieving an inter-rater reliability of Cohen’s kappa = 0.80. Discrepancies were resolved through consensus to ensure consistency and minimize bias.

The extracted data were synthesized using thematic analysis in NVivo (v14) to identify recurring patterns and themes. For example, a peer-reviewed study on AutoGen in SMME logistics was coded under “technological enablers” and “logistics applications,” while a McKinsey report on AI efficiency was coded under “operational impacts” and “cost barriers.” Two researchers independently coded the data, with an inter-rater reliability of Cohen’s kappa = 0.83, resolving disagreements through discussion. Themes were mapped to the RQs: RQ1 (advancements in agentic AI) corresponded to themes like “LLMs” and “multi-agent systems”, while RQ4 (SMME implementation) linked to “financial constraints”, “skill gaps”, and “ecosystem integration.” The reviewed literature consisted of 65.2% peer-reviewed and 34.8% gray literature sources, providing a blend of theoretical perspectives and applied insights relevant to agentic AI in SMMEs.

The synthesized findings are presented in Section 4, supported by visual aids for clarity. Figure 6 highlights key terms such as “agentic AI,” “SMMEs”, “framework”, while Table 4 summarizes themes, codes, and representative studies, linking them to the RQs. This structured approach provides a comprehensive understanding of agentic AI frameworks in SMMEs, addressing the review’s interdisciplinary objectives.

4. SLR Results

This section presents the findings of the SLR on agentic AI frameworks in SMMEs, addressing four research questions (RQs) on advancements, applications, frameworks, and adoption barriers and enablers. Following PRISMA 2020 guidelines [41], results are organized into two subsections: an overview of included studies and a thematic synthesis of findings. Figures and tables summarize trends, quality assessments, and thematic insights to engage SMME stakeholders with practical, actionable outcomes.

4.1. Overview of Included Studies

The SLR included 66 studies (2019–2024): 43 peer-reviewed studies (65.2%), including 31 journal articles (47%) and 12 conference proceedings (18.2%), and 23 gray literature studies (34.8%). These were sourced from repositories like ArXiv, SSRN, and Google Scholar-indexed preprints and reports. The gray literature, assessed using the AACODS framework [46], mitigates publication bias by including practical insights. Figure 4 visualizes the distribution, highlighting the balance of scholarly rigor and industry perspectives.

Figure 5 shows the year-wise distribution: 4 studies (2019), 1 (2020), 8 (2021), 8 (2022), 14 (2023), and 31 (2024), peaking in 2024 (46.97%). This reflects growing research interest in agentic AI for SMMEs, driven by advancements in LLMs and open-source frameworks.

The quality of included studies was assessed using a standardized framework adapted from the CASP checklist for peer-reviewed studies and AACODS for gray literature. Each study was evaluated across five quality assessment criteria (QA1–QA5): methodological rigor, relevance to SMMEs, clarity of findings, data validity, and applicability to agentic AI. Scores ranged from 1 (low) to 3 (high) per criterion, yielding a total score out of 15. Table 3 presents the quality assessment for all 66 studies.

4.2. Thematic Synthesis of Findings

This subsection synthesizes findings across the four RQs under thematic categories derived from a thematic synthesis of the 66 studies: advancements and trends, sector-specific applications and differences from traditional AI, agentic AI frameworks, and barriers and enablers to adoption.

Table 4. Thematic synthesis of SLR studies, mapping RQs to representative codes and key insights.

Theme	RQ	Representative Codes	Example Study	Key Insights
Advancements in Agentic AI	RQ1	LLMs, multi-agent systems, autonomy	Xi et al. (2023) [14], [S1]	LLMs enhance automation, but SMME applications need empirical focus.
Sector-Specific Applications	RQ2	Retail, logistics, flexibility	Pu et al. (2022) [67], [S26]	Agentic AI outperforms traditional AI in dynamic SMME sectors.
Frameworks	RQ3	AutoGen, LangChain, scalability	Wu et al. (2023) [13], Topsakal & Akinci (2023) [72], [S32,S34]	Open-source frameworks suit SMMEs but require setup expertise.
Barriers and Enablers	RQ4	Cost, skills, cloud computing	Oldemeyer et al. (2024) [86], [S49]	Cloud platforms mitigate costs; training addresses skill gaps.
Ethical Implications	RQ1, RQ4	Bias, job displacement, privacy	Watson et al. (2024) [93], [S57]	XAI and training mitigate ethical risks in SMMEs.

summarizes these themes, mapping them to RQs, representative codes, and example studies, providing a structured overview. Advancements in LLMs (RQ1) enable frameworks like LangChain (RQ3) to address financial barriers (RQ4) via low-cost automation, as seen in retail applications (RQ2) [72]. However, skill gaps (RQ4) limit success in micro-enterprises, underscoring ethical concerns such as job displacement and the need for training.

The word cloud in Figure 6 highlights key themes in agentic AI frameworks for SMMEs, with prominent terms like “agentic”, “AI”, “framework”, “SMME”, “system”, and “agent” reflecting the focus on automation and operational improvements. The terms such as “autonomous” and “environment” align with the ecosystemic operation of agentic AI systems. The relatively smaller emphasis on “ethics” indicates a potential gap in research regarding governance and responsible AI practices within SMME contexts. This visualization supports our analysis of AI-driven innovation and adaptability in SMMEs, offering a visual summary of the focus of the literature.

4.2.1. Advancements and Trends in Agentic AI (RQ1)

The current state of agentic AI research reveals a growing interest and varied applications, a rapidly evolving field, with a growing body of research with a focus on developing autonomous systems that can perform complex tasks independently. This technology combines the versatility of LLMs with the precision of traditional programming, enabling AI agents to make decisions, take actions, and interact with external environments. Agentic AI is increasingly recognized as a transformative force for SMMEs, enabling them to enhance efficiency, drive innovation, and improve customer engagement. These systems function more like skilled digital colleagues than traditional tools, allowing businesses to navigate complexity and uncertainty while automating routine tasks, which frees up human resources for more strategic endeavors.

One major advancement in agentic AI has been the development of LLMs [14,47] and their integration into MAS [27]. Models such as OpenAI’s GPT-4, DeepMind’s Gemini, and Anthropic’s Claude [48] have revolutionized the ability of AI agents to comprehend and produce human-like responses, supporting applications from customer service to logistics. Systems that enable agents to communicate and collaborate effectively incorporate these models, making them suitable for complex applications such as negotiation, multi-party coordination, and autonomous decision-making. Recent studies have demonstrated a significant potential for LLMs to achieve a level of thinking and planning skills comparable to those of humans. Humans have high expectations for autonomous entities that are able to sense their environment, make judgements, and act in reaction to those decisions [14,47,49].

Another notable trend is the application of RL methodologies to develop strategic language and decision-making agents specifically for SMME operations. This trend is exemplified by [50], who introduced an RL platform designed to address the unique constraints and requirements of SMMEs in logistics, demonstrating how RL frameworks can be adapted for practical business applications, and reinforcement learning with human feedback (RLHF) for training agentic AI systems [51]. Innovations like OpenAI’s advancements in RLHF have improved the alignment of agent behaviors with human values, making agents more reliable and trustworthy. A critical trend in agentic AI research is the development of explainable AI (XAI) for agent systems. With increased reliance on AI agents in sensitive areas such as healthcare, legal systems, and defense, the need for transparency has grown. XAI techniques aim to make AI decisions interpretable to humans, fostering trust and accountability [52,53]. In addition to explainability, robustness in agentic AI has gained attention as a crucial research area. Robust AI systems can maintain performance despite uncertainties, adversarial attacks, or data anomalies [54,55]. This has particular relevance for applications in critical sectors such as financial services and healthcare, where security breaches could have severe consequences.

The adoption of multi-agent reinforcement learning (MARL) represents another exciting development. MARL allows multiple AI agents to collaborate or compete within shared environments, learning optimal strategies through interactions with one another. This approach has been instrumental in simulating economic markets, traffic systems, and even multiplayer gaming environments. As shown in studies by [15,56], MARL facilitates the emergence of cooperative behaviors among agents, making it a powerful tool for addressing complex, multi-faceted challenges. The integration of multimodal Interaction in agentic AI represents a sophisticated category of interactive systems capable of perceiving visual, linguistic, and environmental data to generate meaningful embodied actions. Advancing these agentic AI systems within grounded environments can effectively address the issues of hallucinations and inaccuracies associated with large foundation models [57].

Moreover, the integration of agentic AI into edge computing has transformed how AI systems operate in real time [32,58]. Ethical considerations in the research of agentic AI have also become central, with increased efforts to align AI systems with societal norms and legal frameworks. Concerns around prejudice, data privacy, and the possible abuse of AI have prompted efforts to establish ethical standards and regulatory rules [93]. A significant trend is the emergence of generative AI (GenAI), which intersects with agentic AI in developing systems that can produce original outputs, including writing, graphics, and code. Generative models such as Stable Diffusion and DALL-E are being included into agentic systems to augment creativity and problem-solving capabilities [59,60]. The scalability of agentic AI systems has emerged as an essential area of research. As businesses adopt AI on a larger scale, ensuring that agentic systems can handle increasing complexity without sacrificing performance is critical. Cloud-based AI platforms, such as Microsoft Azure and Google Cloud AI, provide scalable infrastructures that support the deployment and management of agentic AI agents across diverse applications [61,62]. This scalability has been particularly beneficial for SMMEs, which can leverage these platforms to integrate AI without the need for extensive in-house resources. These advancements drive low-cost solutions, supporting SMME growth, though empirical SMME studies are limited.

4.2.2. Sector-Specific Applications and Differences from Traditional AI (RQ2)

AI has become a transformative force in business, enabling automation, enhanced decision-making, and improved operational efficiency. Within the AI landscape, a distinction is emerging between traditional AI and agentic AI. While traditional AI encompasses algorithms and systems designed for specific tasks under fixed conditions, agentic AI introduces a more dynamic, autonomous, and context-aware approach to addressing complex business challenges. Traditional AI systems typically excel in well-defined tasks, relying on structured inputs and rule-based logic. For instance, recommendation engines [63], predictive analytics models [64], and robotic process automation (RPA) tools [65] are hallmarks of traditional AI. These systems are designed to operate within predefined parameters and require significant human intervention for updates or modifications. Agentic AI, unlike traditional AI, autonomously learns and adapts to new scenarios without reprogramming [40].

Agentic AI systems are characterized by their ability to make decisions in real-time, handle uncertainty, and coordinate with other agents or systems, making them particularly valuable in dynamic and unpredictable business environments. This capability enables them to navigate complex and dynamic challenges across a wide range of applications. Traditional AI relies on static decision trees or pre-trained models to make predictions or classify data [66]. While these methods are efficient for repetitive tasks, they often struggle with unanticipated changes in business conditions. Agentic AI, on the other hand, leverages RL and MAS to make context-aware decisions [67]. For example, in supply chain management, traditional AI might optimize inventory based on historical demand patterns, whereas agentic AI can autonomously adjust strategies in response to real-time disruptions, such as supplier delays or fluctuating consumer demand. This adaptability allows businesses to remain agile and responsive, even in volatile markets.

Collaboration and interaction capabilities further distinguish agentic AI from its traditional counterpart. Traditional AI operates in isolation, performing tasks independently and requiring separate systems for different functions [68]. In contrast, agentic AI often functions within an ecosystem of interconnected agents, each specialized in a particular task but capable of collaborating to achieve shared goals [30,47]. For instance, in customer service, a traditional chatbot might provide predefined responses to queries, whereas an agentic AI-powered virtual assistant can seamlessly coordinate with other systems to resolve complex issues, such as processing refunds, scheduling appointments, or offering personalized recommendations. Explainability and transparency are additional factors that differentiate agentic AI from traditional AI in business contexts. Traditional AI models, particularly deep learning (DL) algorithms, often face criticism due to their “black box” nature, making decisions difficult for stakeholders to comprehend [69,70].

Agentic AI addresses this challenge by incorporating XAI techniques, which provide insights into the decision-making process [53]. For example, in financial services, traditional AI might flag a transaction as fraudulent without explaining the rationale, whereas agentic AI can provide detailed reasoning, such as identifying unusual spending patterns or geographic inconsistencies. This transparency fosters trust and compliance, particularly in regulated industries where accountability is paramount. Agentic AI’s ability to integrate seamlessly with emerging technologies [57] further highlights its distinction from traditional AI. While traditional AI can be integrated into existing systems, agentic AI thrives in environments that leverage cutting-edge advancements, such as the Internet of Things (IoT), blockchain, and augmented analytics. In smart manufacturing, traditional AI might optimize production schedules based on static parameters, whereas an agentic AI system can dynamically adjust operations in response to IoT sensor data, predicting equipment failures and minimizing downtime. This convergence of technologies amplifies the impact of agentic AI, driving business transformation and fostering innovation across industries. The scalability of agentic AI extends beyond operational efficiency to strategic decision-making [71].

Traditional AI systems are typically deployed to address specific, localized challenges, while agentic AI can operate across multiple levels of an organization, from tactical decision-making to long-term strategy formulation. In retail, traditional AI might predict sales trends for a single product line, whereas agentic AI can analyze macroeconomic indicators, consumer sentiment, and supply chain variables to recommend strategic initiatives, such as market expansion or product diversification. This holistic approach enables businesses to navigate uncertainty and capitalize on emerging opportunities.

Table 5. Comparison of traditional and agentic AI in SMME contexts.

Aspect	Traditional AI	Agentic AI	Cost Implications
Learning	Fixed algorithms	Adapts to new data	Lower via open-source tools
Autonomy	Human oversight needed	Independent decisions	Reduces labor costs
Flexibility	Rigid programming	Adjusts to changes	Minimizes rework costs
Collaboration	Isolated tasks	Interconnected agents	Enhances team efficiency
Transparency	Opaque decisions	Explainable decisions	Builds trust, no extra cost
Application	Data analysis	Real-time strategies	Broadens SMME capabilities

compares traditional AI and agentic AI in business contexts, highlighting the superior autonomy, flexibility, and collaboration capabilities of agentic AI, which underpin its effectiveness in SMME applications. Unlike traditional AI, which relies on predefined algorithms and requires significant human oversight, agentic AI adapts to dynamic environments, making it ideal for SMMEs with limited resources.

4.2.3. Agentic AI Frameworks (RQ3)

Agentic frameworks serve as tools for creating AI systems that can operate autonomously, manage self-directed workflows, and execute actions based on user inputs, data, or predetermined rules. These frameworks facilitate the creation of agents capable of comprehending natural language, interpreting intricate instructions, and executing a range of tasks autonomously. The agents utilize AI models, such as LLMs, to analyze prompts and perform actions like making application programming interface (API) calls, executing database queries, or automating user interfaces (UI). Understanding the key frameworks associated with agentic AI frameworks is essential for comprehending their functionalities, applications, and distinguishing features. The following sections explore these aspects in detail, highlighting their use cases and interconnected elements.

LangChain: An open-source framework that aims to streamline the development of robust AI agents utilizing LLMs for intricate, multi-step tasks. The platform offers a user-friendly interface for linking various models, tools, and external APIs, enabling the creation of sophisticated applications that can comprehend, analyze, and engage with a wide range of data sources. LangChain’s capacity to combine document retrieval, decision-making workflows, and tailored language processing pipelines makes it a valuable tool for developers aiming to build dynamic, context-aware AI agents across diverse sectors, including customer service and data analytics [72]. LangChain’s user-friendly interface suits SMMEs with limited technical expertise, but its setup complexity may challenge micro-enterprises compared to AutoGen’s modular design, which, despite documentation gaps, offers faster deployment for logistics tasks.

LangGraph: An advanced framework for AI agents that integrates language models with knowledge graphs, enabling the development of intelligent, data-driven agents proficient in comprehending and engaging with intricate information networks [12]. LangGraph is meticulously crafted to empower agents through structured data and contextual insights, making it exceptionally suited for applications demanding profound, domain-specific knowledge and relational comprehension, including research assistants, recommendation engines, and knowledge-based systems.

Microsoft AutoGen: This framework is open-source and is specifically engineered for the development of sophisticated AI agents and MAS. AutoGen, designed by Microsoft Research, offers a versatile and robust toolbox for the creation of conversational and task-oriented AI applications. The focus is on modularity, extensibility, and user-friendliness, allowing developers to build advanced AI systems with high efficiency [13].

CrewAI: This is a cutting-edge AI agent framework designed to facilitate teamwork and seamless interactions among various agents. It emphasizes the development of smart agents that collaborate, share responsibilities, and enhance their actions through real-time communication and decision-making [20]. CrewAI is great for situations where multiple agents need to work together in a shared space. It really boosts teamwork and cooperation among autonomous systems, making things more productive and helping workflows run smoothly.

Microsoft Semantics Kernel: An open-source AI framework created by Microsoft to facilitate the development of intelligent agents that use LLMs and external technologies for the automation and orchestration of complex operations. It enables developers to include language processing, task management, and memory functionalities into AI agents, therefore equipping them to handle a diverse array of activities with contextual awareness and continuity. The modular architecture of the Semantic Kernel facilitates the development of adaptable and intelligent systems capable of addressing a variety of challenges in practical applications [73].

Hugging Face Transformers Agents: Using the possibilities of transformer models, Hugging Face has developed the transformers agents framework. With this framework, developers can create, test, and implement AI bots able to perform complex natural language tasks under their direction [74]. Combining complex ML models and making them accessible via a single, simple-to-use API not only gives a strong basis for GenAI and natural language processing (NLP) applications but also makes it easier to build smart agents.

MetaGPT: A collaborative open-source framework for multi-agent engagement in organized activities. METAGPT assigns tasks like engineer, project manager, architect, and product manager to LLM-powered AI agents to simulate software industry workflows. Agents perform functions such as competitive analysis and code development, system design, content generation, data analysis, execution, quality assurance, collaboration, decision-making, user interaction, and self-learning to achieve task-specific goals [75].

Swarm by OpenAI: OpenAI Swarm is a Python framework designed for the orchestration of multiple AI agents, enabling collaborative functionality among them [76]. Rather than depending on a singular LLM instance for all functionalities, Swarm facilitates the creation of specialized agents that engage in communication and collaboration, akin to a team of experts possessing distinct skill sets.

Flowise: An open-source, low-code development environment to create custom LLM flows and AI agents. It offers a high-speed development experience and has a user-friendly drag-and-drop interface that allows users to design both conversational workflows and the agentic system in general. It supports integration with external tools, uses AI models like LLM, and is ideal for business automation and task orchestration [77].

OpenAGI: An AGI research platform that is open-source and capable of managing complex, multi-step tasks. Dynamic model selection, tool integration, and the incorporation of various models are all integrated. Supports advanced AGI research and experimentation by utilizing task feedback to self-improve [78].

The potential use cases and applications of agentic AI frameworks span a broad spectrum of fields and businesses, transforming how businesses operate and interact with customers. In customer service and support, autonomous agents can manage customer inquiries, decrease ticket response times, and provide bilingual help without continuous human supervision [79]. Similarly, in healthcare operations, agentic frameworks are automating tasks like claims processing and prior authorization, streamlining healthcare workflows and improving efficiencies [80,81]. Financial institutions are also leveraging agentic AI to track transactions swiftly, identify and curtail fraudulent activity, and execute high-frequency trading strategies [82]. Additionally, agentic AI systems are optimizing retail processes across sales, supply chain coordination, customer service, and logistics by adapting to real-time operational data, improving efficiency, and fostering competitiveness in SMMEs. According to recent industry reports, AI agents are helping small retailers automate complex workflows, reducing operational costs, streamline customer engagement, and respond dynamically to demand fluctuations [83,84,85].

Nine agentic AI frameworks were identified, including LangChain, AutoGen, CrewAI, and Microsoft Semantic Kernel. While Table 4 synthesizes thematic findings across all RQs,

Table 6. Comparative evaluation of agentic AI frameworks for SMMEs.

Framework	Cost	Scalability	SMME Suitability	Limitations
LangChain	Low (open-source)	High	User-friendly, ideal for small teams	Requires technical setup
AutoGen	Low (open-source)	Moderate	Modular, suits logistics	Limited documentation
CrewAI	Low (open-source)	Moderate	Strong for multi-agent collaboration	Moderate scalability
Semantic Kernel	Low (open-source)	High	Integrates LLMs and tools	Complex for non-technical users

specifically evaluates agentic AI frameworks’ suitability for SMMEs based on cost, scalability, and ease of use. LangChain, an open-source framework, is user-friendly and cost-effective, ideal for SMMEs with limited technical expertise, but requires initial setup [72]. AutoGen supports modular MAS for logistics but lacks extensive documentation, posing challenges for small teams [13]. CrewAI excels in collaborative tasks, suitable for SMMEs needing multi-agent coordination, though its scalability is moderate [20]. Semantic Kernel integrates LLMs with external tools, but its complexity may deter SMMEs without technical staff [73].

In essence, the agentic framework enables AI-powered applications to perform human tasks, such as creating reports, automating workflows, and interacting with APIs, by understanding user needs and automatically executing actions that align with those needs. These frameworks simplify sophisticated AI system development by providing reusable building blocks and established methodologies. This enables developers to focus on high-level applications, leveraging existing solutions rather than duplicating effort. For SMMEs, adopting modular frameworks such as LangChain or leveraging multi-agent orchestration tools like AutoGen offers a cost-effective and scalable pathway to harness the capabilities of agentic AI. These technologies enable businesses to transition from reactive operations to proactive, adaptive systems that can autonomously reason, plan, and act based on real-time data inputs. By integrating reasoning engines, memory modules, and external tool use, such frameworks allow SMMEs to implement AI agents for tasks like customer service automation, supply chain coordination, and dynamic sales optimization—unlocking strategic advantages in increasingly volatile and competitive markets.

4.2.4. Barriers and Enablers to Adoption (RQ4)

Adopting agentic AI in SMMEs presents both opportunities and challenges. The barriers and enablers influencing this adoption and potential benefits are discussed below.

Barriers to Adopting Agentic AI in SMMEs

Lack of Resources: SMMEs often have considerable resource limitations that impede their capacity to implement AI solutions. This includes financial limitations and insufficient computing infrastructure [86]. Many SMMEs struggle to justify the investment in AI due to high costs and the complexity of deployment, especially when a compelling business case is lacking.

Lack of Technical Expertise: In order to develop and implement agentic AI systems, specialist technical skills are required. A significant number of SMMEs do not possess the necessary in-house competence to create, deploy, and operate agentic AI systems [87].

Data-Related Issues: Agentic AI systems need significant quantities of high-quality data to operate efficiently. SMMEs may have challenges in obtaining, maintaining, and securing data, hence limiting their capacity to use AI capabilities [86].

Integration Challenges: Integrating AI solutions into existing workflows and business systems presents a considerable challenge, particularly for SMMEs operating on legacy systems [88]. These obsolete systems may lack compatibility with contemporary AI systems, necessitating thorough assessments and meticulous planning for successful integration. The complexity of these integration efforts can lead to significant delays and additional costs, discouraging SMMEs from pursuing agentic AI initiatives.

Cultural Resistance: One of the significant obstacles to adopting agentic AI in SMMEs is cultural resistance among employees. Many employees are concerned that AI might take over their jobs, which creates anxiety about their job security [87]. To combat these fears, companies need to engage in transparent communication, framing AI as a tool that enhances productivity rather than as a threat to employment. Initiatives aimed at involving employees in the AI implementation process can also help build trust and acceptance.

Regulatory Challenges: Regulatory compliance poses another barrier for SMMEs looking to adopt agentic AI systems. Many industries are subject to stringent regulations, and the increased size and complexity of these rules can create significant compliance challenges. Startups developing agentic AI solutions must ensure that their systems can effectively analyze regulations and determine compliance status. The uncertainty surrounding regulatory frameworks can add to the hesitance of SMMEs to adopt these technologies [86].

Enablers to Adopting Agentic AI in SMMEs

Cloud Computing: Cost-effective cloud-based AI solutions can significantly reduce the startup constraints for SMMEs, allowing them to harness the capabilities of AI without the need for substantial initial investment. Cloud-based AI platforms provide cost-effective access to computational resources and pre-trained models, thereby lowering financial and technical obstacles for SMMEs. Cloud platforms like Azure, Google Cloud and AWS offer access to pre-trained models, AI services, and scalable infrastructure, thereby enhancing the accessibility and affordability of AI for SMMEs [89].

Open-Source Tools and Technologies: The availability of open-source AI tools and libraries makes it easier for SMMEs to develop and deploy AI solutions. Open-source frameworks and libraries like LangChain and LangGraph ecosystem, AutoGen, among others provide access to powerful AI tools at minimal cost, making AI development more accessible to SMMEs with limited budgets [12,72].

Training and Upskilling Programmes: Building technical expertise within the organization is another crucial enabler for adopting agentic AI. SMMEs can bridge the skill gap by investing in AI training and upskilling their employees through dedicated programmes and workshops. Collaborations with technology partners can further inject necessary knowledge into teams, empowering SMMEs to effectively implement and utilize agentic AI solutions [90].

Collaboration and Partnership: SMMEs can collaborate with other businesses, research institutions, or AI vendors to access expertise, resources, and knowledge, facilitating the adoption of agentic AI [90].

Government Support: Government initiatives, such as financial support, and incentives from taxes, may promote the use of agentic solutions among SMMEs [87].

Benefits of Adoption of Agentic AI for SMMEs

Performance Optimization: Agentic AI enables organizations to maintain continuous operations without human supervision or heightened operational complexity, hence enhancing operational quality. In contrast to previous AI systems, agentic AI ensures constant quality while perpetually enhancing and adjusting according to present environmental factors and historical results. This facilitates expedited decision-making for firms and eliminates obstacles, resulting in more efficient and dependable operations [90].

Low Costs: Agentic AI, capable of precisely executing intricate tasks autonomously, may provide significant cost reductions. Utilizing agentic AI to automate normal activities enables businesses to decrease expenses while preserving service quality and expanding operations. The automation of regular operations enables organizations to redeploy personnel to more important duties [91].

Market Advantage: Agentic AI offers businesses a notable edge in the market by lowering expenses and enhancing operational efficiency. Rather than focusing on recruiting or upskilling staff, businesses can leverage agentic AI to implement data-driven strategies on a large scale. As autonomous AI systems evolve and enhance their capabilities, they hold the promise of taking over certain human roles, thereby assisting businesses in scaling and maintaining a competitive edge [92].

The adoption of agentic AI represents a game-changing opportunity to level the playing field against larger competitors for SMMEs. By addressing key barriers to adoption, such as costs, data availability, and skill gaps, SMMEs can capitalize on AI solutions to improve productivity, innovate processes, and drive economic growth. Moreover, as government initiatives encourage AI adoption, SMMEs that strategically integrate agentic AI could become leaders in their industries, thereby contributing to job creation and economic sustainability. Barriers to agentic AI adoption in SMMEs, include financial constraints, lack of technical expertise, data-related issues, integration challenges, cultural resistance, and regulatory compliance. Financial constraints limit investment in AI infrastructure, particularly for SMMEs [18].

Technical expertise gaps hinder development and deployment, with SMMEs lacking in-house AI skills [87]. Data quality and security issues impede AI performance, while legacy systems complicate integration. Cultural resistance stems from job displacement fears, and regulatory uncertainty discourages adoption. Enablers include cloud computing, open-source tools, training programs, partnerships, and government support. Cloud platforms like AWS and Azure reduce costs by providing scalable infrastructure. Open-source frameworks like LangChain lower entry barriers, while training programs bridge skill gaps.

Table 7. Mitigation strategies for agentic AI adoption in SMMEs.

Barrier	Enabler	Mitigation Strategy
Financial Constraints	Cloud Computing	Adopt cost-effective cloud platforms (e.g., AWS)
Lack of Technical Expertise	Training Programs	Partner with industries and universities for AI workshops
Data-Related Issues	Open-Source Tools	Use data-cleaning tools in frameworks like LangChain
Cultural Resistance	Leadership Support	Implement transparent AI communication plans

highlights mitigation strategies to address these barriers.

4.2.5. Ethical Implications

The adoption of agentic AI by SMMEs raises several ethical concerns, including algorithmic bias, job displacement, and data privacy. Bias embedded in LLMs can result in unfair decision-making and discriminatory outcomes, particularly in retail contexts, where biased customer profiling may disproportionately affect marginalized groups [93]. Concerns about job displacement are especially pronounced in micro-enterprises that often lack the financial and structural capacity to retrain affected workers [87]. In addition, many SMMEs operate with limited cybersecurity infrastructure, increasing the risk of data breaches and unauthorized access when deploying cloud-based AI systems [40]. To address these risks, several mitigation strategies are emerging. These include the use of XAI techniques to enhance transparency in decision-making, staff training in AI ethics and responsible use, and compliance with data protection regulations, such as the General Data Protection Regulation (GDPR). Recent industry reports underscore the importance of ethical literacy among SMME leadership and employees as a foundational step toward responsible AI integration [39,94].

5. Discussion

This section interprets the SLR findings on agentic AI frameworks in SMMEs, synthesizing results from Section 4 to address the four research questions (RQs). It explores real-world applications, implications, ethical considerations, and limitations, adhering to PRISMA 2020 guidelines.

5.1. Synthesis of Key Findings

Agentic AI’s advancements LLMs, MAS, RL, XAI offer transformative potential for SMMEs, (Table 4, RQ1). SMMEs face unique barriers like financial constraints [6], technical expertise gaps [87], and infrastructure access [86], necessitating tailored studies. The 2024 publication surge (Figure 5, n = 31, 46.97%) reflects growing interest in agentic AI. The decentralized architecture of agentic AI and the interagent communication (RQ2) enable agility, outperforming traditional AI centralized models (Table 5). Frameworks like LangChain and AutoGen (RQ3) create autonomous agent ecosystems, but lack SMME specific guidelines for budget and usability constraints. Adoption barriers, particularly financial constraints affecting SMMEs, are mitigated by cloud computing, training, open-source tools, and leadership (Table 7, RQ4). SMMEs should adopt an incremental implementation to enhance the uptake of technology, particularly for cloud-based AI solutions. The roadmap in Section 4.2.4 (assess needs, select tools, partner with cloud providers, train staff) offers practical guidance, although empirical validation is required.

5.2. Real-World Applications of Agentic AI for SMMEs

Agentic AI startups are rapidly transforming automation across industries in 2024–2025, offering scalable, low-code solutions that hold significant promise for SMMEs. Companies like Cognition Labs, Adept AI, and Hippocratic AI are at the forefront of this evolution, though challenges remain in ensuring affordability and usability for resource-constrained environments. Cognition Labs has advanced its AI agent, Devin, to version 2.0, positioning it as a fully autonomous software engineer. Devin 2.0 features an integrated development environment designed for AI agent collaboration and has demonstrated the ability to resolve 13.86% of issues end-to-end without human assistance, outperforming previous benchmarks [95]. While Devin’s capabilities are promising, its scalability and cost-effectiveness for SMMEs, particularly in emerging markets, require further evaluation.

Adept AI focuses on developing AI agents that can automate complex enterprise workflows through natural language commands. Their flagship model, ACT-1 integrates with various software applications, enabling tasks such as data extraction, document processing, and supply chain management [95]. Adept’s approach allows for quick setup and integration, making it a potential asset for SMMEs seeking to enhance productivity without extensive technical infrastructure. Hippocratic AI is pioneering the use of AI agents in healthcare, focusing on low-risk, non-diagnostic tasks like chronic care management and wellness coaching. Their agents are designed to alleviate physician burnout by handling administrative duties, with ongoing safety testing involving thousands of nurses and physicians. While promising, the implementation of such technology in SMMEs within the healthcare sector necessitates further study to assess its practicality and effectiveness. Hippocratic AI is pioneering the use of AI agents in healthcare, focusing on low-risk, non-diagnostic tasks like chronic care management and wellness coaching. Their agents are designed to alleviate physician burnout by handling administrative duties, with ongoing safety testing involving thousands of nurses and physicians. While promising, the implementation of such technology in SMMEs within the healthcare sector necessitates further study to assess its practicality and effectiveness [96,97].

5.3. Implications for SMMEs

Practitioners can leverage user-friendly frameworks like LangChain (Table 6) for cost-effective automation. Cloud platforms [98,99,100] and training partnerships (Table 7) address financial and skill barriers, enabling SMMEs to compete with larger firms. Developers should prioritize simplified interfaces for frameworks like Semantic Kernel, which is complex for non-technical users [73], to broaden adoption.

Researchers can benefit from the Table 5 comparison of agentic and traditional AI, which clarifies autonomy’s role in SMME innovation, extending prior work on AI adoption [88]. The thematic synthesis (Table 4) provides a model for studying barriers and enablers, but SMMEs specific empirical studies, especially in retail and healthcare, are needed to enhance generalizability. The high quality of included studies strengthens these contributions, though empirical gaps persist.

Policymakers should fund open-source development, training programs, and cloud subsidies to address barriers (Table 7). Regulatory clarity, noted as a challenge [40], could further accelerate adoption, aligning with web calls for supportive AI policies [101]. However, the adoption of these technologies by SMMEs is contingent upon factors such as cost, scalability, and the availability of user-friendly interfaces. Further research and development are essential to tailor agentic AI solutions that meet the specific needs and constraints of SMMEs, particularly in resource-limited settings.

5.4. Ethical and Socio-Economic Considerations

Building on findings in Section 4 on ethical implications, this subsection explores broader socio-economic impacts and policy frameworks to guide SMME adoption. Agentic AI’s adoption raises socio-economic and ethical concerns, linked to RQ4’s barriers and enablers. Workforce dynamics, such as reskilling needs due to automation, and inequality from access disparities in low-resource SMMEs, challenge equitable adoption. Ethical issues, including bias in LLMs and job displacement fears, require mitigation through XAI for transparency, and upskilling programs practical frameworks, like the roadmap in Section 4.2.4 (assess needs, select tools, partner with cloud providers, train staff), support incremental adoption.

5.5. Limitations

This review included 23 gray literature sources, such as Gartner’s automation insights and OECD’s SME policy reports, to mitigate publication bias and capture practical implementations relevant to SMMEs. This helped reduce over-reliance on the 43 peer-reviewed studies (65.2%), comprising journal articles and conference proceedings. However, the variable quality of gray literature may introduce heterogeneity. To address this, the AACODS checklist was applied to assess source credibility. Nonetheless, the absence of formal peer review in some gray literature remains a limitation. The exclusion of non-English studies due to resource constraints may also introduce selection bias, potentially omitting valuable findings from non-English-speaking regions, such as parts of Asia and Europe, where SMMEs operate under diverse regulatory and cultural conditions. Finally, had fewer than 10 studies met the inclusion criteria, the synthesis would have lacked sufficient depth, thereby limiting generalizability.

6. Conclusions and Future Work

The systematic literature review demonstrates that agentic AI frameworks represent an emerging disruptive paradigm which transforms how SMMEs handle operational and strategic and adaptive challenges. Agentic AI differs from traditional AI because it uses decentralized autonomous agents which operate in distributed dynamic contexts while maintaining collaborative capabilities. The scalability, modularity and adaptability of agentic AI systems make it particularly suitable for SMMEs because these features address their resource constraints and need for quick decision-making. The systems enable continuous process optimization, fast adaptation, and resilient operations through autonomous agents that independently perform complex tasks while sensing, acting, communicating, and learning in real time.

The review identifies key agentic AI capabilities, which include autonomy, inter-agent coordination, context-awareness, specialization, explainability and ethical decision-making, that match the strategic requirements of SMMEs operating in dynamic environments. The deployment of these systems becomes faster because of enabling technologies that include large LLMs and MARL together with open-source toolkits such as LangChain, CrewAI, LangGraph, Microsoft AutoGen. The benefits of agentic AI as a high-value investment for SMMEs include potential advantages, cost reduction, operational efficiency, increased competitiveness and intelligent automation. The review reveals that real-world adoption barriers consist of technical skill gaps, data limitations, legacy system integration, infrastructure costs, cultural resistance, and unclear regulatory landscapes.

The adoption of agentic AI becomes possible through cloud-based infrastructure, open-source accessibility, government-led support, cross-sectoral partnerships, and workforce upskilling programs. To address ethical concerns about bias and data privacy and job displacement, AI transparency needs continuous monitoring through XAI techniques and responsible AI training methods. Agentic AI offers practical solutions to help SMMEs achieve digital transformation. Future initiatives should prioritize simplifying framework usability while ensuring robust ethical practices, developing accessible tools, and building collaborative networks to reduce entry barriers. Research on agentic AI in SMMEs should strategically advance both theoretical frameworks and applied technologies. Key directions for further studies include:

6.1. Framework Development and Practical Adoption Models

• Develop implementation frameworks tailored to sector-specific constraints and opportunities within different SMME contexts (e.g., retail, healthcare).
• Design low-code or no-code agentic platforms and toolkits to empower non-technical users in SMMEs.

6.2. Cost-Efficient and Scalable Deployments

• Investigate lightweight agentic AI architectures suitable for resource-constrained environments.
• Explore the use of local large language models (LLMs) and serverless computing to enhance cost and energy efficiency.

6.3. Security, Privacy, and Compliance

• Integrate robust cybersecurity protocols directly into agentic AI frameworks.
• Ensure framework designs align with data governance regulations, such as GDPR, POPIA, and other relevant standards applicable to SMMEs.

6.4. Multi-Agent vs. Single-Agent Architectures

• Conduct empirical studies to evaluate the effectiveness, cost-efficiency, and maintainability of multi-agent versus single-agent systems for various SMME tasks.

These directions aim to bridge empirical gaps, promote accessible and scalable frameworks, and support ethical and equitable adoption of agentic AI in SMMEs.