02/14/2025
Artificial intelligence (AI) agents are revolutionizing the way companies operate, innovate, and deliver value to their customers. Unlike other technologies, these “autonomous helpers” combine automation, machine learning, and natural language processing (NLP) to perform complex tasks, accelerate processes, and even make decisions.
According to McKinsey, the market for AI agents is expected to grow exponentially in the coming years, driven by innovations in generative AI and advances in computing. Companies across sectors such as healthcare, retail, and financial services are already reaping the benefits of this technology, from reducing operating costs to personalizing customer experiences.
But what exactly are AI agents, how do they work, and why are they at the center of discussions on digital transformation and evolution? That’s what we’ll explore in this article.
Agentic AI and its influence on the development of AI agents
While the terms “Agentic AI” and “AI agents” are often used interchangeably, there is an important distinction. Agentic AI refers to the broader concept of artificial intelligence systems that can act autonomously, continuously learn, and make independent decisions. AI agents, on the other hand, are specific implementations of this concept, designed to perform concrete tasks in defined contexts.
While AI agents are built to carry out specific functions autonomously, Agentic AI provides the foundation that enables these systems to learn from new information and adapt to different scenarios. This evolution expands automation and efficiency, making AI agents more sophisticated and essential to complex business processes. In short, Agentic AI is the structure that enables autonomy, while AI agents are the practical applications—found in solutions such as chatbots, virtual assistants, and recommendation systems.
What are AI agents?
AI agents are software systems designed to operate autonomously, performing complex tasks based on data and predefined goals. Unlike conventional virtual assistants like Apple’s Siri or Amazon’s Alexa, AI agents can interact with enterprise systems, automate processes, and even learn over time—adapting to new challenges and contexts.
In healthcare, for instance, AI agents are being used to analyze large volumes of clinical data and help doctors make more accurate diagnoses. In logistics, an AI agent can monitor deliveries in real time, detect delays, and automatically suggest alternative routes to optimize transportation.
These systems sit at the intersection of technologies such as machine learning, NLP, and automation algorithms. This combination allows AI agents to operate effectively and in an integrated way, as highlighted in numerous enterprise case studies. For example, AWS showcases the use of AI agents in business solutions aimed at workflow automation.
How do AI agents work?
AI agents follow a structured workflow, reflecting an approach widely adopted by companies like AWS, McKinsey, and Oracle. The process begins with goal setting, where user instructions are translated into specific objectives, broken down into actionable subtasks. Next, agents collect information, integrating with internal and external data sources and enterprise tools such as ERPs and IoT sensors.
Finally, agents implement tasks iteratively, adjusting based on feedback and results to ensure the final objective is achieved. This cyclical and adaptive flow—supported by machine learning and NLP—highlights AI agents’ ability to operate autonomously across different contexts, combining efficiency and accuracy.
Benefits of AI agents for businesses
The benefits of AI agents converge on key points emphasized by global players such as AWS and Oracle. Automating repetitive and complex tasks is a central advantage, enabling greater efficiency and cost reduction. Another consensus is the productivity boost, as agents work continuously without interruption, ensuring consistent process execution.
They also improve decision-making by analyzing large volumes of data in real time, enabling more strategic and precise actions. On top of that, AI agents enhance customer experience through personalized interactions, quick responses, and more accurate recommendations. These characteristics make AI agents an essential tool for companies seeking efficiency and innovation.
Challenges of using AI agents
Despite their potential, AI agents present challenges ranging from data privacy and security—essential for protecting sensitive user information—to the need for transparency in decision-making. As highlighted by MIT Technology Review, bias in AI models is a central concern, requiring safeguards against flawed training data that could lead to discrimination.
Other challenges include technical complexity and high computational costs, which can be significant barriers at scale. Finally, adapting to rapid change is crucial to prevent operational failures, underscoring the importance of ongoing human oversight. These challenges make it clear that implementing AI agents requires a careful balance between innovation and risk mitigation.
Digital transformation and evolution: INSI’s role in AI agents
INSI, with its expertise in automation, data, and process optimization, leverages cutting-edge technology and a client-centered approach to implement AI agents that not only automate tasks but also create real business value by ensuring efficiency, security, and innovation.
Beyond creating tailored agents for specific business models through our Artificial Intelligence Center of Excellence (CoE), AI agents are already embedded in our offerings such as Smart Squads, Digital Delivery Center, Digital AMS, and Digital BPO. They are part of both our software engineering process and our teams’ day-to-day operations.
This commitment to AI as a driver of digital transformation and evolution reflects INSI’s mission to help organizations navigate a dynamic market, optimize workflows, and achieve new levels of operational performance.