How AI Agents Are Changing Software Development

𝗔𝗜 𝗶𝘀 𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱. 𝗜𝘁 𝗶𝘀 𝗮𝗹𝘀𝗼 𝗺𝗮𝗸𝗶𝗻𝗴 𝗶𝘁 𝗲𝗮𝘀𝗶𝗲𝗿 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝘁𝗵𝗶𝗻𝗴. Prototypes that once took days can appear in minutes. Iteration loops are compressing, and the distance between idea and code is shrinking. Yet the most important change is not speed. It is where the constraint now sits. The bottleneck is no longer coding. I’ve written a new post: 𝗪𝗵𝗮𝘁 𝗔𝗴𝗶𝗹𝗲 𝗟𝗼𝗼𝗸𝘀 𝗟𝗶𝗸𝗲 𝗶𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗧𝗲𝗮𝗺𝘀 The article explores how AI agents are reshaping Agile practice and why many organisations are still optimising the wrong thing. When implementation effort drops, the limiting factors move upstream. The real constraints become clarity of problem definition, quality of decision-making, speed of feedback, and architectural coherence. Agentic teams recognise this shift. They stop optimising for build speed and start optimising for learning speed. Planning focuses less on tasks and more on intent. Hypotheses about value replace detailed task breakdowns. Cadence remains important, not as a delivery schedule but as a learning rhythm that keeps teams aligned. The skill mix evolves as well. Developers spend less effort on syntax and more on architecture, testing, and judgment. Leaders spend less effort tracking activity and more effort clarifying direction and priorities. Architecture becomes the guardrail that allows experimentation without creating fragile systems. Agile does not disappear in this model. It matures. Its purpose becomes clearer: helping teams learn quickly enough to keep pace with reality. Link: https://lnkd.in/eXAj324R If coding is no longer the bottleneck, what is the real constraint inside your organisation? #Agile #AI #SoftwareEngineering #TechnologyLeadership #CIO #CTO #AgenticAI #BusinessAgility

Most conversations about AI in software development stop at code completion. At AMD, we’re going much further.   Over the past several years, we’ve worked closely with both junior and senior developers across our software teams to understand what really drives productivity, velocity, and code quality. Their needs go far beyond autocomplete. Junior engineers want faster onboarding and guided exploration. Senior developers asked for help reasoning about architectural trade-offs, optimizing complex pipelines, and managing risk at scale. Productivity gains don’t come from keystroke savings, they come from intelligence embedded throughout the stack.   This is where agentic AI comes in. Instead of passively suggesting snippets, AI agents now play active roles in design exploration, automated validation, performance profiling, and release optimization. These are not just assistants - they’re collaborators, co-engineering systems alongside us.   By aligning these AI systems with our hardware accelerators and open software stack, we’re reimagining what development looks like from writing code to reasoning about it. The future of software engineering isn’t about typing faster - it’s about augmenting every stage of engineering with intelligence, purpose-built for the problems we solve.   Read my new article for IEEE Spectrum, “AMD Takes Holistic Approach to AI Coding Copilots”: https://lnkd.in/gNfyg2xJ #softwaredev #IEEE #AgenticAI #softwareengineering

When I started coding in the 70s, we dreamed of tools that could understand our intent and help us build faster. Today, that dream is becoming reality – but in ways we never imagined. The rapid evolution of #AI in #softwaredevelopment isn’t just about code completion anymore. It’s about intelligent systems that can understand context, manage workflows, and even anticipate needs. At Booz Allen Hamilton, we’re witnessing a fundamental shift in how software is built. AI-powered development tools are becoming true collaborative partners, managing complex workflows end-to-end while developers focus on architecture and innovation. Tools like GitHub Copilot Enterprise and Amazon Q aren’t just suggesting code – they’re orchestrating entire development cycles, from initial design to deployment and security risk mitigation. The impact is undeniable. Development teams leveraging advanced AI tools are accelerating tasks and enhancing their workflows significantly. But speed alone isn’t enough – #security remains paramount. By integrating AI tools with our security frameworks, we’re mitigating risks earlier and building more resilient systems from the ground up. What excites me most is the emergence of autonomous development agentic workflows. These systems now understand project context, manage dependencies, generate test cases, and even optimize deployment configurations. Booz Allen’s innovative solutions, like our multi-agent framework, push this concept further by coordinating specialized AI agents to address distinct challenges. For example, Booz Allen’s PseudoGen streamlines code translation, while xPrompt enables dynamic querying of curated knowledge bases and generates documentation using managed or hosted language models. These systems aren’t just tools – they’re collaborative problem-solvers enhancing every stage of the software lifecycle. Looking ahead, we’re entering an era where AI-native development becomes the norm. Industry analysts predict a significant uptick in adoption, with a growing number of enterprise engineers embracing machine-learning-powered coding tools. At Booz Allen, we’re already helping our clients navigate this transition, ensuring they can harness these capabilities while maintaining security and control. The question isn’t whether to adopt these tools but how to integrate them thoughtfully into your development ecosystem. How do you see the future of AI in software development? *This image was created on 12/11/24 with GenAI art tool, Midjourney, using this prompt: A human takes very boring data and puts it into a machine. Once it goes through the machine, it turns into a vibrant and sparkling tapestry.

AI is changing software development, but not in the way many expected. It’s not replacing programmers—it’s shifting the skills they need to succeed. Programming has always been about solving problems, not just writing code. Now, with AI in the mix, the ability to define problems clearly, structure solutions effectively, and debug complex systems is more critical than ever. AI can generate code, but it can’t understand the nuances of a problem or the implicit assumptions behind a solution. That’s still up to developers. Debugging AI-generated code is harder than debugging your own. AI mistakes are different from human mistakes—often subtle, sometimes unpredictable. Code quality and maintainability still matter. Left unchecked, AI-generated code can lead to massive technical debt. The real shift isn’t about writing clever prompts—it’s about managing context. AI doesn’t just need instructions; it needs structured, detailed input. The best results come from those who understand the problem deeply and can translate that understanding into precise guidance. For junior developers, this means the learning curve is steeper. It’s no longer just about mastering syntax—it’s about understanding systems, debugging effectively, and structuring maintainable code. For senior developers, mentorship is more important than ever. The next generation of engineers won’t learn by just watching AI generate code; they’ll learn by working through complex problems with experienced guidance. Ignoring AI isn’t an option. But using it well means recognizing its limits, refining how we work with it, and staying focused on the fundamentals of good software development. AI isn’t the end of programming—it’s a new beginning. Mike Loukides, Tim O'Reilly

🚀 AI Is Rewriting the Future of Software Engineering—And Google Just Dropped the Blueprint AI isn’t just “assisting” engineers anymore—it’s co-creating with them. 📌 Google’s latest update on AI in Software Engineering pulls back the curtain on how deeply AI is embedded in its software development lifecycle—from code generation to planning, testing, and even reviews. Some 🔥 highlights: 30%+ of new code at Google is now AI-generated. Engineers are seeing 20–25% productivity gains using AI-powered tools. From internal IDEs to bug triaging systems, AI is quietly revolutionizing how engineering happens at scale. But what sets Google’s approach apart isn’t just the tools—it’s the philosophy: ✅ Select projects with measurable developer impact ✅ Embed AI into “inner-loop” workflows (where devs live day-to-day) ✅ Build feedback loops to constantly improve performance & trust ✅ Share learnings with the broader ecosystem (open papers, DORA reports) One of the most exciting frontiers? Agentic AI 🤖—systems that plan, act, and adapt on behalf of developers. Google's acquisition of Windsurf’s top talent into Google DeepMind signals serious intent here. These tools won’t just autocomplete your functions… they’ll soon handle full-stack code changes, migrations, and dependency resolutions—autonomously. 👨💻 This also means the role of the engineer is evolving. Welcome to the era of the Generative Engineer (GenEng)—where prompts, design thinking, human-AI pair programming, and strategic oversight replace routine code churn. Of course, challenges remain: ⚠️ Ensuring reliability & debugging AI-written code ⚠️ Avoiding misalignment with developer intent ⚠️ Managing trust, governance, and security across codebases But Google’s model—balancing speed with rigor—offers a practical path forward. 💬 So here’s my take: AI won’t replace software engineers. But engineers who embrace AI as a true partner? They’ll be 10x more valuable—because they’ll ship better software, faster, and at scale. If you're in tech leadership, now’s the time to: 🔹 Assess AI-readiness across your dev lifecycle 🔹 Define how productivity and quality will be measured 🔹 Empower teams with the right AI tools, context, and guidance The future of software isn’t about who writes the best code—it’s about who builds the smartest systems to write, verify, and evolve that code over time. 💡 Let’s not just use AI to write software. Let’s use #AI to reinvent how software gets written. #SoftwareEngineering #GenAI #DevOps #EngineeringLeadership #AItools #TechInnovation #AgenticAI #FutureOfWork #GoogleAI #ProductivityBoost #DevX #LLM #GenerativeEngineering 🚀👨💻🤝

Most developers still think AI helps you write code faster. That’s already outdated. The real shift happening in 2026 is this: AI Agents are starting to run the Software Development Lifecycle. Not just coding — but planning, testing, debugging, and deployment. Software development is moving from SDLC → ADLC (Agent-Driven Lifecycle). Here’s what actually changed 👇 📌 SDLC (The Traditional Way) The classic development model most teams still follow. • Planning → Design → Development → Testing → Deployment • Each phase happens sequentially • Humans manage every step • Requirement changes mid-cycle create chaos Testing usually happens after development, and feedback comes too late. 📌 ADLC (Agent-Driven Lifecycle) The new model emerging with AI agents. Instead of sequential work: • Agents write, refactor, and test code simultaneously • Requirements evolve dynamically • Multiple agents collaborate across tasks • Feedback happens in real time This turns software development into a continuous adaptive system. 🚀 6 Major Shifts Happening Right Now 1️⃣ Execution Driver From manual human execution → Autonomous AI agents handling tasks across phases 2️⃣ Planning From fixed scope and static PRDs → dynamic goals that evolve during development 3️⃣ Development Speed From sequential handoffs → multiple agents working in parallel 4️⃣ Testing From post-development QA phase → continuous automated testing during coding 5️⃣ Adaptability From mid-cycle disruption → agents re-planning in real time 6️⃣ Feedback Loop From post-project retrospectives → live monitoring and anomaly detection 📊 What This Means for Engineers This shift isn’t theoretical anymore. Companies experimenting with agentic coding workflows are already seeing major gains in execution speed. The developer role is evolving from: Code Writer → System Orchestrator Your job becomes: • defining goals • designing systems • supervising outcomes • handling edge cases ⚡ 5 Practical Ways Engineers Can Start Using Agents 1️⃣ Start with testing automation The lowest risk and fastest ROI for agent adoption. 2️⃣ Write clearer PRDs Agents execute exactly what you define. 3️⃣ Break work into parallel agent tasks Instead of one big task → create multiple agent workstreams. 4️⃣ Change how you review code Stop reviewing every line. Focus on logic, outcomes, and edge cases. 5️⃣ Build monitoring loops Let agents flag performance issues and anomalies automatically. The biggest shift in software development is not AI writing code. It’s AI running the development process itself. And the engineers who learn to design and supervise agent workflows will move 10× faster than those still coding the old way. #AI #AIAgents #SoftwareDevelopment #Engineering #TechLeadership #FutureOfWork

AI coding assistants are changing the way software gets built. I've recently taken a deep dive into three powerful AI coding tools: Claude Code (Anthropic), OpenAI Codex, and Cursor. Here’s what stood out to me: Claude Code (Anthropic) feels like a highly skilled engineer integrated directly into your terminal. You give it a natural language instruction, like a bug to fix or a feature to build and it autonomously reads through your entire codebase, plans the solution, makes precise edits, runs your tests, and even prepares pull requests. Its strength lies in effortlessly managing complex tasks across large repositories, making it uniquely effective for substantial refactors and large monorepos. OpenAI Codex, now embedded within ChatGPT and also accessible via its CLI tool, operates as a remote coding assistant. You describe a task in plain English, it uploads your project to a secure cloud sandbox, then iteratively generates, tests, and refines code until it meets your requirements. It excels at quickly prototyping ideas or handling multiple parallel tasks in isolation. This approach makes Codex particularly powerful for automated, iterative development workflows, perfect for agile experimentation or rapid feature implementation. Cursor is essentially a fully AI-powered IDE built on VS Code. It integrates deeply with your editor, providing intelligent code completions, inline refactoring, and automated debugging ("Bug Bot"). With real-time awareness of your codebase, Cursor feels like having a dedicated AI pair programmer embedded right into your workflow. Its agent mode can autonomously tackle multi-step coding tasks while you maintain direct oversight, enhancing productivity during everyday coding tasks. Each tool uniquely shapes development: Claude Code excels in autonomous long-form tasks, handling entire workflows end-to-end. Codex is outstanding in rapid, cloud-based iterations and parallel task execution. Cursor seamlessly blends AI support directly into your coding environment for instant productivity boosts. As AI continues to evolve, these tools offer a glimpse into a future where software development becomes less about writing code and more about articulating ideas clearly, managing workflows efficiently, and letting the AI handle the heavy lifting.

Software used to help us work. Now, it's starting to do the work and asking us what's next. For decades, you opened Slack to send messages or fired up Excel to crunch numbers. But now, AI agents can run tasks independently with no constant hand-holding required. The paradigm is flipping. Instead of typing in chat boxes, you're reviewing completed analyses. Instead of manually updating databases, you're approving batch changes. Instead of writing emails, you're orchestrating communication workflows. The interface is transforming from "help me do this" to "show me what's been done" and "let me direct what happens next." This isn't an incremental upgrade. It's an inversion that is already happening: companies are embedding autonomous AI agents into enterprise workflows. These agents aren’t just assist users with tasks; they manage entire business processes across systems. That's not an add-on. That's a rewrite of the software layer. Most incumbents are still focused on "adding AI" to existing workflows, missing that the workflows themselves are about to disappear. The tools you rely on every day will fundamentally change. They'll shift from helping you work to helping you oversee work that's happening without you. The companies building software to manage agents, not just assist humans, will define the next decade. We're not just talking about productivity. We're talking about rebuilding the software stack from the ground up.

Al and the future of software engineering   I was a skeptic of AI for years, but I’m now convinced that it’s going to transform many aspects of our lives. One area where its impact is already becoming clear is software development.   While I don’t believe AI will replace all software engineers, it will undoubtedly change how they work. We’re on the verge of seeing a new kind of software engineer—one who doesn’t write code line by line, but instead crafts prompts that guide AI to generate code, piece by piece. These engineers will stitch the components together, getting systems up and running far faster than before. I believe their productivity could be 10x that of the average pre-AI developer.   The role of the software engineer will shift toward high-level design, validation, and oversight—ensuring that the AI-generated code is correct, secure, and efficient. That alone is a significant responsibility, requiring robust processes and safeguards.   As this shift takes hold, companies will begin targeting this new skill set, and the hiring process will evolve. Interviews will no longer focus on writing quicksort or traversing a tree by hand. Instead, candidates may be asked to design and implement a high-performance, multi-threaded server with load balancing and caching that can retrieve and traverse millions of trees per second—using AI tools to build, test, and run it—all within a one-hour interview. The integration of AI into software engineering isn’t just a technological shift—it’s a fundamental redefinition of the role itself. As AI takes over routine coding tasks, the value of engineers will increasingly lie in their ability to design, guide, and validate complex systems. Those who adapt and learn to collaborate with AI will not only stay relevant but thrive in this new landscape. The future of software development belongs to those who can think at a higher level—and speak the language of AI.

Everyone’s talking about AI in software development. But the real shift isn’t copilots or code generation. It’s a fully agentic software development lifecycle, with humans guiding and approving at critical steps. When agents are embedded across the SDLC, three things fundamentally change: 1) Data - You’re no longer operating on partial context, blocked by availability, access or capacity. - Agents pull together product, design, engineering, customer, and business data into a shared, living understanding. - No access walls. No “can you share that doc or tool with me?” Just comprehensive context, available when it matters. 2) Dialog - Agentic systems create shared understanding faster. - Not just within product teams, but across: • Stakeholders • Customers • Delivery teams Fewer translation layers. Less re-explaining the “why.” Faster alignment because everyone is working from the same source of truth. 3) Decisions - Better context leads to better decisions. - And faster feedback loops mean those decisions don’t linger in theory. - Signals from users, delivery, and the business flow back immediately. - Teams adjust in days, not quarters. This is what modern product teams should be building toward: Not AI bolted onto tools… …but agents woven into how decisions, alignment, and execution actually happen. That’s where the real leverage is.