Agentic AI, the systems powered by autonomous, general-purpose, goal-oriented AI agents, has rapidly moved from theoretical concept to reality. It has promised a major leap in automation and intelligent operation. Unlike traditional AI that simply follows a set of rules or provides recommendations, an agentic system can perceive and interpret inputs, reason, plan and execute multi-step actions with minimal human intervention. This shift is poised to redefine work across industries, from healthcare and finance to logistics and software development, among many others.
AI is expected to extend and scale human expertise to new levels of capability and efficiency by integrating large language models, structured and unstructured knowledge repositories, computational functions, operational tools and automated agentic systems. This should redirect human workers from repetitive, routine, attention-light or high-volume tasks, and allow them to focus on higher-order thinking for creative, analytical and strategic activities. The ability to automate complex workflows end-to-end will lead to significant improvements in operational efficiency.
In industries like healthcare, manufacturing and construction, among others, specialized agents are expected to reduce administrative overhead and improve productivity and accuracy in decision quality through continuous monitoring and consistent, data-driven decision-making, leading to substantial cost savings.
Agentic AI and How It Works
The fundamental benefits of agentic AI stem from its ability to emulate the intelligent, collaborative problem-solving processes of humans. Agents are typically allowed to respond and process a broad spectrum of tasks regardless of their specificity and scope.
When defined, Agentic AI Architecture is unconventional in the Computer Science realm since it rarely describes the underlying architecture and often exhibits ambiguity in how responses are generated and governed. The vast majority of AI Agents have an architecture surrounding a single AI process and conditionally adjust prompts and data access based on, typically, a simple analysis of a request. These agents function only with generalized or non–domain-specific inputs and lack the capability to interpret or execute complex, rule-based, or industry-specific tasks.
Retrieval-Augmented Generation & It’s Limitations
This approach is generally termed Retrieval Augmented Generation (RAG). RAG represents a foundational mechanism within Agentic AI, enabling relevant information retrieval and context enrichment. Advanced agentic systems extend beyond RAG by integrating computational tools, function invocation and autonomous task execution based on AI-generated instructions, reasoning and decisions.
Retrieval-augmented generation promises significant benefits when integrated into AI agents, essentially transforming them from static models into dynamic, knowledgeable and more reliable tools. The combination of an AI agent’s ability to reason, plan and act with RAG’s ability to provide more accurate, current and verifiable context creates a powerful system known as Agentic RAG, which is ideal for complex, knowledge-intensive tasks in a business environment.
However, the uncontrolled or ungoverned nature of such autonomous operations introduces risks such as error propagation, omission of critical steps, contextual forgetfulness and other compounding inaccuracies, which can lead to catastrophic failures in production-grade, enterprise environments.
AI Swarms and Their Limitations
AI Swarms, also referred to as Swarm Intelligence have the potential to be revolutionary. It’s an approach where multiple independent AI agents work collaboratively and in a decentralized manner to solve complex problems.
When multiple agents are needed, typically one Agent is designed to determine which other agents it will assign the problem. So, basically One Problem = One Agent. Occasionally, in attempting to solve a complex problem, AI Agents are connected together to form a ‘swarm.’ That’s where each agent can invoke, send inputs to other agents as well as share information with them either in a planned sequence or to an agent of its choosing.
In almost all cases, AI Agents are allowed to respond to almost any prompt, often necessitating user training or prompt-engineering guidance leading to a typical requirement to train users on how they can talk with the AI agent in order to improve the probability of an accurate response. These agents may operate on identical or distinct underlying AI model architectures but are designed to emulate reasoning patterns, decision making behavior and domain expertise of human specialists. But, within the limits of their training data and contextual grounding. This limitation often manifests performance bottlenecks and increased dependency on prompt quality, input clarity and system governance on autonomous behavior.
The Challenge of Specialization That Breaks Agentics at Scale and Complexity
Due to the nature of having an open-ended conversation with a seemingly sentient agent, users and programmers typically add complexity to prompts, RAG content, data sources and requests until they find the AI system becomes highly inaccurate. Ideally it would be better to predict the complexity of a prompt plus RAG content before allowing it to be sent but that is unusual.
This leads to the expectation that agentic systems can break down vast, complicated goals, like managing a global supply chain or optimizing workflows, into manageable sub-tasks. The hope is that they can then carry out nuanced, adaptive decisions in real-time based on their understanding of the current situation. Unfortunately, that is not the case.
Despite its promise, the path to a fully realized agentic future underlines a significant challenge – the necessity of creating many highly specialized agents for any given complex goal. For a system to handle a multi-faceted problem, like determining where to send multiple pieces of construction equipment from various locations as projects end and new ones start, it cannot rely on a single, general-purpose agent.
Instead, it requires a multi-agent system where different agents possess narrow, deep expertise. This specialization is both a strength in that it allows for high-precision, deep-domain performance and a current limitation, as it creates an architecture of many moving parts.
The drawback is that developing, training and managing these numerous specialized agents for every distinct, complex workflow is resource-intensive and technically demanding. Additionally, once an agent performs a task, it has no inherent memory, and no capacity for learning, though there are some programming tricks to make it seem like it does, but this capacity is limited.
Significant Human Oversight is Required
A great deal of human oversight is required to ensure that information provided by AI agents is accurate, or that the right kind of information is being accessed or processed. This need for significant human intervention in complex, scaled scenarios appears to mitigate much of the savings potentially gained through agentics – or makes the increases in efficiency minor. And all this work has a cost in terms or finances, time and retraining of team members, among others.
While the power of agentic AI lies in teamwork. We are still in the early stages of teaching these AI agents how to be an effective, reliable team at scale. Until that time, and it could be some time, the agentic approach is not ready for anything more than very simple tasks within the enterprise.
About the Authors:
Ken Fischer is the CEO of Atigro, the proven ERP transformation firm that pairs its modular augmentation capabilities with AI-native frameworks. Atigro’s experience and capabilities generate the rapid development and provisioning of new enterprise software functionality that meets dynamically changing business processes.
Pranjal Biyani is a researcher and systems architect specializing in AI and cognitive decision systems. His work at Atigro focuses on designing and developing adaptive orchestration systems and AI governance frameworks that bridge deterministic and learning-based processes for AI based automation solutions.
