Agentic AI (also called multi-agent AI systems) will revolutionize business operations. Joe Dunleavy, Global SVP and Head of AI Pod at Endava, explains how this exciting technology will pave the way for more transparent, accountable and sustainable use of AI and how its impact will transform businesses at scale.
Until now, humans had to be in the driver’s seat when it came to giving detailed instructions to AI technology. This ensured that AI was not only steered in the right direction in terms of outcome, but also helped to minimize any risks such as hallucinations, misinformation or biases. Furthermore, organizations that deploy AI often only improve the efficiency of individual tasks, reaping short-term value rather than focusing on large-scale autonomous automation.
However, AI systems are now able to handle more ambitious business processes, decision-making and data transformation. Agentic AI will revolutionize the way organizations across industries can use this technology to their advantage. Using agentic AI, they will soon be able to automate processes in entirely new, more efficient and autonomous ways, helping them tackle complex business problems at scale and speed. But how do they get there in the most effective, safe and compliant way?
Global SVP and Head of AI Pod at Endava.
The three phases of AI transformation
To achieve this level of performance, automation and autonomy, AI needs a solid foundation. The transformation takes place in three phases. The first phase focuses on improving daily work by helping with tasks such as summarizing documents or generating assets such as presentations, leading to faster, more cost-effective and more accurate results. In the next phase, automation processes will be more integrated with business objectives. At this point, AI takes more responsibility for task sequences, working alongside humans rather than just following individual orders. This is how AI evolves from a tool to a trusted partner.
In the third phase, the technology reaches an even higher degree of autonomy. At this point, AI is no longer ‘just’ a teammate that collects, summarizes and analyzes information. Instead, she takes on an advisory, more ‘proactive’ role. This is made possible by AI-based autonomous agents (agentic AI) that can work within any environment without direct human intervention, including various large language models (LLMs) and cloud platforms. Unlike traditional AI models, which are specifically programmed for individual processes, agentic AI approaches can handle much more complex tasks.
In a team of autonomous agents (multi-agent system), each agent is assigned an individual role and provided with the necessary knowledge. These agents can communicate and interact with each other as well as with their environment, respond to changes and contextualize their tasks to make holistic decisions and achieve the best possible outcome. All of this works with minimal human supervision, without having to provide manual input at every step of the process.
While agentic AI technology is still in its early stages, these systems can safely advance workflows with minimal oversight. While autonomous agents automatically perform time-consuming, mundane, and repetitive tasks, they can accelerate the amount of work done within a specific time frame, which can be applied across the business to drive large-scale efficiencies. This frees up employees who can, in turn, focus on more complex strategic and creative challenges. This approach nurtures the potential of every employee, increases employee job satisfaction and drives company growth and value.
Benefit from autonomous agents – but not without transparency
Autonomous agents can be applied to address complex and nuanced workflows in every conceivable industry. However, AI systems are usually built as so-called black boxes, whose functions and processes are neither visible nor understandable to their users. As a result, highly regulated industries such as healthcare, financial services, insurance and energy – where strict regulations apply to the collection, processing and storage of sensitive data – are often reluctant to implement the technology into their daily operations. After all, they must meet specific requirements when collecting, processing, using and storing (sensitive) data. Just as it is important to not only find the right answer, but also demonstrate the steps taken in areas such as law or accounting, these industries must be able to clearly demonstrate how AI achieves its results to meet compliance requirements to fulfil.
The solution to this challenge is a data-first approach. If these industries want to use AI to their advantage and optimize their processes, they must be able to crack open the black box and make its contents public in a transparent and auditable way. An autonomous multi-agent system that shows how AI agents ingest and transform data is ideal to address this challenge, because every time an agent acts on data, the system captures the relevant information surrounding the operation, providing a clear line of sight and understanding of the activities is created. decision the agent makes as an audit test. This breakdown can make both data and processes visible and understandable and effectively circumvent common AI-related problems such as AI hallucinations.
Using agentic AI, companies can automate advanced processes and solve complex business problems at scale, while remaining compliant with regulations. As a result, technology is critical to unlocking productivity, satisfaction, business growth and sustaining competitive advantage. This does not mean that employees will be replaced by technology. While requiring less human intervention and supervision, users remain in full control of the AI system and are at the core of business operations. AI may be at the wheel, but users dictate the direction and can hit the brakes at any time.
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