Bitclever
rpaPublished on July 13, 20266 min read

AI Coding Agents Don't Eliminate Low-Code — They Make It More Essential

With the rise of AI coding agents, UiPath argues that low-code gains even more relevance: not for ease of building, but for governance.

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AI Coding Agents Don't Eliminate Low-Code — They Make It More Essential
Bitclever AI Research
Author: Bitclever AI Research ## Executive Summary A recent narrative suggests that AI coding agents have made low-code obsolete, since anyone can now generate functional code from natural language. UiPath challenges this view, arguing that the real challenge is not who can build automations, but who can govern them — and this is precisely where low-code becomes more valuable than ever. ## What Happened In recent months, the idea that AI coding agents have made low-code dispensable has gained traction. If a user with no technical background can describe a need in natural language and get functional Python code, why bother with a low-code visual canvas? Raghu Malpani, Chief Technology and Product Officer at UiPath, acknowledges that a genuine transformation is underway. Coding agents have significantly lowered entry barriers: developers who once spent hours on syntax and initial scaffolding now move at a much higher speed. At the same time, professionals who never saw themselves as "builders" — product managers, operations leads, financial analysts — are now able to create functional automations, with AI translating their intentions into executable code. Malpani describes this shift as a generational transition in how software is built. According to him, tasks that once required weeks or months of development are now delivered in the same timeframe with a multiplied volume of output. "Software is just eating the world faster," he summarises. As coding agents evolve from small, discrete tasks to the autonomous execution of complex functions, this acceleration effect tends to intensify in a compounding way. However, UiPath's article stresses that this acceleration generates its own new problems — and it is precisely at this point that the low-code discussion becomes more relevant than ever. ## Why This Matters UiPath's central argument is that the decisive question is not who can build agents, but who can govern them. When more people can build agents faster, the number of agents an organisation needs to oversee doesn't decrease — on the contrary, it increases. And most of the people responsible for that oversight — compliance officers, operations managers, business-line leaders — don't read Python code. This, according to Malpani, is the point frequently overlooked in the debate: the narrative that "low-code is dead" focuses exclusively on the build phase, ignoring everything that happens after an agent goes into production — the 2am incident, the audit request six months later, the questions about what the agent is authorised to do and when that authorisation was granted. When a process spanning multiple departments and systems fails, someone needs to be able to quickly understand what happened. A visual flowchart is something an operations manager, a compliance officer or a business leader can interpret immediately; source code is not. The value of low-code, UiPath argues, was never solely about making building easier, but about making processes readable and understandable for all stakeholders. As coding becomes easier, this value doesn't diminish — it amplifies, because the pace of agent creation is growing faster than the ability to audit them. As Malpani summarises: "I believe that in a world where non-coders can also understand the low-code experience, the value of low-code goes up; it can translate intent expressed in natural language into visual business logic that these people can understand." There is also an additional dimension, particularly relevant at enterprise scale: complex processes spanning multiple systems, departments and human decision points intrinsically benefit from structural visibility — even when built by experienced developers. Visualising how a process branches and where human decisions intervene is not a concession to non-technical users, but a design requirement for any enterprise-grade system. Additionally, visual tools allow constraints to be enforced at design time, catching issues before deployment — unlike code, where problems tend to only surface at runtime. ## Business Impact For organisations accelerating their adoption of AI agents and automation, this debate carries concrete practical implications: - **Multiplication of governance risk**: as more employees — technical and non-technical — build automations, the number of assets requiring governance grows rapidly, demanding robust oversight structures from the outset. - **Need for cross-functional readability**: automated processes need to be understandable not just by those who built them, but by compliance, audit, operations and business leadership — functions that rarely have coding skills. - **False dilemma between low-code and code-first**: platform choice shouldn't force organisations to pick between visual building and code-based building. Teams should be able to start with a visual canvas and migrate to code, or vice versa, without losing logic, structure or intent. - **Early risk detection**: design-time verification capabilities and guardrails applied before production significantly reduce the operational and compliance risk associated with autonomous agents. - **The right strategic question**: instead of asking "low-code or code-first?", organisations should be asking whether their platform supports both paths and whether they can consistently govern everything built, regardless of the method used. ## Bitclever Perspective At Bitclever, we closely follow the evolution of automation and low-code platforms — including solutions such as OutSystems, Appian and enterprise RPA — and we recognise in this debate a central point for any mature automation strategy: rapidly building automations is only half the problem; the other half, often underestimated, is sustainable governance throughout the lifecycle of the agent or process. We help organisations design automation architectures that don't force a choice between agility and control. This involves implementing visibility and audit layers that allow any business stakeholder — not just technical teams — to understand what an automated process does, why it does it, and who authorised that logic. Our consultative approach focuses on three essential pillars: defining clear governance criteria from the design phase onward; implementing design-time checks and guardrails before production deployment; and creating documentation and visualisation structures that make processes readable for compliance, operations and leadership teams. With this foundation, companies can fully benefit from the acceleration brought by AI coding agents without compromising auditability, control and accountability. ## Conclusion AI coding agents haven't put an end to low-code — they've clarified its true purpose. In a context where anyone can generate functional code, the value of low-code no longer lies in ease of building, but in the ability to make every automation understandable, auditable and governable for everyone responsible for it. The organisations that will stand out won't be the ones that chose the right "build path", but the ones that ensured everything they build remains visible, understandable and under control — regardless of how it was created.

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