University of Kwazulu-Natal

The Rise Of Agentic Ai And The Architecture That Will Power It

Bonisiwe Shabane
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the rise of agentic ai and the architecture that will power it

For the last few years, most of the progress in AI has been tied to size. Bigger models, bigger datasets, bigger everything. And sure, that brought us a long way. But as we head into 2026, it feels like we’ve hit a point of diminishing returns. Models keep getting larger and demo videos keep getting flashier, but that doesn’t translate into real operational value for most companies. The gap between “cool prototype” and “this actually runs our business” is still too wide.

What is starting to move that line is the shift toward agentic AI. Instead of waiting for a prompt and producing a single answer, these systems operate more like persistent software components that chase a goal, react to new information, and adjust as they go. It’s a very different mindset from what we’ve been building toward over the last decade, and it requires us to rethink the architecture around AI – not just the models themselves. Generative AI changed how people interact with computers, but the loop hasn’t changed much. You ask, it answers, and the conversation resets. Agentic systems don’t behave that way.

They take in live data, watch for changes, make decisions, and revise them if things don’t play out as expected. Think of problems that don’t fit neatly into a single step: customer journeys that unfold over days or weeks, inventory levels that fluctuate by the hour, fraud patterns that evolve in real time. These aren’t “give me an answer once and I’m done” problems. They’re ongoing loops. The surprising part is that the bottleneck isn’t the model. It’s the architecture around it.

If an agent doesn’t have the right data, or the data doesn’t agree across systems, the agent ends up making the wrong call, quickly and confidently. This report is a collaborative effort by Alexander Sukharevsky, Dave Kerr, Klemens Hjartar, Lari Hämäläinen, Stéphane Bout, and Vito Di Leo, with Guillaume Dagorret, representing views from QuantumBlack, AI by McKinsey and McKinsey Technology. We’re at a moment when gen AI has entered every boardroom, but for many enterprises, it still lingers at the edges of actual impact. Many CEOs have greenlit experiments, spun up copilots, and created promising prototypes, but only a handful have seen the needle move on revenue or impact. This report gets to the heart of that paradox: broad adoption with limited return. The current diagnosis is this: Today, AI is bolted on.

But to deliver real impact, it must be integrated into core processes, becoming a catalyst for business transformation rather than a sidecar tool. Most deployments today use AI in a shallow way—as an assistant that sits alongside existing workflows and processes—rather than as a deeply integrated, engaged, and powerful agent of transformation. Agentic AI is the catalyst that can make this transition possible, but doing so requires a strategy and a plan to successfully power that transformation. Agents are not simply magical plug-n-play pieces. They must work across systems, reason through ambiguity, and interact with people—not just as tools, but as collaborators. That means CEOs must ask different questions: not “How do we add AI?” but “How do we want decisions to be made, work to flow, and humans to engage in an environment where software...

Redefining how decisions are made, how work is done, and how humans engage with technology requires alignment across goals, tools, and people. That alignment can only happen when openness, transparency, and control are central to your technology and implementation—when builders have an open, extensible, and observable infrastructure and users can easily craft and use agents with... That alignment creates the trust and effectiveness that is the currency of scalable transformation that delivers results rather than regrets. Capturing the full potential of agents will require modernizing enterprise architecture. By Pascal Gautheron, Chris Bell, and Stephen Hardy This article is part of Bain’s Technology Report 2025

Agentic AI isn’t just another wave of automation; it’s a structural shift in enterprise technology, one with the potential to completely redefine how work gets done. Previous waves of automation tackled parts of processes, leaving exceptions where humans had to step in. AI agents can reason, collaborate, and coordinate actions, allowing them to accomplish complex, multistep, nondeterministic processes that have so far depended on humans. It’s easy to see the transformative potential of this, from improved operational efficiency and customer experience to sharper decision making and beyond. Forward-looking leaders aren’t asking if agentic AI will reshape their business but how to prepare their organizations to deploy it safely and effectively. Bandi, A.; Kongari, B.; Naguru, R.; Pasnoor, S.; Vilipala, S.V.

The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges. Future Internet 2025, 17, 404. https://doi.org/10.3390/fi17090404 Bandi A, Kongari B, Naguru R, Pasnoor S, Vilipala SV. The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges. Future Internet.

2025; 17(9):404. https://doi.org/10.3390/fi17090404 Bandi, Ajay, Bhavani Kongari, Roshini Naguru, Sahitya Pasnoor, and Sri Vidya Vilipala. 2025. "The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges" Future Internet 17, no. 9: 404.

https://doi.org/10.3390/fi17090404 Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025). The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges. Future Internet, 17(9), 404.

https://doi.org/10.3390/fi17090404 This document presents a point of view describing the IT architecture that enterprises will need over the next 3-5 years to fully capture the value of an agentic workforce; it outlines the IT transformation... The goal is to provide a strategic guide and reference architecture to help CIOs, CDOs, and IT leaders plan their journey toward becoming an Agentic Enterprise. Powerful AI models are enabling the creation of an agentic workforce capable of sensing the environment, reasoning about data, making autonomous decisions, performing tasks, and effectively collaborating with human workers. This new workforce promises a step-change in innovation, productivity, and agility, creating value for shareholders and customers. To realize this vision, organizations must undergo a business and IT transformation to become Agentic Enterprises.

Today, the traditional enterprise faces operational inefficiencies arising from information siloes, employees buried in manual work, misaligned incentives in organizational structures, and disjointed feedback loops between strategies and outcomes. These issues lead to suboptimal customer experiences, inefficient processes, and missed opportunities for growth. The Agentic Enterprise overcomes these limitations by integrating a digital workforce of intelligent AI agents with human workers. With this new AI-augmented workforce, an organization can foster innovation for growth, drive operating excellence, and build enterprise resilience with several types of new business capabilities. New business capabilities to foster innovation: You have full access to this open access article

Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbolic models—a practice known as conceptual retrofitting. This survey cuts through this confusion by introducing a novel dual-paradigm framework that categorizes agentic systems into two distinct lineages: the symbolic/classical (relying on algorithmic planning and persistent state) and the neural/generative (leveraging stochastic... Through a systematic PRISMA-based review of 90 studies (2018–2025), we provide a comprehensive analysis structured around this framework across three dimensions: (1) the theoretical foundations and architectural principles defining each paradigm; (2) domain-specific implementations... Our analysis reveals that the choice of paradigm is strategic: symbolic systems dominate safety-critical domains (e.g., healthcare), while neural systems prevail in adaptive, data-rich environments (e.g., finance). Furthermore, we identify critical research gaps, including a significant deficit in governance models for symbolic systems and a pressing need for hybrid neuro-symbolic architectures. The findings culminate in a strategic roadmap arguing that the future of Agentic AI lies not in the dominance of one paradigm, but in their intentional integration to create systems that are both adaptable...

This work provides the essential conceptual toolkit to guide future research, development, and policy toward robust and trustworthy hybrid intelligent systems. Avoid common mistakes on your manuscript. The field of artificial intelligence (AI) is undergoing a paradigm shift from the development of passive, task-specific tools toward the engineering of autonomous systems that exhibit genuine agency. Modern Agentic AI systems (Wissuchek and Zschech 2025; Viswanathan et al. 2025) are defined by capabilities such as proactive planning, contextual memory, sophisticated tool use, and the ability to adapt their behavior based on environmental feedback. These systems operate not as mere solvers but as collaborative partners, capable of dynamically perceiving complex environments, reasoning about abstract goals, and orchestrating sequences of actions—either independently or as part of a sophisticated multi-agent...

2024; Du et al. 2025). To establish a precise conceptual foundation, we distinguish between the field’s core concepts. An AI Agent (or a single-agent system) is a self-contained autonomous system designed to accomplish a goal. It operates primarily in isolation, though it may interact with tools and APIs. Its agency is defined by its autonomy, proactivity, and its ability to complete a task from start to finish independently.

In the rapidly evolving landscape of artificial intelligence, Agentic AI is emerging as a transformative force, empowering systems to act autonomously toward specific goals with minimal human intervention. Unlike traditional AI models that follow rigid scripts, Agentic AI often referred to as autonomous AI agents, mimics human-like decision-making to navigate complex, dynamic environments. At its core, Agentic AI is fundamentally defined by AI agents that decompose complex, multifaceted tasks into manageable subtasks, collaborate through orchestration mechanisms, and adapt dynamically in real-time. This shift from reactive tools to proactive partners is redefining efficiency across industries. Powered by large language models (LLMs), Agentic AI systems integrate perception, reasoning, and action to solve problems autonomously. For instance, while a generative AI like ChatGPT might draft an email, an Agentic AI agent could schedule the meeting, update calendars, and follow up all without prompts.

This autonomy stems from its ability to interact with external tools like APIs and databases, making it ideal for enterprise workflows where speed and adaptability are paramount. The architecture of Agentic AI is modular and scalable, comprising several interlocking components that enable seamless operation. This structure allows Agentic AI in LLMs to scale from single agents for routine tasks to swarms handling enterprise-wide initiatives, ensuring robustness in volatile business environments. The 2025 Artificial Intelligence and Business Strategy report, from MIT Sloan Management Review and Boston Consulting Group, looks at how organizations that are adopting agentic AI are gaining advantage while facing four distinct tensions. The research and analysis for this report was conducted under the direction of the authors as part of an MIT Sloan Management Review research initiative in collaboration with and sponsored by Boston Consulting Group. Executives have long relied on simple categories to frame how technology fits into organizations: Tools automate tasks, people make decisions, and strategy determines how the two work together.

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