Tag: software engineering

  • Bringing Order to Chaotic Software Engineering Workflows

    Bringing Order to Chaotic Software Engineering Workflows

    Software development has never been tidy, but the current landscape feels more chaotic than ever. Shannon Mason, chief strategy officer for Tempo, dives into why software engineering workflows remain chaotic and what teams should be doing to try and restore application development order.

    Developers today face a relentless push to innovate while keeping complex codebases running securely and efficiently. AI has been billed as the great productivity booster, but early research paints a more nuanced picture. Cornell studies and MIT reports show that while AI tools can accelerate work for junior engineers or greenfield projects, they often slow down experienced developers working with mature, complex systems. The result? More debugging, more oversight, and sometimes more frustration than gains.

    Shannon draws a useful analogy: think of AI as “the world’s smartest intern.” It’s promising, but it still needs guardrails, guidance, and plenty of supervision. Left unchecked, AI-generated code can be verbose, insecure, or just plain wrong—adding to the workload rather than reducing it. Layer in the explosion of low-code and no-code tools, plus the rise of citizen developers, and the volume of software entering pipelines is set to grow dramatically. That democratization is exciting, but it also raises hard questions about scale, security, and quality.

    At the human level, the pace of change and the constant context-switching are pushing teams to their limits. Shannon highlights practical remedies: make work visible, avoid overloading teams, plan realistically (80% capacity, not 120%), and carve out focus time. Even simple steps like instituting a weekly no-meeting day can improve flow and reduce burnout.

    The takeaway is clear: AI won’t magically fix software development chaos, but with smart practices, cultural shifts, and realistic planning, teams can navigate the turbulence—and maybe even rediscover some joy in building great software.

  • We’re Not Being Replaced. We’re Inventing What Comes Next, Including Ourselves.

    We’re Not Being Replaced. We’re Inventing What Comes Next, Including Ourselves.

    After graduating with degrees in computer science and business in hand, I entered the software industry with the energy and optimism of someone ready to create great software. My first job wasn’t called “software developer”—I was hired as a systems engineer. And that title mattered, a lot more than I first realized.

    Right after being hired, I was sent through a formal systems engineering program and was immediately placed on a project that would become the foundation of a new division: Future Banking Systems. After two years of heads-down redesigning and building banking systems in Garden City, New York, I was shipped to Dallas, Texas, for an “exciting opportunity” in a new group just being formed.

    Turns out the new group began with me and one salesperson, starting from zero, to form the foundation for a group that would create banking systems for an industry experiencing massive disruption due to deregulation. Our current customers were disappearing because of acquisitions, which were rolled up to become financial institutions that were beyond the scale and capabilities of our current products and business model.

    Inventing Without A Net

    There was no approved business plan. No product definition documents. No product roadmap. No backlog. Just a vision document, leaders with foresight standing beside us, and the knowledge that the industry was headed somewhere new and our current path wouldn’t get us there. However much we thought it would cost, we instinctively knew it would probably cost 4x more. It was our responsibility to build systems for much larger financial institutions in an industry still very much in flux.

    We weren’t just creating next-gen banking applications. We were inventing systems for where the industry was going to be, not where it was. It was early, messy, and it was foundational. And it eventually led me into my first experiences with AI, developing in Lisp, Prolog, and expert systems while teaching database theory. Those formative experiences taught me something that’s stayed with me ever since: Creating software isn’t just about shipping code. It’s about inventing what comes next.

     

     

    That mindset—the instinct to look ahead, to build with intent—is what real engineering is about.

    Today’s AI Moment

    Fast forward several decades…We’re now in one of the most disruptive and transformative moments in software history. AI can write code, scaffold entire applications, generate test cases, and devise solutions to problems in seconds. Developers are using copilots, models, and mega-prompts to accelerate their work at a pace we’ve never seen. And agents are coming online, backed by models with reasoning capabilities, to automate development tasks and tackle harder problems.

    And with this combination of exuberant expectations and feverous innovation in AI, comes a familiar claim: “We won’t need developers anymore.”

    I couldn’t disagree more.

    Yes, AI can generate code. But just code isn’t the application, system, or product. Underlying every working application is a complex system. There are architectural decisions, engineering challenges, design trade-offs, failure modes, built-testing, security constraints, integration boundaries, long-term lifecycle concerns, and, oh yes, operability. Those don’t just come from better context and prompts.

    AI massively expands what’s possible. And software development is the landing zone for AI innovations. (See Tech Vendors See the Future – Software Development Is the Agentic AI-Proving Ground.) But someone still has to know what to build, why to make it, when to create what, and for whom, and how to keep it working when the world around it continuously changes. Today’s design decisions are tomorrow’s technical debt.

    What Software Engineering Really Means

    Leading software teams taught me that engineering is the art behind software. It’s where experience, discipline, creativity, and instinct converge.

    When a team is deep in shipping the release with heads down, checking in code, solving bugs, driving toward delivery, engineering leaders raise periscope. You have to scan for the problems that haven’t surfaced yet, the threats and opportunities forming at the edge of the horizon, and verify that our instrumentation matches course, depth, and speed. That’s engineering. It’s not just watching the project plan or tracking how many issues are in Jira. It’s reading the signals others might miss.

    I’ve learned to trust that instinct: the kind of bugs we’re finding, the questions we’re not asking, the problems we haven’t had to solve yet. These are clues. They tell me if we’re still on track, or if we are behind, even when the project status says otherwise. Engineers know to pay attention to how we’re progressing, not just what we’ve done and how fast.

    Engineering is more than shipping by a deadline. It’s knowing when the real blockers will hit and deciding when to face them. It’s applying instinct in tangible ways: adjusting the design, pulling forward a hard dependency, probing deeper when something hard is too easy. This isn’t about perfectionism. It’s about intent. It’s about leading a system into existence, not following a script or just building what’s asked.

    We Lead the Loop

    I’ll say it again: I take issue with the idea that AI will simply replace developers. We hear the phrase “human in the loop” tossed around like it’s some kind of failsafe. But that’s not what this is.

    Humans aren’t destined to just be in the loop, supervising AI. We lead the loop, and where the loop is going.

    We define the problem. We decide what great, good, and bad looks like. We understand when to trust, when to probe, when to double-check, and when to ask if we are asking the right questions. We raise the periscope. And that’s what this moment means more than ever.

    Skating to Where the Puck Will Be

    AI is where the puck is. But engineering? That’s how we skate to where the puck will be.

    And more than that, it’s how we set the puck on a new arc entirely. That’s what we’re here to do now: not just follow the capabilities of AI, but lead us into new innovations in how software is created, new architectures, new apps, and systems we wouldn’t have imagined. We are the force. AI is the force multiplier.

    That’s what excites me about this moment. We’re not automating ourselves out of relevance. We’re stepping into a higher role: engineers with expanded reach, clearer intent, and tools that extend our vision—not replace it. The AI IDE of the (near) future looks more like the game Starcraft, engaging through language, voice, and keyboard, versus today’s code editor and NLP interface hybrid.

    We’re Being Called Forward

    For those of us who’ve built real systems, who’ve led teams through impossible delivery schedules, who’ve watched the intangibles reveal deeper design flaws—we know what this era asks of us.

    It’s not about writing every line. It’s about knowing that what we are doing ultimately matters.

    It’s about being the one who sees the storm before it hits. The one who adjusts course, not because it’s easy, but because it’s necessary.

    AI will continue to evolve. And so will we. So will all the roles across the software development lifecycle. AI is raising the bar for engineering, but remember…

    We lead the loop.

     

    Mitch Ashley is VP and Practice Lead of Software Lifecycle Engineering at The Futurum Group. The voice of “AI across the SDLC”, Mitch is a serial-CTO, speaker, advisor, entrepreneur, and product creator. He leads analyst coverage of the Software Development Cycle (SDLC), with emphasis on AI-native and agent development, cloud-native, DevOps, platform engineering, and software security.

    See Mitch’s analyst research on the Futurum website.

    Subscribers can access Mitch’s Software Engineering Lifecycle practice
    decision-maker data, insight reports, and advisories through the Futurum Intelligence Platform.

  • Is Generative AI Replacing Junior Developers?

    Is Generative AI Replacing Junior Developers?

    Generative AI tools aren’t just autocomplete for code—they’re reshaping the very first rung on the engineering career ladder. Host Mike Vizard asks Juan Salinas, VP of business development at Jalasoft, and software engineer Rolando Lora whether entry‑level developer jobs are about to disappear.

    Salinas is blunt: the routine, low‑risk tickets that once taught newcomers the ropes are now exactly what teams hand to AI agents. University graduates were “20–30 percent job‑ready” before; today the same agents that help them finish coursework will compete for their first paychecks.

    Lora counters that the picture isn’t all doom. Lightweight “vibe‑coding” chats let designers and product owners spin up prototypes in hours, while senior engineers use agentic AI to explore multiple implementation paths and ship higher‑quality features. The catch, he says, is that newcomers must learn to review—not just generate—code, and master “context engineering,” the craft of feeding agents the right background so they solve problems in one pass.

    Both envision a mixed future: specialized agents will handle boilerplate and reviews, while humans focus on architecture, judgment and systems thinking. For that to work, academia must stop graduating “juniors” and instead turn out mid‑level developers who already know how to collaborate with AI responsibly. Hiring managers, Salinas adds, should map each task to a spectrum—augmentation, automation or full agency—and invest in upskilling rather than backfilling rote work.

    The take‑away: early‑career coding isn’t dead, but its fundamentals are changing fast. Anyone entering the field will need to treat AI as a teammate they trust—yet constantly verify. That means pairing curiosity with critical thinking and realizing that debugging a machine’s work may become the new entry ticket to the profession.

  • Perforce Commits to Building Agentic AI Fabric for DevOps Workflows

    Perforce Commits to Building Agentic AI Fabric for DevOps Workflows

    Perforce Software this week committed to building an agentic artificial intelligence (AI) framework that will be embedded across its entire portfolio of DevOps tools and platforms.

    Company CTO Anjali Arora said Perforce Intelligence will provide access to a set of AI agents that have been trained to automate a range of software engineering tasks that DevOps engineers will be able to orchestrate.

    Additionally, Perforce is committing to using the same fabric to integrate its AI agents with other AI agents being developed by, for example, cloud service providers, she added.

    Perforce, for example, has already added several AI agents to platforms such as Gliffy, a diagramming platform for Confluence, a Wiki-based collaboration platform for sharing documents provided by Atlassian.

    Next month, AI agents will also be added to the Puppet Enterprise Advanced platform to provide access to Puppet Infra Assistant, a natural language chat interface that allows DevOps teams to quickly understand what is happening across the IT environments that have adopted the IT automation that Perforce acquired in 2022.

    Via that interface, it becomes possible to know how something is working, what versions of modules or server operating systems are being used and whether something is in or out of compliance without needing to know the Puppet programming language.

    Ultimately, the AI agents being developed by Perforce will make it simpler for DevOps teams to create and maintain workflows across a portfolio of DevOps tools and platforms that Perforce has acquired over the past few years, including BlazeMeter, an application testing platform, Delphix, a provider of data masking platform, and, most recently, Snowtrack, a provider of version control software used by application designers.

    The overall goal is to eliminate the need for scripts, frameworks, or other legacy approaches to integration in favor of AI agents that share access to an orchestration framework. Those agents will be able to proactively adapt as changes are made across the entire software development lifecycle (SDLC), said Arora.

    DevOps teams that have had early access to these AI agents have seen 50% efficiency gains while increasing test coverage by 20%, according to Perforce. In time, it’s apparent DevOps teams will be able to more effectively manage a much broader portfolio of applications, noted Arora. The challenge will be adapting existing DevOps cultures to include AI agents that software engineers are assigning tasks to complete, with the help of other AI agents and policy controls to provide governance functions and validations of output, she added.

    It’s not clear to what degree DevOps teams have adopted AI, but research from the Futurum Group finds 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code. A total of 39% also plan to make use of AI models based on machine learning algorithms.

    Regardless of what type of AI technologies are adopted, hopefully, DevOps workflows and pipelines will soon be reconstructed in a way that is significantly less fragile as more scripts and connectors are eventually replaced by AI agents.

  • Windsurf Launches SWE-1: AI Models Built for the Entire Software Engineering Process

    Windsurf Launches SWE-1: AI Models Built for the Entire Software Engineering Process

    Windsurf has unveiled its first family of specialized models designed to transform developers’ work in a significant development for AI-assisted software engineering. The SWE-1 family, announced on May 15, 2025, represents a fundamental shift in AI assistance for developers, moving beyond mere code generation to encompass the entire software engineering workflow.

    Beyond Just Writing Code

    While recent years have seen remarkable improvements in AI coding capabilities, Windsurf recognized a critical limitation in current approaches: Software development involves much more than just writing code.

    “Why build SWE-1? Simply put, our goal is to accelerate software development by 99%. Writing code is only a fraction of what you do. A ‘coding-capable’ model won’t cut it,” explains the Windsurf team in their announcement.

    The SWE-1 family includes three distinct models:

    • SWE-1: The flagship model, comparable to Claude 3.5 Sonnet in tool-call reasoning capabilities, while being more cost-efficient to run. It’s temporarily available to paid users at zero credits per prompt.
    • SWE-1-lite: A mid-sized version that replaces Windsurf’s previous Cascade Base model, offering all users improved quality and unlimited use.
    • SWE-1-mini: A small, ultra-fast model powering Windsurf Tab’s passive experience for all users.

    Flow Awareness: The Key Innovation

    What sets Windsurf’s approach apart is its concept of “flow awareness”—the ability for AI systems to understand and operate within the complete, shared timeline of development work. This insight came from the company’s popular Windsurf Editor, which enables seamless collaboration between humans and AI.

    This flow awareness allows the models to understand incomplete work states and switch naturally between AI and human contributions. If a model makes an error, the human can jump in to correct it, and the model can then continue working based on those corrections, creating a truly collaborative workflow.

    “It will be a while before any SWE model can truly do everything independently,” acknowledges Windsurf. “Flow awareness enables the right form of interaction during this intermediate period.”

    Impressive Benchmark Performance

    According to Windsurf’s evaluation data, SWE-1 performs comparably to frontier models from major AI labs and significantly outperforms mid-sized and open-weight alternatives. The company uses two primary benchmarks:

    1. Conversational SWE Task Benchmark: Testing how well a model can address the following user query in the middle of an existing session with a half-finished task.
    2. End-to-End SWE Task Benchmark: Evaluating a model’s ability to solve a problem independently of beginning to end.

    In production experiments with real users, SWE-1 demonstrated strong performance in metrics like “Daily Lines Contributed per User” and “Cascade Contribution Rate,” reflecting both the quality of its suggestions and users’ willingness to adopt them.

    A DevOps Perspective

    These developments hold particular promise for DevOps professionals. The SWE-1 models’ ability to work across multiple surfaces—including the terminal, text editor, and browser—aligns perfectly with the integrated nature of modern DevOps workflows.

    The models can:

    • Incorporate terminal outputs and understand errors
    • Seamlessly transition between text editing and debugging
    • Maintain awareness of terminal commands and IDE actions
    • Process user feedback and testing results

    These capabilities could significantly streamline the often complex handoffs between development and operations phases that DevOps teams manage daily.

    “Windsurf’s SWE-1 announcement is a clear indicator that the future of software development is rapidly becoming AI-driven, extending far beyond simple code generation,” said Mitch Ashley, VP and practice lead, DevOps and application development at Futurum. “I applauded Windsurf’s ambition to address the entire development process with AI, integrating human-AI interaction at a fundamental level. This raises the bar for all vendors in the space, pushing them to deliver more holistic, contextually aware, and truly agentic capabilities to developers.”

    Building a Software Engineering Flywheel

    Windsurf’s approach represents a promising flywheel effect: As users interact with their tools, the company gains valuable insights into where models need improvement, enabling it to enhance model capabilities continuously.

    “We always know, at scale, exactly what our users want us to improve with our models next,” notes the Windsurf team. “That’s how we’ve rapidly built our model to the level it has achieved in today’s SWE-1 state.”

    What’s Next for Windsurf

    While the company is proud of its initial results, it emphasizes that SWE-1 is just the beginning. Windsurf plans to invest significantly further in its model development, with the ambitious goal of not just matching but exceeding the performance of frontier models from major research labs within the software engineering domain.

    For the growing number of DevOps teams integrating AI tools into their workflows, Windsurf’s focus on the complete engineering process rather than just coding tasks represents a promising evolution that could help bridge the traditional gaps between development and operations.

    As software teams continue exploring how AI can enhance their productivity without sacrificing quality or maintainability, Windsurf’s “flow-aware” approach offers an intriguing model for human-AI collaboration that respects the complex, iterative nature of modern software development.

  • Agentic Process Automation This Way Comes to Software Development

    Agentic Process Automation This Way Comes to Software Development

    The shift from robotic process automation (RPA) to agentic process automation (APA), prominently on display at Automation Anywhere’s Imagine conference in Orlando this week, promises significant implications for the software development community—particularly for low-code/no-code and citizen developers. Automation Anywhere, a provider of automation platforms and framework, articulated how APA, powered by agentic AI, will redefine the development landscape by providing unprecedented flexibility, intelligence and ease-of-use in application development. More than that, they are going all in on APA.

    Historically, RPA has been widely employed to automate a range of routine and repetitive tasks. However, its limitations were around RPA requiring highly structured environments and precisely defined logic paths. Small deviations or variations in data inputs often meant manual intervention, limiting its scalability and efficiency in dynamic business processes.

    APA, with its integration of advanced AI capabilities, overcomes these limitations. It offers a significant advancement—automation agents capable of understanding context, learning from experiences and adapting workflows dynamically. This intelligence liberates automation from rigid script-bound processes, providing immense potential to enhance productivity, especially in software development environments dominated by low-code/no-code tools.

    One of APA’s most transformative impacts will be felt by citizen developers—individuals with limited or no formal coding training who build applications primarily through visual interfaces and drag-and-drop components. Historically, citizen developers faced significant hurdles as their applications became more complex, often necessitating the involvement of professional developers to handle the logic-intensive, conditional workflows beyond their skill sets.

    With APA, citizen developers have access to a new class of tools—intelligent agents capable of automatically handling complex backend logic and decision-making processes. These agents don’t just automate routine tasks; they actively assist in managing more sophisticated operations typically reserved for professional developers. As a result, citizen developers can confidently create more robust, intelligent, and dynamic applications without becoming overwhelmed by complexity.

    APA’s impact on low-code/no-code platforms will also be profound. These platforms have democratized application development, empowering non-technical users to address their unique business challenges rapidly. Integrating agentic automation into these platforms amplifies their capability significantly. APA agents can proactively identify patterns, suggest optimal workflows, troubleshoot issues autonomously, and dynamically adapt processes based on evolving data inputs.

    Consider a typical scenario where a citizen developer creates a customer-facing app to streamline order processing. Traditionally, even low-code platforms require significant configuration to handle variations in customer behavior or unexpected data formats. APA agents, by contrast, autonomously manage these exceptions in real-time, learning from every interaction and continuously refining the process. This means less manual tweaking, fewer errors, and dramatically improved operational efficiency.

    Moreover, the Imagine conference emphasized the importance of orchestration—a crucial consideration in the APA era. Organizations increasingly utilize diverse automation tools and multiple intelligent agents from different vendors. Effective orchestration ensures these various agents collaborate seamlessly, complementing rather than competing against each other. For citizen developers, simplified orchestration interfaces integrated directly within low-code/no-code platforms will be a game changer, dramatically reducing complexity and improving user experience.

    This orchestrated approach mirrors strategic insights from industry giants like Salesforce and ServiceNow, who recognize orchestration as key to leveraging multi-vendor, multi-agent environments successfully. Automation Anywhere’s commitment to robust orchestration tools positions them well to lead this transformative shift.

    Yet, as with any technological shift, the transition from RPA to APA must be handled strategically. Automation Anywhere’s 20 years of industry experience provides a significant advantage, enabling the company to anticipate potential pitfalls and smoothly manage the transition. Their established market credibility and deep industry relationships offer solid assurance to businesses hesitant about adopting radically new technologies.

    From a broader AppDev perspective, APA will significantly influence software quality, delivery speed, and development agility. Intelligent automation agents support developers by rapidly handling mundane, repetitive tasks, freeing them to focus on more innovative, strategic initiatives. Consequently, APA facilitates faster innovation cycles, improved customer satisfaction and enhanced business agility.

    As businesses adopt APA-driven low-code/no-code solutions, they’ll likely witness an acceleration in their digital transformation journeys. Citizen developers, empowered with intelligent APA agents, will increasingly contribute to critical business solutions without relying heavily on IT departments. This not only democratizes software development but also enables organizations to respond more rapidly and effectively to market demands.

    In conclusion, Automation Anywhere’s strategic pivot from RPA to APA doesn’t merely represent an incremental upgrade—it heralds a new era of intelligent, flexible and powerful automation that is poised to revolutionize the AppDev landscape, empowering citizen developers and accelerating innovation.