Intelligent Applications: Agentic AI & AI Agents for the Modern CIO
We are witnessing the rise of AI and its promise to change the world, or at least the way we work. It will enable systems capable of taking autonomous actions, making intelligent decisions, and even learning on their own with each iteration, features that until now were reserved for humans. It is already a topic that appears in mass media, and in barbecue conversations and family gatherings.
However, this paradigm shift has profound implications for companies, and especially for CIOs tasked with implementing them. This forces CIOs to have a rigorous understanding of the technology and its pragmatic application to solve tangible and relevant problems at an affordable cost.
With the rapid development of AI, a multitude of new terms are constantly appearing, and just as many become obsolete in record time. We have barely digested the term "AI Agents," and market analysts and AI service providers have already coined a new concept: "Agentic AI."
Understanding the Terminology: AI Agents vs. Agentic AI
AI Agents:
AI Agents are AI systems designed to perceive their environment, process information, and take actions to achieve specific goals. Think of them as subroutines (those of my generation will understand) within a more complex system, with a certain degree of autonomy.
The most common uses include chatbots, virtual assistants, and specific task automation tools. While they can make decisions, their scope is often limited to predefined tasks and workflows.
An example of an AI Agent would be a customer service chatbot on an e-commerce website. Its function is to answer frequently asked questions from customers about products, shipments, and returns. It follows a predefined set of rules and responses, and provides information based on a database with a pre-established script. Its adaptability is limited, and it requires manual updates to incorporate new information.
Agentic AI:
Agentic AI is a subset of AI Agents, representing a more advanced and sophisticated stage, and is characterized by higher degrees of autonomy, adaptability, and the ability to handle complex and unstructured tasks. Think of them as complete and independent programs that execute a more general purpose than AI Agents, such as an end-to-end business process.
Agentic AI systems can learn from experience, reason, and adapt to changing circumstances instead of executing predefined workflows. Essentially, Agentic AI is an evolution of AI Agents, where agents gain greater autonomy.
An Agentic AI example would be a system that manages a user's travel planning. It organizes complete itineraries, books flights and hotels, recommends activities, and adapts to changes in the user's plans. It shows autonomy and the ability to make complex decisions. It can learn and adapt to the user's preferences and needs over time and interacts with multiple systems and information sources (airline APIs, hotels, etc.). Therefore, it has the ability to carry out several chained tasks in parallel and/or sequentially to achieve a more complex goal (completing the travel planning).
In conclusion:
AI Agents are oriented towards specific and well-defined tasks, have limited autonomy when making decisions based on pre-programmed rules, and their focus is on the specific functionality for which they were conceived.
In contrast, Agentic AI is oriented towards complex processes that require multiple tasks, technologies, and reasoning, has greater autonomy when making decisions without constant human supervision, and adapts to previous experiences and changes in the environment. Its focus is on the general purpose for which they were conceived, pursuing a broader and longer-term goal. Finally, it should be noted that while AI Agents are reactive and have to be explicitly invoked, Agentic AI can take a more proactive and adapted role in its environment at any given time.
How we can help you get started from dataguru:
- Start with Specific Use CasesWith our AI Discovery Workshop service, you can identify the most relevant use cases based on their impact on your organization and the feasibility of their implementation. During the Workshop, conducted by experts in technology, business strategy, and group dynamics, participation and creativity are enhanced in an inclusive (with the participation of different stakeholders and not just the IT department), motivating and collaborative environment. Innovative thinking and the generation of practical and feasible solutions with a clear return on investment are stimulated, and as a result, a clear map is drawn that serves as your starting point for a successful path to AI implementation.
- Define Adequate and Necessary Infrastructure and Data to Support Agentic AI and AI Agent Implementations. The nature of the data can be very diverse: numerical data represented in rows and columns from traditional Data Warehouses; semi-structured data from text files such as PDFs, JSON, or NoSQL formats, typically from Data Lakes, and in general any digitized data (images, audios, videos, vectors, graphs, map routes, locations, etc.). From dataguru we help you with our AI Strategy & Design service so that you have everything prepared for your journey to AI.
- Talent and Skills that allow you to work with the full potential of Agentic AI, when you need it. You won't have to go to the market to hire highly demanded talent, initiating costly search and selection processes, complex justifications to get internal approvals, and budget allocation in human resources. We help you with Talent as a Servicea novel and comprehensive service where you will have talent in different areas of expertise at your disposal, to make your AI initiatives a reality.
