INDUSTRY TRENDS & EMERGING TECHNOLOGIES
Agentic AI is being hailed as the next leap in automation—tools that not only follow instructions but take initiative. But how far are we really from that reality?
1. The mechanisms behind agentic AI: How it operates
2. Practical applications of Agentic AI Across industries
3. Jalasoft’s Research & Development Investigation
4. Challenges and risks associated with agentic AI adoption
5. Frequently Asked Questions
Picture an AI that actively works alongside you: planning, making decisions, and moving tasks forward without being told what to do at every step. That’s the main idea behind Agentic AI.
Until now, AI has progressed at an almost inconceivable pace, becoming an increasingly natural part of our daily lives. Yet, we still tend to see it primarily as a tool—something that responds to our input rather than takes initiative.
Agentic AI is set to change that. It acts as an autonomous collaborator, capable of setting its own goals, adapting to new information, and managing complex tasks over time.
As this new kind of AI begins to take shape, it’s prompting a shift in how we think about automation, its ethical implications, and raising important questions about how soon it will begin to reshape our work.
Agentic AI is built on advanced machine learning models, memory architectures, and decision-making systems that allow it to operate with a high level of autonomy. It combines language models with tools for long-term memory, reasoning, and planning. This allows it to break down goals into sub-tasks and execute them step by step.
The main difference with traditional AI is that it reacts to single prompts; agentic systems can keep track of context, learn from outcomes, and adjust their strategies dynamically. This makes them capable of handling more human-like tasks, such as open-ended tasks that unfold over time: Agentic AI can manage projects, conduct research, or write code—tasks that typically require foresight and flexibility; abilities that are, typically, exclusively human.
Another key mechanism is the ability to interact with various environments or digital tools through APIs and integrated systems. Agentic AI can decide when to use a specific tool, evaluate the results, and determine the next best action, often without direct human input.
Many of these systems also incorporate feedback loops, allowing them to reflect on their actions and refine their approach based on success or failure, regardless of the much-discussed soft skills and the values that only human character can provide.
In essence, agentic AI integrates perception, memory, reasoning, and action into a unified system designed to replicate certain self-directed behaviors commonly associated with human traits. The question here is whether it is truly possible to transfer our essential human nature to AI, allowing it to make decisions without our intervention.
(Jalasoft was recognized as a Microsoft Solutions Partner for Data & AI, offering comprehensive solutions to help businesses in the US and Canada manage their data and harness the power of artificial intelligence. )
As expected, Agentic AI is making significant strides across multiple industries, with its promise of intelligent, autonomous systems that can manage complex tasks with minimal human intervention.
As companies rush to embrace agentic AI, many are driven by the promise of faster delivery and lower costs. In this pursuit, a growing number may rely heavily on AI-generated code, scaling back their development teams and keeping only a handful of engineers to supervise the tools. While this shift can yield short-term gains, it also sets the stage for deeper, long-term risks.
With less human oversight and a growing dependence on autonomous systems, the quality and reliability of the code may begin to erode. Over time, subtle bugs, overlooked vulnerabilities, and compounding inefficiencies can surface, often too late to prevent significant damage.
This over-reliance prompts a costly reckoning: companies may be forced to roll back their AI usage, reintegrate engineers, and undertake the painstaking task of reviewing and correcting layers of flawed code.
Regarding this, the practical deployment of agentic AI doesn’t come without challenges. Because these systems operate with a greater degree of independence, if an agentic AI begins down an incorrect path or makes flawed decisions early on, the consequences can be significant.
“To me, this is the classic double-edged sword: AI can dramatically increase velocity, but in the wrong hands, it can amplify risk at scale,” explains Jorge López, Jalasoft’s CEO and Founder. And when that happens, it’s not a technical failure. It’s a strategic failure that exposes the urgent need for leadership, governance, and intentional decision-making.
(Interested in how AI is reshaping the industry for professionals? Read our blog on "What Happens to Software Engineers When AI Takes Over?")
Unlike simpler AI tools that perform isolated tasks, agentic AI’s long-term and goal-driven nature means mistakes can compound over time, making errors harder to detect until they have already influenced multiple stages of a process.
In such cases, organizations may face the daunting task of not only identifying and correcting each misstep but potentially having to restart workflows from the beginning to realign outcomes with original objectives.
This risk highlights the critical importance of continuous monitoring, human-in-the-loop oversight, and robust validation processes when working with agentic AI systems.
And this is a job for very experienced engineers, because determining whether the AI-designed solution is a good one requires a sharp mind, as well as profound knowledge of software engineering. “While the code will compile and pass automated tests, the junior developer won't have the experience to evaluate why the AI solution worked or whether it should have been trusted. In fact, this version could introduce subtle bugs and compromise scalability,” explains López.
While the technology holds great promise to improve workflows, unchecked autonomy can lead to setbacks that are costly both in time and resources. In this context, striking the right balance between innovation and thoughtful oversight will be essential to unlocking the full potential of agentic AI while avoiding the risks of unchecked autonomous errors.
(Jalasoft is staying on top of the AI trend. Learn more about the MTLC AI-Focused event that we sponsored.)
At Jalasoft, we’ve been exploring this question not just from a technical standpoint but also from a strategic and ethical perspective. That’s why our Research and Development teams have been testing the performance of code and engineers assisted by AI, as well as its possible implications and risks.
As part of an ongoing test within our R&D team, a senior engineer was assigned to refactor a complex piece of code as a test. The task required a full day of work and demanded a deep understanding of system architecture, business logic, and long-term impact.
With the help of AI, that same refactor could be completed in just hours. Even more striking: a junior engineer using the same AI tool could technically perform the task. But as we’ve mentioned, the code needed a check that a regular Junior development couldn’t have provided.
Our R&D teams also found that while AI can create simple features in under 5 minutes, complex tasks still require human expertise to ensure quality and precision.
Performance improves when AI can test and learn from its own output, reinforcing that AI alone isn’t enough; it needs smart automation and human oversight to deliver real value.
Although we’ve already touched on some of the difficulties that come with agentic AI, it’s essential to explore the most pressing challenges in greater depth. Doing so can support more informed decision-making and ensure that industries adopt these technologies with foresight and responsibility.
Like anything new, adopting agentic AI brings huge potential, but it also introduces a new set of challenges and risks that organizations must address with care.
One of the most significant concerns lies in the loss of human oversight.
From its design, agentic AI systems operate with a high degree of autonomy: setting goals, making decisions, and executing multi-step tasks over time. Even though this enables efficiency and scalability, it also means that, if the system takes a wrong turn early on, errors can cascade and compound without immediate detection. The result is not just a flawed outcome, but a long trail of decisions and outputs that may need to be meticulously traced (and corrected), often at a high cost.
Another significant risk that demands attention is the growing over-reliance on these systems.
Initial positive outcomes can quickly build confidence in these systems. The increase of trust in agentic AI to handle core functions comes with a temptation to reduce human teams to a supervisory role. This might seem efficient in the short term, but it can hollow out institutional knowledge and limit an organization’s ability to respond effectively when the AI fails or underperforms.
Accountability and transparency, on the other hand, are also major challenges.
Agentic AI models often make decisions based on layers of data processing and internal reasoning that are difficult to interpret. In addition to the complexity of the task itself, there’s the added challenge of understanding why the system operated in a certain way that ultimately led to an incorrect outcome, especially in high-stakes environments like finance, healthcare, or law. Without transparent audit trails and clear explainability, determining accountability when errors occur becomes challenging, which raises ethical, legal, and operational concerns.
Another aspect that covers all the previous concerns is security.
As agentic AI systems interact with other software, APIs, and data sources, they broaden the attack surface and may introduce vulnerabilities if not properly managed. A system with access to sensitive operations—such as approving transactions or modifying infrastructure—must be protected against both external threats and internal misjudgments. A seemingly small flaw in how the AI interprets instructions or prioritizes goals could lead to significant, unintended consequences that compromise both individual and systemic safety.
Last but not least, there's the risk of misalignment when an agentic AI system’s understanding of its objective diverges subtly from human intent. This brings up one crucial question: Is it possible to be 100% sure that AI understands exactly what we meant?
If goals are poorly defined, if a certain context shifts, or maybe if the AI's internal optimization logic overemphasizes one aspect of a task at the expense of others, even a system that is technically performing “well” can end up producing results that are counterproductive, biased, or harmful.
As organizations consider implementing agentic AI, these challenges highlight the need for robust governance frameworks, ongoing human oversight, and the ability to intervene quickly when systems drift off course. Without these safeguards, the very autonomy that makes agentic AI powerful can become a liability.
Agentic AI refers to a class of artificial intelligence systems designed to operate autonomously, with the ability to set goals, make decisions, and take actions without requiring constant human guidance. Unlike traditional AI, which typically responds reactively to specific inputs or performs narrowly defined tasks, agentic AI functions as an independent agent capable of long-term planning, adapting to new information, and managing complex, multi-step workflows. Agentic AI can proactively pursue objectives and adjust its behavior based on changing environments or feedback due to its autonomy, which makes it more versatile and powerful in dynamic contexts.
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