Hype surrounding AI is to be expected. It happens with any significant advance in technology and tends to move in cycles as new breakthroughs occur, and investors inevitably follow the money. This hype often comes at the expense of clarity, however. The result, is people are often left confused about what the various terms mean.
The origin of the first AI wave is hard to pinpoint exactly. Still, it almost certainly has its roots in not only machine learning (ML) but also large language models (LLMs), which, over-simplified, allowed for smart automation. ML was often used for routine tasks such as remote diagnostics and self-testing for example. Then came the second wave, generative AI (GenAI), which was a significant step forward in terms of the technology’s ability to mimic human behaviour by creating content based on simple prompts.
The emergence of Agentic AI
As the excitement around this second wave begins to cool, there are now signs of a third wave with the potential to create a whole new hype cycle. Agentic AI has been described by Forbes as a ‘breakthrough for business and tech,’ while IBM describes it as ‘the next big thing in AI research.’ It is being greeted as the missing link due to its potential to augment through different types of AI, allowing various ‘agents’ to play together to share skills.
What this means in practice is that processes and tasks currently requiring human decision-making regarding the outputs that preclude automation will reduce. This is because Agentic AI can adapt, learn, and optimise its processes in real-time without needing explicit instructions at every step. It will enable humans to interact with AI to execute incredibly complex tasks through ever simpler user interfaces and natural language instructions.
Put simply, Agentic AI is different because it has, as the name suggests, agency. This ability to adapt to new tasks and make complex decisions based on real-time reasoning allows it to act almost like a human employee.
As an example, in Virtuoso’s world of QA testing, when tests are running and going through a new version of the application, there are AI bots running autonomously making decisions. If the code behind the ‘button’ has changed, these bots will be able to make real-time decisions on whether to fix this and keep running or stop the process, effectively enabling diagnosis and resolution in real time.
Preparing for the Human-Centric Revolution
While AI is not yet ready to replace all human workers, it is moving very fast, and changes, as well as a reduction in current job roles, are inevitable. Where previously we have had industrial revolutions replacing manual labour with machines we now have machines that carry and grow intelligence. That is significant.
AI thinks like a human but at a greater pace and scale, and the evidence is that much of what humans are doing today can be done better, quicker and more affordably by leveraging AI. We have to accept that we need to evolve into the Human Centric Revolution, where people and machines work together more effectively to solve problems faster than ever.
Talent challenges
Building teams that are capable of managing these new environments will present new challenges for leaders in the short term. They will need to focus on training existing staff or hiring new employees capable of fully embracing the power and efficiency of these technologies and with the ability to adapt to the fast pace of change they will inevitably set.
Cultivating a culture that encourages cross-functional collaboration between QA engineers, developers, and AI specialists to integrate Agentic AI into workflows effectively will be key to this. Upskilling existing talent by investing in training programs for employees to develop skills in AI technologies, machine learning, and automation tools will also be vital.
By defining clear roles and responsibilities and clearly articulating how those roles will evolve with the introduction of Agentic AI, business leaders can help allay some of the understandable anxieties within their workforce. Establishing new responsibilities like AI supervision, model training, and integration management and fostering adaptability and continuous learning will help teams embrace AI-driven changes and innovate within their roles.
Equally, encouraging a growth mindset by starting teams off with small projects or proofs of concept that they can learn from swiftly will pay dividends. Teams should be able to provide extensive feedback so that progress and sticking points can be monitored.
Looking further ahead
In the longer term, Agentic AI will make processes faster, smarter, and more efficient. They will have the power to quickly unlock industry potential once it is understood. As AI adoption continues to grow, this new era is accelerating the pace of innovation.
The future of AI will likely involve even deeper integration with human intelligence, enabling collaborative workflows where humans and AI work side by side. Far from being the stuff of science fiction, this type of autonomous, intuitive AI is now a firm reality, and its impact is only just beginning.
Take exploratory testing, for example – a software testing task that, until now, couldn’t be executed by AI. This is because there are no defined rules for this type of testing. It requires human curiosity and adaptability to investigate a software system and test it for bugs or other weaknesses that an AI needs to be programmed in order to think of. In theory, Agentic AI agents could handle such tasks with minimal human oversight.
AI is evolving fast. So fast, in fact, that Agentic AI itself may be simply a stepping stone on the way to a yet-to-be-invented fourth wave. It is even probable that as Agentic AI matures, it will have a hand in its own transformation through collaboration and learning. The human race could do worse than follow this example.
Founded by Adil Mohammed, Hugo Farinha, and Andrew Doughty in 2017, Virtuoso QA was developed by a team passionate about improving the quality of low-code/no-code test automation software without slowing down the development process. By 2019, Virtuoso had reimagined test automation software by pioneering the next generation of low-code/no-code testing in the cloud. Virtuoso believes anyone can test, and uses AI, ML, NLP, and Robotic Process Automation to run a test automation tool offering speed paired with the power of scripted test steps.
