Rethinking Low Code, No Code in the era of GenAI

Tools that generate code have always been on my persona non grata list. These have included the likes of UML-driven coding and analysis platforms and the plethora of low-code, no–code (LC/NC) platforms that have emerged as frameworks for UX and data handling becoming ever more powerful. The expansion of the LC/NC field was certainly enabled by the standardization of identity and access management protocols and standards (IAM), the emergence of highly productive Web UX frameworks, and the strong enterprise adoption of Agile development.  I won’t mention product names or vendors. Best to stay out of hot waters.  Marketing engines, went into high gearing, often painting old products, born decades ago,  with a veneer of AI shine, 

In a recent discussion with a CIO at a specialty insurance company , I was asked to comment specifically on the  low-code, no-code topic.  My response. LC/NC tools are useful for certain types of prototyping exercises.  But they can quickly become elements of friction once you enter the space of enterprise level integration and complexity.  I have heard this same verdict from a few of my CTO friends.  In my days, as a full-time developer, more than once did I inherit a corpus of tool generated code that I had to maintain after pushing tools beyond their design specifications. A fun predicament? Not at all. 

But my negative view about LC/NC is changing due to the SuperPowers that GenAI is bringing to the table. See my previous articles on this topic. The very concept of LC/NC development itself is changing.  And I am liking what I am seeing, what I am experiencing as I build new systems and products. Indeed one could now say that  Computer Aided SW Engineering is being reborn as AI-first toolchains, hatched as a set of composable, extensible  agentic systems that will allow the creation at scale of AI Factories, accelerated product prototyping. Yes, that was a mouthful. But it is happening, and doing so at a pace never seen before.  In today’s world of GenAI, LC/NC platforms are going to be less about fancy, WYSIWYG screen and code editors, and more about  a conversational , iterative modality that teams - smaller in size - can use to develop their products. Natural language will be the default mode of interaction, the default manner in which humans and computers, almost as peers, work together. Agentic coding assistants, won’t live and operate strictly within the confines of an developer IDE. They will instead, operate in parallel, exploring, analyzing and interpreting a complete set of information of specifications, codebases written in different languages, styles and for different purposes. These agentic systems through MCP integration will act as detached coding CoPilots that act in developers terminals, and on internal developer platforms (see emerging IDPs). Coding agents and sub-agents  will be responsible for the heavy lifting of writing , through LLM powered GenAI, the computer code and instructions that will be required to implement a system.  

When looking at where software development is heading with these new considerations, it will not be a stretch to say that we are in a new era of Low Code , No Code tooling and SW development.   Code is being written,  but just not by us.  The modern SW developer, to use an analogy from the work, will become a conductor, and composer, ensuring that the orchestra can execute the beautiful music that the mind has envisioned and communicated through words, sounds and images.

In this new world, the LC/NC notion of citizen developers also has a new chance to re-emerge, to excel. Conversational AI will make business users co-creators of new applications, and systems. They will work more closely than ever with engineers with training in SW best practices, and architectural foundations, They will co-create not just test, validate and approve. Teams will become hybrid by default. Workforces will become hybrid congregations of human experts and agentic systems, working side by side.

Conclusions 

The way software engineering is executed is drastically changing.  It has happened before. This time however, change is happening faster than ever compared to what was experienced after the Agile revolution took hold across enterprises. The change in modality is fascinating, and is fueling positive trends in productivity. It is also bringing out speculation, and emerging analysis that for some technologists predict the demise of an entire global industry, with impactful labor implications.  I tend to be more optimistic about where things are headed. In my previous posts I have talked about  how superpowers are emerging. Like Reid Hoffman, entrepreneur of LinkedIn fame,  who brings us the concept of SuperAgency,  essentially a view that AI is an intelligence amplifier, I am inclined to view AI as a positive force. 

As a realist, however, I will remain very aware of unavoidable bumps in the road to a more steady state, and how this technological iteration will impact some job sectors.  The very recent August 2025 report out of Stamford university provides quantitative research on AI-exposed sectors, such as SW development and customer service.   According to its findings entry-level workers have been disproportionately affected by the AI shift. The culprit? Automation vs augmentation is the key characteristic that infusion of AI into our work world, that will drive. This is not very different from what happened in the industrial sector, where automation since the 1970s  — think robotics -  impacted , and will continue to impact jobs with physical interaction to it. In the software world, materials are no longer tangibly physical, they are simply digital renditions of concepts.  Adaptation is the key remedy.  We should not be resigned to a "survival of the fittest” scenario. We should think about resilience plus reinvention through reskilling, continuous learning , and interdisciplinary interests.

Let’s just ask ourselves the question: would we, the World, be better off if the industrial revolution had not happened ?  Or for that matter, would we be better off without transistors, integrated circuits and EUV lithography?  

Reference articles by the author

Part 1 -  https://www.linkedin.com/pulse/how-genai-granting-superpowers-developers-knowledge-alike-bisignani-c9i2e/

Part 2 -   https://www.linkedin.com/pulse/how-genai-granting-superpowers-developers-knowledge-alike-bisignani-udcfe/