Author: Alan Shimel

  • 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.

  • AI Native Dev: Shaping the Future of AI-First Software Development

    AI Native Dev: Shaping the Future of AI-First Software Development

    In an era where artificial intelligence is rapidly transforming the software development landscape, a new community has emerged to lead this evolution: AI Native Dev. The community is centered around the website, https://AINativeDev.io/, The site serves as a platform for a raft of different types of content and information.

    This platform is not just a repository of tools and insights though; it’s a vibrant hub for developers dedicated to reimagining software creation through an AI-first lens.

    I recently had a chance to sit down with my friend Patrick Debois, of DevOps fame who is a prominent member of this budding community. You can listen to my DevOps Chats podcast with Patrick for more color and details.

    What is AI Native Dev?

    AI Native Dev is a collaborative initiative that brings together developers, technologists and thought leaders to explore and define the principles of AI-native software development. Unlike traditional AI-assisted development, which integrates AI tools into existing workflows, AI Native Dev advocates for a foundational shift. It emphasizes building software with AI as a core component from the ground up, fundamentally altering how applications are conceived, designed, and maintained.

    The community is spearheaded by industry veterans, including Guy Podjarny, founder of Tessl and former CEO of Snyk, and Simon Maple, a seasoned developer advocate. Their combined expertise in security, development, and AI positions AI Native Dev as a leading voice in the AI-driven development movement. 

    Beyond GuyPo and Simon Maple, other members of the community include Patrick Debois, Olivier Pomel, Macey Baker and Amanda Brock. 

    Core Principles of AI Native Development

    At the heart of AI Native Dev are four guiding principles that encapsulate the ethos of AI-first software engineering: 

    1. Specification-Driven Development: Emphasizing clear, structured specifications that serve as the foundation for AI-generated code. This approach ensures that AI tools can produce accurate and contextually relevant codebases.
    2. Context-Aware Development: Leveraging AI’s ability to understand and integrate comprehensive project contexts, leading to more informed decision-making and cohesive software solutions.
    3. AI Agents as Developers: Transitioning AI from a supportive role to an active participant in software creation, where AI agents can autonomously develop and operate components of software systems.
    4. Human Oversight by Design: Ensuring that AI-driven workflows are designed with clear human oversight mechanisms, maintaining alignment with human intent and ethical standards.

    These principles are not static; AI Native Dev encourages community contributions to refine and evolve these guidelines, fostering a dynamic and inclusive development philosophy. 

    Engaging with the Community

    AI Native Dev offers multiple avenues for engagement, catering to a diverse range of interests and expertise:

    • Discord Community: A space for real-time discussions, knowledge sharing, and networking with like-minded developers passionate about AI-native development. 
    • AI Native Dev Podcast: Hosted by Guy Podjarny and Simon Maple, the podcast delves into the nuances of AI-native development, featuring conversations with industry leaders and innovators. 
    • AI Native DevCon: An annual virtual conference that showcases practical applications of AI in software development, offering insights into current trends and future directions.
    • AI Dev Tooling Landscape: A comprehensive, community-curated repository of AI tools and technologies spanning the entire software delivery lifecycle, from prototyping to deployment. 

    These platforms collectively serve as a nexus for learning, collaboration, and innovation, empowering developers to stay at the forefront of AI-native methodologies.

    The Vision Ahead

    AI Native Dev is more than a community; it’s a movement aimed at redefining the paradigms of software development in the age of AI. By fostering a culture of openness, collaboration and continuous learning, AI Native Dev aspires to equip developers with the tools, knowledge and mindset necessary to harness AI’s full potential.

    As AI continues to evolve, so too will the practices and principles of software development. AI Native Dev stands as a beacon for those ready to embrace this transformation, offering a roadmap for building software that is not only enhanced by AI but fundamentally rooted in it. 

    Join the AI Native Dev Community

    Embark on your journey into AI-native software development by exploring the resources and connecting with the community at AINativeDev.io. Whether you’re a seasoned developer or new to the field, AI Native Dev welcomes all who are eager to shape the future of software development through the lens of artificial intelligence.

  • GenAI in Software Development with Nathen Harvey

    GenAI in Software Development with Nathen Harvey

    Nathen Harvey, DORA Lead at Google Cloud, delves into how generative AI is rapidly reshaping the software development landscape, presenting both exciting opportunities and complex challenges.

    Harvey reflects on his own journey, from early web operations and his time at Chef to leading the DORA program after its acquisition by Google. DORA (DevOps Research and Assessment), now in its 10th year, continues to be a definitive source for understanding how technical teams improve performance and value delivery. The program’s annual reports, especially its latest focused on generative AI, offer research-backed insights that are widely used across the industry.

    The conversation dives deep into this year’s findings, starting with the rapid and widespread adoption of AI. Nearly 90% of organizations surveyed are prioritizing AI from a top-down perspective, while over 76% of practitioners report already using AI tools daily. This simultaneous push from leadership and uptake by developers indicates that AI isn’t a passing trend—it’s a permanent shift.

    But the data reveals nuance. While AI tools are helping improve areas like documentation, productivity and developer satisfaction, there’s also a measurable decrease in software delivery stability and throughput. Harvey suggests that this may be due to larger and more complex change sets generated by AI tools, which clash with workflows optimized for smaller, more iterative updates. In essence, we’re accelerating one part of the pipeline while other parts can’t yet keep up.

    Harvey emphasizes the importance of interpreting DORA findings in context—using them as hypotheses to test within each unique organization. He also raises critical questions about workforce development, particularly how AI might reduce opportunities for junior developers to gain real-world experience and grow into senior roles. The future, he notes, will require balance: leveraging AI as an amplifier while still investing in people and process.

    The takeaway? AI is transforming DevOps, but not without growing pains. To navigate this shift, organizations need to blend data-driven research with community collaboration, continuous experimentation, and a commitment to developing talent at every level.