Agentic AI Is Quietly Replacing Developers  ## Metadata - Author: [[Neel Sundaresan]] - Full Title: Agentic AI Is Quietly Replacing Developers - Category: #articles - Document Note: --- tags: [#AI, #developer-experience, #agentic-AI] Author: Richard MacManus Source: The New Stack Reference: [Agentic AI is Quietly Replacing Developers](https://thenewstack.io/agentic-ai-is-quietly-replacing-developers/) date created: {{2025-07-14}} {{19:30}} --- # Title: Agentic AI is Quietly Replacing Developers ## Table of Contents: - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📖 References](#-references) - [🗣️ Paraphrase](#-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB (Diversity, Equity, Inclusion, and Belonging) Assessment](#-deib-diversity-equity-inclusion-and-belonging-assessment) - [✅ Logical validity](#-logical-validity) - [🔍 DARVO Analysis Prompt](#-darvo-analysis-prompt) - [🔍 SEO Evaluation](#-seo-evaluation) - [🎯 Conclusion](#-conclusion) ### References: - Richard MacManus. "Agentic AI is Quietly Replacing Developers." The New Stack. 2025. [Link](https://thenewstack.io/agentic-ai-is-quietly-replacing-developers/) ## 📝 Summary: This article explores the rise of agentic AI — autonomous AI agents capable of performing sophisticated tasks without constant human intervention — and its implications for software developers. It examines whether these AI agents are enhancing developer productivity or quietly pushing them out of critical roles. ## 📖 Main Content: - Defines "agentic AI" and how it differs from traditional code suggestion tools like Copilot. - Discusses the shift from code completion to full-on code generation and deployment. - Analyzes developer reactions: enthusiasm for productivity vs. fear of being replaced. - Considers philosophical and practical implications for code ownership, quality assurance, and maintenance. - Compares historical shifts in developer tools (like moving from assembler to high-level languages) with current AI-driven changes. ### 🗒️ Notes - **Agentic AI Definition** - Unlike passive tools, agentic AIs are self-initiating and capable of completing entire development workflows. - They do not just assist; they act on behalf of humans. - **Shift from Augmentation to Automation** - Previous AI tools focused on augmenting developer tasks. - New agents can plan, code, test, and even deploy — potentially without human review. - **Developer Reactions** - Split between optimism (faster delivery, fewer mundane tasks) and skepticism (loss of craft, job security). - **Philosophical Tension** - Raises questions about authorship: Who "owns" code written by an AI? - Ethical considerations in accountability when AI code causes failures. - **Historical Parallels** - Mirrors the progression from manual memory management to garbage collection — abstraction improves productivity but removes certain controls. - **Industry Impact** - Startups and enterprise teams exploring agentic AI to reduce costs and time to market. - Potential for reshaping team structures, with fewer traditional developer roles. - **Open Source Concerns** - Community contribution models could be threatened as AI can automatically generate large volumes of code. - Potential reduction in community engagement and contributor identity. - **Governance Implications** - Need for new governance frameworks to address AI decision-making in software pipelines. - Legal challenges around intellectual property and liability. ## 📖 References: - The New Stack article - Historical trends in software abstraction layers - AI ethics discussions in developer forums ## 🗣️ Paraphrase: 1. The new wave of AI tools goes beyond helping developers write code faster — they can now design, build, and deploy software almost entirely on their own. This fundamental shift moves AI from a passive assistant to an active agent. 2. While some see this as a huge productivity gain, others worry that we are heading towards a future where the unique craftsmanship of human developers is sidelined, with AI systems quietly taking over. ## 📰 Newsletter: **🚀 AI Agents & The Future of Dev Workflows!** Agentic AI is no longer science fiction — it’s reshaping our dev jobs today. These tools don’t just suggest code snippets, they autonomously plan, write, and deploy entire systems. While this promises massive gains in productivity, it raises questions about our roles as creators. Are we becoming supervisors of bots rather than builders? What does this mean for open source, team dynamics, and our sense of craftsmanship? 👉 Read the full analysis: [Agentic AI is Quietly Replacing Developers](https://thenewstack.io/agentic-ai-is-quietly-replacing-developers/). ## ✅ Action Items: - Evaluate current usage of AI tools in WordPress workflows and broader open source contributions. - Organize discussions on balancing automation with community-driven development values. - Develop educational materials to help contributors understand AI capabilities and ethical considerations. - Advocate for governance policies around AI-generated code in open source ecosystems. ## 🧪 Hypotheses: - Widespread adoption of agentic AI will reduce the number of junior developer roles while increasing demand for AI oversight positions. - Open source community engagement will decline as AI-generated code reduces individual contributor visibility and ownership. ### 📊 Methodology: - Surveys with open source contributors on AI usage and perceptions. - Longitudinal studies on project contributions before and after introducing agentic AI. - Codebase analyses to measure AI vs human-written code contributions. ### ❓ Questions: - How can we ensure quality and maintainability when AI generates code? - What frameworks are needed to handle accountability for AI errors? - How do we maintain human creativity and ownership in an AI-driven development world? ### 🔮 Future Research: - Comparative studies on project success rates with and without agentic AI integration. - Exploration of hybrid models where AI and human co-create while preserving developer identity. - Legal research on intellectual property rights for AI-generated software. ### 💡 Implications: - Could redefine job descriptions in tech. - May shift the focus of developer education from coding to system design and AI supervision. - Could influence funding models and sustainability strategies in open source. ### 🧠 Learning Styles Assessment: - **Readability**: Advanced, targeting experienced developers (reading level ~college graduate). - **Visual Learners**: Lacks visuals; could benefit from architecture diagrams showing AI workflow. - **Aural Learners**: No audio content; recommend adding podcast or audio version. - **Verbal Learners**: Strong narrative style; uses clear examples and analogies. - **Physical Learners**: Minimal interactivity; suggest adding code labs or sandbox tasks. - **Logical Learners**: Excellent for analytical minds; structured reasoning and historical comparisons. - **Social Learners**: Could include discussion forums or group case studies. - **Solitary Learners**: Good for independent reflection; could add guided journaling prompts. - **Naturalistic Learners**: Minimal; might incorporate environmental analogies for broader appeal. ### 📊 Accessibility Assessment: - **Screen Reader**: Likely good (HTML-based), but diagrams lacking alt text. - **Keyboard Navigation**: Should be verified. - **Contrast & Readability**: Generally high, but should confirm compliance. - **Text Resizing**: Layout seems responsive. ### 🌍 DEIB (Diversity, Equity, Inclusion, and Belonging) Assessment: - **Cultural Relevance**: Global developer perspective; more regional case studies would help. - **Inclusive Language**: Generally inclusive; no gendered or exclusionary terms noted. - **Neurodiversity Support**: Could improve by simplifying long paragraphs and adding clear breakpoints. - **Representation**: Limited perspectives; no mention of diverse developer voi - Summary: Discover how agentic AI is transforming software development from manual coding to intelligent automation. - URL: https://thenewstack.io/agentic-ai-is-quietly-replacing-developers/ Featued image for: Agentic AI Is Quietly Replacing Developers For decades, elegant code has been the hallmark of great software development — code that is streamlined, efficient, well-documented, reusable and expertly crafted. Early in my career, I was trained to prioritize writing tools and program generators over directly writing programs. This approach led to my involvement in numerous innovative initiatives that automated software modernization and improved productivity. By developing tools that generated transpilers, we were able to modernize millions of lines of code efficiently. Today, generative AI [models can create code from human language](https://thenewstack.io/ai-coding-human-engineers-are-more-important-than-ever/), making development more intuitive. It’s not surprising that concepts like [vibe coding](https://thenewstack.io/to-vibe-or-not-to-vibe-when-and-where-to-use-vibe-coding/), which emphasizes intuition, aesthetics and the experiential impact of code, have gained traction. Though despite the expansion of [low-code and no-code systems](https://thenewstack.io/low-code-vs-no-code/), most software is still crafted by engineers. #### How Agentic AI Is Revolutionizing Software Development With the rapid evolution of AI, from traditional AI to [generative AI](https://thenewstack.io/ebooks/generative-ai/how-generative-ai-transforms-software-development/) and now agentic AI, the focus is shifting from merely coding logic to automating the entire software development process. Automated testing, continuous documentation, automated pull requests and CI/CD systems, and AI-driven performance and [security issue tracking](https://thenewstack.io/how-linux-kernel-deals-with-tracking-cve-security-issues/) and patching are showing great promise. Building on these advancements, modern IT systems — including on-premises, hybrid cloud and superscalar environments—offer opportunities beyond simple reporting and analysis. These systems enable advanced problem detection, localization, correction, optimization, resilience and prevention, all facilitated by modern AI technologies. For example, our clients use [IBM’s](https://www.ibm.com?utm_content=inline+mention) suite of automation products to leverage both traditional and generative AI to transform raw data into intelligent systems. This powerful combination enhances IT operations by enabling sophisticated, data-driven solutions across the entire technology stack. Agentic AI takes this a step further by executing tasks independently. Instead of writing code, it can handle the elements of building and running software automatically. This allows engineers to design AI teams where individual AI agents specialize in different tasks but collaborate to validate, refine and improve solutions. These agents, powered by [large language models](https://thenewstack.io/what-is-a-large-language-model/) and other AI systems, can communicate, share data and iteratively enhance their outputs. This continuous cycle of problem-solving will support correctness, scalability and efficiency. Ultimately, the true engineering excellence of the future will lie in elegant automation, driven by the capabilities of advanced AI systems. #### The Rise of Automation-First Development This paradigm shift brings new opportunities and challenges for IT leaders. They will need to address key questions, such as: How can they encourage their team to use AI to achieve end-to-end automation? How can they ensure that the right controls are in place to enhance efficiencies and unlock innovation, rather than creating new problems? What tools and experiences can facilitate AI integration earlier in the software development life cycle? ##### 3 Steps to Pivot Your Team from Code to Automation ###### 1. Focus on Outcomes, Not Lines of Code To achieve excellence in automation, shift your focus from the amount of code written to the number of processes automated and [time saved](https://www.ibm.com/case-studies/ibm-software-team). Implement metrics that [measure these outcomes to track progress effectively](https://thenewstack.io/set-goals-and-measure-progress-for-effective-ai-deployment/). Create a culture that relies on data and experimentation to drive decision-making. Encourage engineers to think about how automation, especially with AI, can improve the speed and scalability of their work, rather than just focusing on the technical details. Promote a culture where automation is seen as a way to enhance and support the team, rather than replace it. Last year, a small team at IBM consisting of experienced and newly hired product developers took over code repositories that contained approximately 750 JavaScript files without any documentation. The team’s goal was to figure out what the code was and exactly what it did. To start, the team members created a proof of concept that used [watsonx Code Assistant](https://www.ibm.com/case-studies/ibm-software-team) to document about 1,000 lines of code across nine different files. This allowed them to understand and document the content of each file in seconds, resulting in a time savings of over 90% for this specific task. The time saved with this specific task has the potential to scale up into other use cases across the enterprise and culminate in thousands of hours saved. ###### 2. Build with Automation-First Principles Integrate automation into the core development workflow from the start. Encourage teams to automate deployment, testing, and monitoring as a regular part of their development process. Utilize AI-powered development assistants to speed up the creation of high-quality software systems. Prioritize low-code and no-code solutions for repetitive tasks, freeing up engineers to focus on high-value work and boosting overall efficiency and innovation. For example, [Ensono](https://www.ibm.com/case-studies/ensono), a global managed service provider, leveraged IBM watsonx Code Assistant for [Red Hat](https://www.openshift.com/try?utm_content=inline+mention) Ansible Lightspeed to consolidate the company’s automation platform, perform direct database migrations, and establish a single source of truth for its Ansible content. This streamlined approach allowed Ensono to complete [28 million tasks and save developers](https://thenewstack.io/the-roi-of-speed-how-fast-code-delivery-saves-millions/) more than an estimated 100,000 hours in just one year, significantly enhancing client incident response, applications performance and overall efficiency. ###### 3. Develop AI-Ready Skills and Mindsets Transform developers into automation engineers by focusing on how AI and automation tools can augment their expertise. Invest in AI and automation training to help [teams build expertise in AI-driven development](https://thenewstack.io/building-high-performance-software-development-teams-7-tips/), observability, and self-healing systems. Encourage cross-functional collaboration between IT, operations and business teams to ensure automation aligns with strategic goals. Establish a feedback loop to encourage continuous iteration and improvement of automation strategies. #### From Developer to Automation Engineer The next era of IT isn’t about coding harder, it’s about automating smarter. The best IT leaders recognize that their teams’ greatest value is in building intelligent, adaptive systems that scale effortlessly and respond proactively to change. By shifting focus to elegant automation, developers are empowered to solve bigger problems, create greater impact, and future-proof organizations. The transition from elegant code to elegant automation represents a significant shift in the software development landscape. It requires a new mindset, a new set of skills and willingness to embrace AI as a collaborative tool rather than adopt AI as a replacement for human expertise. As we move forward, successful IT teams will be those that effectively harness the power of AI to create elegant, efficient, and scalable automated solutions., Agentic AI Is Quietly Replacing Developers, Neel Sundaresan, 01k020kwrn2hawc3jeyqvxwr4v.