Generative AI (GenAI) and low-code development are reshaping the software landscape, empowering users across various functions to design and implement intelligent and customised solutions seamlessly. These innovations are breaking barriers, allowing businesses to craft tailored workflows and experiences with the speed and convenience of ready-made tools, without compromising on flexibility or creativity.

Low-code, which is a software development approach that uses visual interfaces and drag-and-drop tools instead of extensive manual coding, allows users, especially those without deep programming skills, to build applications quickly and with minimal hand-coding. Similarly, agentic AI refers to artificial intelligence systems that behave similar to agents, and can perceive their environment, make decisions, take actions, and pursue goals over time, without human intervention. The success of low-code platforms serves as a blueprint for enterprises to explore and adopt agentic AI, paving the way for greater agility and innovation.

Low-code and no-code platforms can potentially enable users across various departments such as marketing, HR, or operations, to create custom solutions without relying on full-time developers. These platforms offer intuitive, visual interfaces and drag-and-drop tools that drastically reduce the complexity of app development. Embedded AI further empowers users to accelerate every stage of the software development life cycle – from ideation and design to deployment, governance, and continuous optimisation.

One of Australia’s largest financial institutions redefined customer experience using low-code tools to deploy 25 customer-centric enterprise applications within just 18 months. These applications cover critical areas such as ATM management, credit card services, fraud detection, dispute resolution, loans, mortgages, and more. A prominent global finance company specialising in insurance and reinsurance implemented a low-code app that allows sales managers to have a comprehensive view of essential data and team activities. The app delivers sales reports 50 per cent faster compared to the previous system, enabling staff to focus on critical areas and drive better results.

While traditional AI requires explicit prompts to generate results, agentic AI can analyse situations, develop strategies, and execute tasks in parallel; bringing in true autonomy and adaptability. Without needing human prompts or input for direction, it can set goals, plan multi-step actions, adjust strategies in real-time, and interact with its environment. This makes it ideal for complex and dynamic tasks.

Agentic AI has already been deployed in areas like cybersecurity, where autonomous agents can proactively identify and respond to threats. Human resources departments are leveraging it to streamline recruitment processes, from initial screening to predicting candidate success. The financial industry benefits from its ability to analyse complex market data for fraud detection and optimised trading. Supply chains are becoming more efficient through AI agents that can dynamically adjust routes and manage inventory, while marketing teams are employing these technologies to personalise customer interactions and optimise campaign performance. Even healthcare is experiencing the benefits, with AI agents assisting in patient data analysis and real-time condition monitoring—monitoring ICU patients’ vitals to alert staff of early deterioration, analysing medical records to identify individuals at risk for chronic diseases, supporting radiologists in detecting tumours from scans, and helping primary care providers with symptom triage and care recommendations.

While there is an overlap in philosophy and purpose between low-code platforms and agentic AI, both are used to empower users to create and scale solutions tailored to rapidly evolving needs. Both technologies emphasise flexibility and modularity, enabling organisations to innovate quickly, integrate new features seamlessly, and grow systems organically.

Despite their promise, integrating agentic AI into workflows comes with challenges. Data privacy and security are key issues. Autonomous AI systems rely on vast amounts of high-quality data, raising concerns around transparency and regulatory compliance. Also, while low-code platforms reduce technical barriers, understanding agentic AI’s decision-making requires new competencies such as ethical oversight, prompt design, and system monitoring. For instance, in financial services, agents that autonomously flag transactions for fraud must be carefully monitored to avoid bias or false positives that could impact customers. In healthcare, AI agents assisting in clinical decisions need robust validation and explainability to ensure that recommendations align with medical guidelines and do not compromise patient safety.

In addition, adopting agentic AI demands a cultural shift, as teams must embrace AI as a collaborator rather than a mere tool. This requires strong leadership and structured onboarding processes.

To navigate these challenges, enterprises can draw inspiration from the success of low-code platforms. By empowering users, providing accessible tools, and fostering continuous learning, organisations can pave the way for the widespread adoption of agentic AI and future-ready innovation.

The proven success of low-code platforms in democratising software development offers a clear blueprint for the adoption of agentic AI in enterprises. By combining the accessibility of no-code tools with the autonomy and intelligence of AI, businesses can create richer, more responsive applications that meet the demands of a fast-evolving market. This convergence does not just streamline operations; it redefines what’s possible in terms of agility, personalisation, and innovation.

To fully realise this potential, enterprises must prioritise role-specific skilling and AI literacy initiatives across teams. By building a digitally fluent workforce, organisations can empower a broader range of users to confidently adopt and work alongside agentic AI, accelerating transformation and securing a competitive edge in the AI-driven future.

The author is Associate Director – AI/Data Science, Great Learning