Modern AI Agents in the Cloud: Exploring the Future

The field of artificial intelligence is growing rapidly, with AI agents being key to future innovations. However, the idea of intelligent agents is not new. They are mostly researched in fields such as robotics, multi-agent systems, swarms, and specialised robotic applications. AI agents now power virtual assistants, chatbots, intelligent automation of business processes, and many more use cases.

The integration of Large Language Models (LLMs), generative AI, and multi-modal capabilities has enabled AI agents to interact more naturally with humans using natural language capabilities.

We spoke with Oleh Chaplia, Senior Software Developer at ELEKS, to bring you the expert insights.

What sets modern AI agents apart from traditional software?

The key distinction between AI agents and traditional software lies in their interaction with the environment. AI agents are software or hardware entities equipped with a specialised architecture that enables them to perceive, analyse, and respond to changes in their environment. Unlike traditional software, AI agents work in the environment, making decisions based on data and evolving environment conditions. This ability to sense, learn, and act allows agents to operate with greater flexibility and intelligence, even in complex, unpredictable scenarios.

What industries will benefit from using AI agents?

In my opinion, AI agents are driving major advancements in robotics, physical AI, autonomous systems, space exploration, cybersecurity, and business workflow automation, transforming industries through intelligent automation.

Robotics and autonomous systems will be significantly enhanced by modern AI achievements, enabling machines to become more intelligent, adaptive, and efficient. In space exploration, AI-driven agents assist in autonomous navigation optimisation and satellite monitoring, optimising missions with minimal human intervention. Cybersecurity benefits from AI agents that detect cyber threats automate incident responses and enhance fraud prevention.

Workflow automation in businesses leverages AI-powered systems for intelligent document processing, predictive analytics, and supply chain optimisation, thereby reducing operational costs and improving efficiency. As AI evolves, these intelligent agents will continue to redefine industries by enabling smarter, safer, and more autonomous systems.

The environment may contain sensitive data. How do we protect it?

AI agents interact with humans, so security, privacy, and transparency are critical. The environment may contain sensitive data, such as user information, proprietary business data, or confidential records. This data must be safeguarded against breaches, misuse, or unauthorised access.

Companies need strong security measures to protect data and follow regulations. The level of security depends on the type of private information they handle. A good approach is using private environments for AI systems, which keep data inside the company instead of sharing it externally. Organisations can achieve this by leveraging secured cloud services (e.g., Azure, GCP) with contractual agreements or by deploying AI models in on-premise environments to maintain full control.

Transparency is equally important in AI systems to understand decision-making processes and prevent potential harm. By ensuring AI agents are explainable, organisations can identify biases, improve fairness, and maintain ethical standards. Understanding how AI systems operate enhances accountability, fostering trust, reliability, and responsible AI deployment.

To conclude, what are your thoughts on the future of AI agents?

The current generation of AI agents will evolve into highly autonomous systems with advanced reasoning, problem-solving, and decision-making capabilities. Large Language Models (LLMs) will further enhance human-AI interaction, making communication more natural, intuitive, and efficient.

These agents will move beyond simple task execution to actively analyse complex scenarios, strategies solutions, and adapt to dynamic environments. The integration of Multi-modal AI (text, vision, and speech), real-time learning, and contextual awareness will enhance human productivity, making tasks faster, more efficient, and highly personalised.

Generative AI will amplify the capabilities of AI agents by enabling them to generate insights, create content, and adapt their responses dynamically. AI agents will become trusted collaborators across multiple domains as they gain deeper reasoning capabilities. Different industries, from business automation and robotics to healthcare and scientific research, will benefit from seamless human-AI interaction.