Large language models (LLMs) have revolutionized our interaction with AI systems artificial intelligence. Their ability to understand and generate human-like text has given rise to a wave of applications, from sophisticated chatbots to powerful content creation tools. As these models become more capable, we’re seeing the emergence of AI systems often referred to as “agents.” However, within this developing landscape, it’s crucial to differentiate between what might be termed “plain LLM agents” and the more complex and increasingly impactful concept of “Agentic AI.” While the terminology is still evolving rapidly, understanding this distinction is key to appreciating the capabilities and potential of the next generation of AI systems.
The Nature of Plain LLM Agents
Think of a plain LLM agent as a highly sophisticated language processor, equipped with an incredible ability to understand context and generate relevant text based on the input it receives. These systems excel at tasks that are primarily communicative or informational. You provide a prompt, and they generate a coherent and often insightful response. This is the experience many of us have with contemporary AI chatbots or language-based assistants. Their power lies in their linguistic fluency and the vast knowledge embedded within their training data.
A plain LLM agent operates largely in a reactive mode. It waits for a query or prompt and then processes it using its language capabilities. It can access and utilize information and even employ simple tools (like searching the web or a calculator) if integrated, but its actions are typically a direct, single-step response to the immediate input. There’s a limited sense of continuity beyond the current interaction, and the system doesn’t typically formulate long-term strategies or independently decide on a course of action without explicit prompting for each step. Their memory is often confined to the immediate conversational window or the length of the current interaction.
An Example: Data Discovery with Plain LLMs
Consider a practical example relevant to many enterprises: the challenge of data discovery and analysis for professionals like database engineers, business analysts, and data scientists. Historically, finding the specific data required to answer a business question is a time-consuming process. It involves navigating complex data stores, deciphering dense data dictionaries and metadata, and engaging in numerous manual steps and consultations with subject matter experts to understand the relevance and structure of different datasets. This intricate process, filled with trials and errors, can often take days, or even weeks, before the right data is identified and utilized to generate actionable insights.
A plain LLM-based solution can offer significant acceleration in this initial data discovery phase. By providing the LLM with access to an organization’s data dictionaries, schema information, and other relevant metadata, a user can pose questions in natural language like, “Show me the tables that contain customer transaction details for the last fiscal year” or “Where can I find information about product inventory levels?” The LLM, acting as an intelligent interface, processes this natural language query, leverages its understanding of language and the provided technical documentation, and generates a response suggesting relevant datasets, tables, or even column names. This is a powerful application of a plain LLM agent — it effectively translates a human request into relevant technical information based on its training and the provided context, drastically reducing the initial manual exploration time. This type of solution, focused on understanding the user’s intent and retrieving relevant information from a knowledge base, is a prime example of how LLMs can serve as highly effective, although primarily reactive, tools for specific tasks.
Beyond Basic Reactivity
Moving along the spectrum, some more advanced LLM-based systems for data interaction incorporate multiple steps or specialized modules (we call them agents) orchestrated in a sequence to improve performance and accuracy. For instance, such an agent might first use an LLM to classify the user’s intent within a specific business domain (e.g., sales, operations, finance). Based on this classification, another agent might identify a narrowed set of potentially relevant data sources. Subsequent agents could involve using the LLM to generate potential queries, which might then be validated or refined. While these agents employ a more structured workflow and leverage the LLM for different sub-tasks, they often still operate primarily in response to an initial user prompt and follow a predefined sequence of operations. They represent a significant enhancement over a basic prompt-response LLM but may still lack the deeper autonomy and adaptive planning characteristic of true Agentic AI.
Understanding Agentic AI
Agentic AI, however, envisions a system with a substantially higher degree of autonomy and proactive capability. It moves beyond simply processing language or executing a predefined multi-step process in response to a query. A truly agentic AI system is designed to understand a broader, high-level goal and independently strategize, plan multi-step actions, utilize a diverse set of tools, and adapt its approach based on the outcomes and dynamic changes in its environment, all aimed at achieving that overarching objective with minimal human intervention.
Agentic AI in Action: Transforming Data Discovery
Applying the principles of Agentic AI to the data discovery and analysis challenge would transform the system from a sophisticated tool that answers direct questions or follows a fixed workflow into a proactive, intelligent collaborator that helps solve the entire data-related business problem from initiation to insight.
An agentic data analysis system would start with a complex, potentially ambiguous business objective, such as “Understand the key factors contributing to customer retention issues in the past year and propose data-backed strategies to improve it.” Instead of requiring the user to specify datasets or formulate queries, the Agentic AI, understanding this high-level goal, would autonomously work on a multi-step process:
- Goal Decomposition and Planning: The agent would break down the broad objective into a series of necessary, interconnected sub-goals: identify relevant customer data, explore historical interaction and transaction data, find demographic and behavioral information, analyze trends in customer activity before churn, and identify potential correlations. It would then formulate a dynamic plan, outlining the steps needed to achieve these sub-goals.
- Autonomous Tool Utilization and Orchestration: A crucial capability here is the agent’s ability to seamlessly interact with and orchestrate a variety of external tools and systems. This could include:
- Interfacing with data catalogs and governance tools to identify potential datasets and understand their compliance and access restrictions.
- Employing database query tools (like SQL or NoSQL clients) not just to execute generated queries, but to explore schemas, sample data, and understand table relationships through iterative interaction.
- Using data profiling tools to assess data quality and identify potential issues that might impact analysis.
- Leveraging statistical or machine learning libraries to perform exploratory data analysis or build preliminary models to test hypotheses.
- Interacting with visualization tools to present findings or explore data distributions.
- Potentially utilizing internal communication systems to proactively ask clarifying questions to subject matter experts based on ambiguities encountered in the data or documentation.
- Reasoning, Self-Correction, and Adaptation: As the agent executes its plan, it would continuously reason about the results of its actions. If a query returns unexpected data, if a data profiling reveals significant quality issues, or if an initial analysis doesn’t yield meaningful insights, the agent would be capable of recognizing these discrepancies, diagnosing potential causes, and adapting its plan autonomously. This might involve revisiting earlier steps, exploring alternative datasets, or trying different analytical approaches without requiring explicit human guidance for each adjustment.
- Persistent Memory and Context: The agent would maintain a rich, persistent memory of its exploration process — the datasets examined, the queries executed, the patterns observed, the hypotheses tested, and the conclusions drawn at each stage. This comprehensive context allows it to build upon previous findings and avoid redundant efforts, enabling a more efficient and focused investigation over an extended period.
- Proactive Insights and Actions: Beyond simply delivering data or analysis results when the task is complete, an agentic system could proactively identify interesting patterns, flag potential issues, or suggest follow-up questions or analyses based on its ongoing exploration and the initial high-level goal. It moves from being a tool that responds to commands to a partner that actively contributes to problem-solving.
This level of autonomous planning, dynamic tool orchestration, continuous reasoning, and adaptive execution, driven by a high-level goal, is what truly distinguishes Agentic AI. It’s a fundamental shift from AI systems that primarily talk or generate content or even execute fixed workflows, to AI systems that do, act, and strategize independently in pursuit of complex objectives.
Recognizing this distinction is vital as we continue to build and deploy increasingly capable AI systems. The evolution from simple LLM-powered assistants to systems with growing agentic capabilities marks a significant step towards more versatile and impactful artificial intelligence, capable of tackling more complex, real-world problems with greater autonomy.
Path Towards Artificial General Intelligence
The development of Agentic AI is not just about creating more capable specialized systems; it is also seen as a crucial step along the path towards Artificial General Intelligence (AGI). While there is no single, universally agreed-upon definition of AGI, it is generally understood as an AI system that possesses the ability to understand, learn, and apply intelligence across a wide range of tasks at a human-like level, rather than being limited to a specific domain (like playing chess or generating text). AGI would exhibit characteristics like common sense, creativity, and the ability to reason and make decisions in novel situations.
Why are agentic capabilities considered important for AGI? The core components of Agentic AI—the ability to understand goals, plan, execute actions in an environment, utilize tools, manage memory, and adapt—are fundamentally aligned with the abilities we associate with general intelligence. An AGI system would need to interact with the world, set its own sub-goals to achieve larger objectives, learn from its experiences in a persistent way, and leverage whatever resources are available (tools) to accomplish tasks across diverse domains.
Current LLMs, while demonstrating impressive language understanding and generation, are often described as lacking “agency” in the human sense. They don’t inherently want to achieve a goal or independently decide to take a series of actions in the real or digital world without explicit prompting or external frameworks guiding their behavior. Agentic AI research focuses precisely on building these missing pieces: the architectures and capabilities that enable AI systems to act more autonomously and purposefully in dynamic environments.
Therefore, advancements in Agentic AI are pushing the boundaries of what AI can do, moving it closer to the kind of flexible, goal-directed behavior expected of AGI. While achieving true AGI remains a significant scientific and engineering challenge, the progress being made in developing agentic systems is seen as contributing foundational elements—building blocks like sophisticated planning algorithms, robust memory systems, and seamless tool integration—that are essential for creating AI that can operate with the breadth and adaptability characteristic of general intelligence. The journey toward AGI is likely a long one, but the development of increasingly capable agentic AI systems is a clear indicator of progress, demonstrating the path towards AI that can not only understand the world but also act within it effectively and autonomously.
References
1. Aisera. 2025. What is Agentic AI? Key Trends in 2025. (Jan. 2025). https://aisera.com/blog/agentic-ai/
2. Arbués, P. Agents explained. https://www.pelayoarbues.com/notes/Agents-explained
3. Automation Anywhere. What is Agentic AI? Key Benefits & Features. https://www.automationanywhere.com/rpa/agentic-ai
4. IBM. Agentic AI: 4 reasons why it’s the next big thing in AI research. https://www.ibm.com/think/insights/agentic-ai
5. Moveworks. Agentic AI Vs AI Agents: 5 Differences and Why They Matter. https://www.moveworks.com/us/en/resources/blog/agentic-ai-vs-ai-agents-definitions-and-differences
6. ResearchGate. What are the characteristics of agent-based artificial intelligence? https://www.researchgate.net/post/What_are_the_characteristics_of_agent-based_artificial_intelligence
Azim Shaik is an Enterprise Architect at KeyBank, where he leads AI-driven innovation and cloud transformation across core financial platforms. With over a decade of experience in financial services, he specializes in integrating emerging technologies into secure, scalable architectures that support digital banking, automation, and enterprise technology strategy. He is currently pursuing his MBA at the University of Illinois Urbana-Champaign, with a focus on technology leadership and business strategy in regulated industries.