How AI and low-code are changing software development
The landscape of software development is undergoing a seismic shift, driven by rapid advancements in artificial intelligence (AI) technology and its integration into the software development lifecycle (SDLC). AI is a key player, and the potential is huge for it to automate and optimize code generation, testing and deployment.
As businesses strive to enhance and further support digital transformation, AI's role in enabling faster, more efficient, and higher-quality software development has tremendous appeal. It offers multiple opportunities to significantly alter (or even eliminate) traditional processes, compress the SDLC, and foster multidisciplinary approaches to enhancing productivity and driving innovation.
This blog, inspired by our on-demand webinar, The future of AI-driven software development: Featuring a perspective from Forrester, explores the effects of AI on delivering and generating applications and the opportunities it presents.
What’s the effect of generative AI on software development?
Generative AI is known for allowing developers to write prompts in natural language and receive code snippets almost instantaneously, transforming how development teams approach coding tasks. But since it first burst on the scene in 2022, generative AI’s role in software development has grown. It’s now enhancing efficiency and quality by automating code generation, testing, documentation, and deployment.
Delivering features from standardized templates
Generative AI automatically produces detailed product features using standardized templates, helping you move from concept to implementation faster. The technology excels at creating consistent solution architectures and breaking down complex requirements into clear user stories with acceptance criteria.
Improved testing
Testing becomes more thorough when generative AI handles wireframe creation, generating comprehensive test cases that cover scenarios teams might miss, and producing test scripts for quality assurance. It synthesizes synthetic data for regression testing while protecting sensitive information, and helps with rapid troubleshooting by comparing expected versus actual outputs.
Helping the humans
While generative AI can't build complex applications from scratch yet, it enhances human developers through intelligent code completion, refactoring suggestions, and automated reviews. It also handles documentation tasks like release notes and user guides, making handoffs between teams smoother and troubleshooting more efficient.
In addition, the true potential of generative AI is realized when integrated with other AI-driven tools, creating a cohesive development environment. For example, combining generative AI with agentic AI, which autonomously checks, edits, and refines code, streamlines workflows, and minimizes context switching and tool fatigue.
Going deeper into development with agentic AI
The true power of AI extends beyond generating code to understanding and optimizing the entire development process. Agentic AI exemplifies this by not only generating code but also making recommendations for editing, fine-tuning, and automating tasks such as code conversion and refactoring. This comprehensive integration of AI is creating more cohesive and streamlined development workflows.
Not your ordinary agents
Agentic AI offers agents that function like team members with reasoning, planning abilities, and contextual understanding across different tasks. They have the autonomy to make decisions, interact with other agents, and adapt to changing circumstances. Simple tasks like summarizing code or drafting documentation can run independently with completion notifications.They can also actively orchestrate complex processes from security checks to compliance reviews.
Tackling specific jobs
Specialized agentic AI is emerging for specific tasks like code modernization, security vulnerability detection, test generation, performance optimization, and documentation, using models optimized for particular jobs rather than general-purpose solutions. Tasks that once took weeks now complete in hours, freeing developers to focus on complex problem-solving and creative work that requires human insight.
How to capitalize on agentic AI’s opportunities
At times, generative AI and agentic AI can appear to be working at cross-purposes, especially when individual solutions are used for them. In addition, there are the questions of ethics, governance, security, and keeping humans in the loop. A platform approach to generative AI and agentic that includes low-code provides an opportunity to smooth the unevenness of technology that has grown super fast.
Using generative AI, agentic AI, and low-code for application generation
Low-code platforms provide a logical foundation for AI-driven development that include generative, agentic, and any other AI that’s on the horizon. These platforms use visual models to represent software components, simplifying the understanding and manipulation of underlying code for developers.
By offering intermediate representations, low-code platforms bridge the gap between natural language prompts, AI agents, and actual code, enabling the generation of complete applications. This approach accelerates development and ensures higher quality and consistency, as visual models are inherently more interpretable and reliable than raw code. The result? Application generation (AppGen), a term John Bratincevic, Principal Analyst, and Diego Lo Giudice, VP, Principal Analyst, Forrester Research, use to describe platforms that “represent the evolution of practical platform engineering to take full advantage of AI (especially generative AI) while mitigating its drawbacks.”*
Platforms that combine AI-driven development with low-code mark a significant advancement in software development. AppGen platforms use AI to generate entire applications rather than isolated code snippets, addressing the limitations of current AI tools that often operate in silos and require extensive human intervention for integration and optimization. By combining various AI capabilities into a unified platform, AppGen helps developers avoid context switching and tool fatigue. This integration eliminates traditional barriers, making complex projects more feasible and cost-effective.
AppGen with AI and low-code is tomorrow’s development reality
True application generation brings together the best of the technologies we’ve just explored. It combines the automated content creation of generative AI, the smart decision-making of agentic AI, and the visual simplicity of low-code platforms. This creates a new way of building software that feels as simple as describing what you want to create.
Application generation platforms use specialized agents working behind the scenes while giving users familiar, low-code interfaces. The result is software development that works for everyone, from individual visionaries creating simple workflows to professional teams building enterprise applications. This is the next step in making software creation truly accessible.
Want to dig deeper into the convergence of AI, low-code, and software development? Watch our on-demand webinar, The future of AI-driven software development: Featuring a perspective from Forrester or explore the OutSystems AI-powered low-code platform.
*Bratincevic, John and Diego Lo Giudice, “The Rise Of Application Generation Platforms,” Forrester Research, May 7, 2024.
https://www.forrester.com/blogs/the-rise-of-application-generation-platforms/
