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Jacob Beningo explains why agentic firmware development is already here and how embedded teams can move beyond ad hoc AI prompting. He covers skills, agents, RAG databases, and MCP servers, shows how NXP's new agentic AI tools put them into practice, and offers practical steps for building an agentic firmware workbench. Key Takeaways: • In a poll of 50 embedded developers at a conference, everyone used AI, but only two were building skills, agents, or RAG databases, and only one was using MCPs. • Ad hoc prompting is not agentic development. Agentic workflows combine skills, agents, RAG, and MCPs to get repeatable results. • Encode your company's coding style guide in a RAG database and give an agent access to it so code reviews and driver generation follow your standards. • If you use AI for the same task more than two or three times, build a skill for it. • Build an agent when you keep orchestrating the same skills and sub-agents, and use RAG for knowledge you keep pasting into prompts. • MCP servers give AI "hands" in the physical world, such as debug sessions, power supplies, and oscilloscope or logic analyzer data, so it can work iteratively in a loop. • Silicon vendors like NXP are releasing LLM-independent agentic toolkits with around 100 prebuilt skills, MCP connectors, AI-powered documentation, and prompt libraries. • As AI produces thousands of lines of code, validation and test harnesses around AI tooling become critical. • AI makes mistakes like human engineers do, and iterating on them is similar to agile. Used well, it can handle the boring infrastructure work so developers can focus on innovation. • Jacob expects embedded development to change dramatically in the next 6–12 months, so start learning these techniques now rather than falling behind.

Jacob Beningo concludes his embedded software modernization framework series by discussing the establishment of metrics scoreboards for embedded systems development. He explores various metrics categories including code quality, performance, and security, with a deep dive into cyclomatic complexity as a powerful but underutilized metric among embedded developers. Key Takeaways: • Metrics scoreboards provide visibility into quality, predictability, and improvement opportunities in embedded development • Things that get measured get managed - tracking metrics helps identify root causes of development problems • Avoid vanity metrics like commit frequency; focus on metrics aligned with project goals • Cyclomatic complexity measures independent paths through functions and should stay below 10 for maintainable code • Seven key metrics categories: code quality, reliability/safety, testing, performance, resource utilization, process/productivity, and security • AI tools can significantly simplify dashboard creation and metrics analysis automation • Code coverage is valuable but can be gamed - ensure tests actually validate functionality, not just execute code • Compiler warnings should be eliminated or documented; aim for zero warnings in production code • Metrics trends are more important than point-in-time values for identifying code quality decay • Modern tools like Segger SystemView and Percepio Tracealyzer enable real-time performance metrics collection

Jacob Beningo explores how artificial intelligence and machine learning can modernize embedded systems development, sharing practical examples and dispelling myths about AI's applicability to embedded development. He demonstrates how AI can accelerate development workflows, from requirements gathering to code generation, while emphasizing an iterative, agile approach rather than fully autonomous AI development. Key Takeaways: • AI can provide 2x performance improvements for embedded development teams through automation and assistance • Use AI iteratively in small problem spaces rather than attempting fully autonomous agentic workflows • Create specialized AI tools for specific embedded systems domain areas like requirements gathering and code reviews • AI can help reduce debugging time, which typically consumes 20-40% of development effort • Machine learning inference can run effectively on resource-constrained 16-bit microcontrollers • Teams ignoring AI adoption risk being left behind as the technology becomes mainstream in embedded development • Focus on identifying repetitive daily tasks that can benefit from AI automation • Modern microcontrollers with neural processing units enable sophisticated on-chip machine learning applications • AI works best as a productivity multiplier rather than a replacement for embedded developers • Start with low-hanging fruit like debugging assistance and code review automation

In this episode, Jacob explores simulation for embedded systems as the seventh step in modernizing embedded development. He discusses functional simulation, on-chip simulation, and software architecture simulation, demonstrating how proper abstraction enables developers to validate code before hardware is available. Jacob provides a practical demonstration of simulating a push button module with debouncing algorithms, showing how modern software architecture can dramatically improve embedded system development workflows. Key Takeaways: • Functional simulation allows embedded developers to validate code without physical hardware by abstracting low-level hardware dependencies • Modern embedded architecture should decouple application code from hardware using zero-cost or low-cost abstractions • Push button debouncing serves as an excellent example for learning simulation techniques that every embedded engineer can relate to • On-chip simulation tools like QEMU and Renode can simulate processor-level instructions but may be incomplete for microcontroller peripherals • State machine simulation enables visual validation of system behavior before code deployment • AI tools can now rapidly generate simulation code, making functional simulation more accessible than ever • Socket communication provides deterministic and fast data exchange for functional simulation environments • Start simple with side projects like weather stations or button modules to learn simulation techniques • Legacy systems can be gradually refactored to support simulation by decoupling high-risk areas from hardware dependencies • Simulation enables faster development cycles and reduces dependency on hardware availability during early development phases

In this episode, embedded systems expert Jacob Beningo explores test-driven development (TDD) as the fourth step to modernize embedded software development. He addresses common misconceptions that TDD doesn't work for embedded developers, demonstrates how proper software architecture enables effective testing, and explains the mental shift required to write tests before production code for higher quality, more reliable embedded systems. Key Takeaways: • Test-driven development requires writing tests first, then production code to make tests pass, which feels counterintuitive initially but improves over time • Properly architected embedded software with decoupled hardware layers makes TDD highly effective for application code • The mental adjustment to TDD takes several weeks but results in faster overall development and higher code quality • TDD provides immediate feedback when changes break existing functionality through regression testing • Multiple testing types are available: unit, integration, system, regression, performance, and user acceptance testing • System-level TDD can drive development from user requirements down to individual components • AI tools can accelerate TDD processes while maintaining rigorous testing standards • A simple push button module can require 98 tests and 4,000 checks for complete validation • 100% test coverage is a worthy goal but doesn't guarantee bug-free code • TDD initially feels slower but dramatically reduces debugging time and improves long-term development velocity

Embedded systems expert Jacob Beningo explores five major industry trends for 2026, focusing on how AI-assisted development, security requirements, and platform-based engineering are reshaping embedded software development. This comprehensive analysis covers emerging technologies and methodologies that microcontroller-based system developers need to understand to stay competitive in the evolving embedded systems landscape. Key Takeaways: • Use AI to create custom development tools rather than just copying code, potentially saving months of development time annually • Security is no longer optional due to regulations like CRA - threat modeling and secure boot processes are now requirements • Platform-based engineering is driving adoption of modern tools like CMake, VS Code, and Zephyr RTOS across silicon vendors • C++ continues gradual adoption in embedded systems, growing from 3% to 30% market share over 20 years while C remains dominant • Functional simulation techniques can reduce debugging time by 50%, saving significant development resources when combined with AI tools • Edge AI and tiny ML are poised for resurgence as microcontrollers gain more compute power and specialized processing units • DevOps adoption varies widely, with basic Git and compilation pipelines being minimum requirements for modern development • West manifest tools are becoming important for managing complex multi-repository platform projects • AI should enhance expertise rather than replace fundamental embedded systems knowledge and skills • Copy-paste culture threatens to erode deep embedded systems expertise needed for solving complex hardware-software integration issues

Visit our sponsor's website to learn more about their embedded security solutions at https://www.RunSafeSecurity.com/jacob  This episode explores the critical shift from reactive "patch and pray" security approaches to proactive embedded security strategies. Host Jacob discusses common vulnerabilities in embedded systems, real-world security threats from nation-state actors, and practical tools and processes developers can implement to secure their devices throughout the entire development lifecycle. Key Takeaways: • Memory exploits (buffer overflows, out-of-bounds reads/writes, use-after-free) are the most common embedded system vulnerabilities • Nation-state actors like Voltaifun are actively targeting critical infrastructure through embedded devices • Even simple connected devices like $20 coffee makers pose significant security risks through botnets and grid manipulation • Supply chain attacks have risen 700% in recent years, requiring secure programming and signed keys throughout manufacturing • Threat Model Security Analysis (TMSA) should be performed upfront to identify critical data and potential attack vectors • Hardware isolation using ARM TrustZone, multi-core processors, or memory protection units provides essential security layers • Software Bill of Materials (SBOM) helps track open source components and monitor for newly discovered vulnerabilities • Static and dynamic analysis tools should be integrated into CI/CD pipelines for continuous security monitoring • Security must be considered throughout the entire device lifecycle, from design to end-of-life decommissioning • Proactive security approaches using runtime protection tools are more effective than reactive patching strategies

In this episode of the Embedded Frontier podcast, host Jacob interviews Darwin from GitLab's field CTO office about the adoption and implementation of DevOps practices in embedded systems development. They explore the unique challenges embedded developers face when modernizing their workflows, including managing complex codebases with hundreds of millions of lines of code, compliance requirements, and the critical differences between software-only products and embedded systems where software is just one component of the final product. Key Takeaways: • Embedded systems require different DevOps approaches than pure software products since shipping software doesn't mean shipping the final product • Modern vehicles contain 650 million lines of code in 2025, up from 200 million just five years ago, creating new complexity management challenges • Three categories of embedded systems each need different DevOps strategies: digital disruptors, stable machines, and functional safety systems • Containerized builds and shared development environments eliminate "works on my machine" problems and create reproducible, auditable builds • Software supply chain security through Solza attestation provides traceability from source code to final artifacts • Compliance as code can automate many regulatory requirements like ISO 26262 and MISRA C++, reducing manual bottlenecks • AI integration at the platform level helps embedded developers onboard to DevOps without becoming DevOps experts • Continuous delivery (creating release-ready firmware) is more appropriate for embedded than continuous deployment to production • Automated testing and QA are crucial to prevent manual processes from becoming the limiting factor in development speed • Over-the-air updates in embedded systems require managed deployments with higher reliability than cloud container replacements

This podcast episode explores step three of modernizing embedded software development: adopting DevOps practices to solve the critical problem of late and over-budget project delivery in embedded systems. Host Jacob Beningo discusses the four core DevOps principles, presents a compelling case study of the Ariane 5 rocket failure that cost $500 million, and provides practical guidance for implementing CI/CD pipelines to improve software quality and delivery speed. Key Takeaways: • Only 35% of embedded development teams deliver projects on time, with most running 3-6 months late • DevOps focuses on incremental value delivery, improved collaboration, automation, and continuous improvement • The Ariane 5 rocket explosion ($500 million loss) could have been prevented with proper integration testing and CI/CD practices • Start DevOps implementation with automated builds using containers to create unified development environments • Enforce code quality and standards automatically within CI/CD pipelines using tools like Misra C/C++ • Implement regression testing to catch bugs early when they're easier and less costly to fix • Use metrics analysis to automatically identify tight coupling and potential bug locations in code • Artifact management ensures traceability and ability to deliver specific software versions to customers • Deployment automation should at minimum enable automatic hardware testing, even if not direct customer deployment • DevOps creates a value feedback loop between companies and customers through observability and telemetry

NXP's Brendan Slade discusses why major semiconductor companies are investing heavily in Zephyr RTOS, exploring how this open-source real-time operating system is transforming embedded development for IoT and edge computing applications. The conversation covers NXP's role as a founding platinum member of the Zephyr project, practical advice for developers evaluating RTOS options, and the collaborative ecosystem driving innovation in microcontroller software development. Key Takeaways: • Zephyr provides integrated middleware beyond just a kernel, unlike traditional RTOS options like FreeRTOS • NXP was a founding platinum member of Zephyr project to drive strategic direction for microcontroller enablement • Device tree configuration is the biggest learning curve hurdle, but once mastered, enables easy hardware portability • The Zephyr project has ~50 members including major OEMs from automotive, industrial, and consumer sectors • All code contributions require pull requests, tests, and peer review - no single company can force changes • Power management infrastructure is built into Zephyr drivers, enabling faster development of low-power applications • Training resources are available from multiple partners, and NXP offers Visual Studio Code extensions for easier development • The MCX N947 Freedom board (~$30) provides an excellent platform for exploring Zephyr capabilities • Future focus areas include AI enablement, cybersecurity compliance, and higher-level application frameworks • Companies should invest in device tree training first, then start with evaluation boards to assess portability benefits

Summary In this episode of the Embedded Frontier Podcast, Jacob Beningo dives into modernizing embedded software architecture—the second step in his seven-step framework for building faster, smarter firmware. He explores why most legacy architectures turn into “giant balls of mud,” how tightly coupling software to hardware holds teams back, and the four architectural dimensions you must consider: characteristics, principles, decisions, and structure. Jacob also shares controversial yet practical perspectives, including his philosophy that “there is no hardware, only data,” and why data—not hardware—should dictate design. From architectural decision records (ADRs) to adopting a top-down approach with the 4C model, this episode provides actionable insights for designing scalable, maintainable, and future-ready embedded systems. Takeaways Avoid spaghetti code: Emergent architectures rarely scale; intentional design upfront is critical.Define architecture characteristics: Select 3–7 key qualities (e.g., modularity, scalability, robustness) to guide decisions.Establish design principles: Adopt philosophies like “data dictates design” and “there is no hardware, only data.”Capture architectural decisions: Use ADRs to record trade-offs and prevent wasted rework.Think top-down: Use the 4C model (Context → Containers → Components → Classes) instead of bottom-up hardware-first design.Abstract hardware: Decouple hardware details from application logic for scalability and reusability.Prepare for change: Modern IoT and ML-driven systems demand flexible, forward-looking architectures. Keywords embedded systems, software architecture, modularity, scalability, ADR, design principles, data dictates design, hardware abstraction, IoT, DevOps, test-driven development, modern firmware, top-down design, containers, 4C model

In this episode of the Embedded Frontier podcast, Jacob Beningo and Kyle Dando from NXP discuss the modernization of embedded software and firmware, focusing on the MCU Xpresso SDK. They explore the significant changes made to the SDK, including the adoption of CMake and KConfig, the introduction of West for managing software repositories, and the overall impact on developer workflows. The conversation also covers the migration path for existing users and future trends in embedded software development, emphasizing the importance of user feedback in shaping the SDK's evolution. Takeaways - Modern build systems are essential for embedded software development. - The MCU Xpresso SDK packages low-level drivers and middleware for ease of use. - Accessibility improvements were a primary focus in SDK modernization. - CMake was adopted for its universal build capabilities and customer demand. - West allows for efficient management of software from multiple repositories. - KConfig simplifies project configuration and dependency management. - Developers can choose between command line and IDE workflows. - Migration to the new SDK is designed to minimize disruption for users. - Future trends include support for new operating systems and ecosystems. - User feedback is vital for continuous improvement of the SDK.

In this episode of the Embedded Frontier Podcast, Jacob Beningo discusses the importance of modernizing build systems in embedded software development. He outlines a seven-step framework for developing faster and smarter firmware, focusing on the need to overhaul build systems to keep pace with contemporary development practices. The conversation covers containerization, the adoption of tools like CMake and West, and the integration of AI technologies to enhance build processes and ensure flexibility and scalability in software development. Takeaways - Overhauling your build system is crucial for modern development. - Build systems must adapt to contemporary software practices. - Containerization provides a consistent build environment. - CMake offers flexibility and modularity in builds. - West helps manage multiple repositories effectively. - Modern build systems should integrate with CI/CD workflows. - Flexibility in workflows enhances developer productivity. - AI can assist in transitioning to modern build systems. - Scalability is essential for long-term software projects. - A modern build system reduces stress and increases efficiency. Keywords embedded systems, build system, DevOps, CMake, containerization, software development, AI, firmware, modern architecture, CI/CD

Summary In this episode of the Embedded Frontier Podcast, Jacob Beningo introduces a comprehensive framework for modernizing embedded systems development. He discusses the importance of adapting to modern techniques in firmware development, emphasizing the need for a shift in workflows, architectures, and tools. The episode outlines a seven-step process that includes modernizing build systems, improving software architecture, implementing DevOps practices, embracing test-driven development, leveraging simulation, adopting AI and machine learning, and establishing a metrics scoreboard to track progress. Each step is designed to help teams develop faster and smarter firmware, ultimately leading to more efficient and effective embedded systems development. Takeaways Modernizing firmware is essential for efficient development.The seven-step modernization framework includes build systems, architecture, DevOps, testing, simulation, AI, and metrics.Modern build systems should support various developer tools and environments.Software architecture must be scalable and flexible, separating application code from hardware.DevOps is crucial for improving collaboration and speeding up development cycles.Testing should be prioritized, with an emphasis on test-driven development (TDD).Simulation can help developers test application code without hardware dependencies.AI and machine learning can enhance both on-chip processing and development workflows.Metrics are essential for tracking progress and identifying areas for improvement.The framework aims to help teams deliver better products on time and within budget. Keywords embedded systems, firmware development, modernization framework, build systems, DevOps, test-driven development, simulation, AI, machine learning, metrics

In this episode of the Embedded Frontier podcast, Jacob Beningo explores the evolving role of artificial intelligence (AI) in embedded systems development. He discusses the potential applications of AI, including code review, documentation, and generating code, while emphasizing the importance of adopting AI technologies to enhance productivity and efficiency in the field. Beningo also addresses the challenges and risks associated with AI, advocating for its integration into development workflows to stay competitive in the industry. Takeaways AI has a unique role to play in embedded systems development.Documentation is a critical area where AI can assist developers.AI can help automate code reviews, making them more efficient.Using AI for generating code can speed up the prototyping process.AI can help document architectural decisions and trade-offs.The rapid evolution of AI technology presents both opportunities and risks.AI can serve as an augmentation tool for developers, enhancing their capabilities.Understanding how to effectively prompt AI is crucial for getting desired results.AI can help reduce technical debt by improving documentation practices.The integration of AI into workflows can lead to significant productivity gains.