Workshop

Embedded AI: Leveraging AI Agents for Smarter Development

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02:29:35

EOC 2025

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Jacob Beningo

Jacob Beningo is an embedded software consultant, founder of Beningo Embedded Group, creator of the Embedded Software Academy, and co-founder of the Embedded Online Conference. He helps developers and teams maintain and modernize their embedded software skills and processes so they can deliver better firmware faster. His expertise spans automotive, defense, medical, and space industries. He’s even developed software systems that have propelled systems to the Moon and beyond. He has authored three books, including Embedded Software Design, sharing insights that guide engineers toward industry best practices and streamlined development approaches. Jacob also hosts the Embedded Frontier podcast, delving into cutting-edge trends and techniques that empower teams to continuously improve. Additionally, Jacob lectures, consults, and contributes articles (including for embeddedrelated.com, Design News, and embedded.com) on modern, real-time embedded software development. He holds two bachelor’s degrees from Central Michigan University in Electronics Engineering, and Physics and Mathematics. He also has a Master’s in Space Systems Engineering from the University of Michigan.

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IoT AI/ML Design Cycle Software Design

Workshop

Embedded AI: Leveraging AI Agents for Smarter Development

Jacob Beningo

02:29:35 EOC 2025

Jacob Beningo

02:29:35

Artificial intelligence (AI) is transforming how software is developed, tested, and deployed; yet most embedded engineers have barely scratched the surface of what AI can do for them. AI agents, in particular, have the potential to accelerate development workflows, automate repetitive tasks, and enhance productivity in embedded systems. But where do you start?

In this workshop, we’ll demystify AI agents and explore how embedded developers can integrate them into their daily workflow. We’ll begin with a one-hour lecture, covering key concepts such as AI agents, their architectures, and their role in embedded development. You’ll learn how to leverage AI tools for code generation, debugging, and optimization and see real-world examples of AI-powered automation in embedded projects.

The second hour will be hands-on, where participants will connect an AI agent to GitHub, explore practical AI tools, and experiment with automating everyday embedded development tasks. By the end of the session, you’ll have first-hand experience using AI in an embedded context and a clear roadmap to continue integrating AI into your workflow.

Key Takeaways:

What AI agents are and how they apply to embedded systems
How to connect an AI agent to GitHub for automation
Exploring AI-powered tools for embedded software development
Hands-on experience with AI-driven code generation and optimization
Strategies for integrating AI into your embedded development workflow

Join us for this practical and forward-looking workshop to explore how AI can enhance your embedded software development processes today!

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Please log in or create an account to test your knowledge and see the answers.

According to Jacob Beningo, what best describes an AI agent in the context of embedded development?

A A deterministic code generator that only outputs final code without planning or tool use.

B A goal-oriented autonomous system that can reason, plan, break tasks into steps, and use tools like web search, filesystems, and code generation.

C A simple single‑turn chatbot limited to answering one question at a time with no autonomy.

D A special microcontroller optimized only for running ML inference on the edge.

E An IDE plugin that only suggests code snippets but cannot operate or act autonomously.

What initial approach does Jacob recommend when deciding where to apply AI agents in your embedded development process?

A Start by mapping and analyzing your development workflows, identify consistent repeated tasks, and automate small, well‑defined steps first.

B Immediately give the agent an entire product design and let it run end‑to‑end to maximize speed.

C Only use agents at the very end of development (e.g., to generate release notes) since they are unreliable earlier.

D Avoid mapping workflows—agents will infer the correct process automatically if given access to the repo.

E Replace CI/CD entirely with an agent without human oversight to save time.

What important property of LLMs/agents did Jacob highlight that affects repeatability of lab/demo results?

A LLM outputs are non‑deterministic; the same prompt can produce different results, so you must design guardrails, templates, or examples to improve repeatability.

B LLM outputs are fully deterministic; the exact same prompt will always yield identical output across accounts.

C Variability doesn’t matter because agents will always self‑correct without human review.

D Once you run a prompt once, re-running it later guarantees identical Dockerfiles and folder structures.

E Determinism is only an issue with cloud models; local models never vary their outputs.

Which of the following did Jacob identify as particularly good, practical use cases for AI agents in embedded firmware engineering?

A Automating testing, code formatting/linting, code reviews, documentation, and CI/CD orchestration.

B Replacing engineers by having the agent produce large volumes of unchecked production C code without human review.

C Only using agents for customer‑service chatbots since they have no role in embedded development.

D Training agents to run low‑level real‑time control loops on production hardware instead of firmware.

E Using agents exclusively for purely cosmetic UI tasks unrelated to embedded concerns.

What is Jacob’s recommended stance on intellectual property and using cloud-based agents?

A Assume cloud-based agents could expose data; use enterprise plans that guarantee data protections or use local models when IP sensitivity is a concern.

B Consumer cloud APIs always protect your IP and never use your data for training, so no special precautions are needed.

C There’s no way to protect IP so agents must never be used in professional work.

D The safest approach is to publicly publish all code before using agents to avoid any IP risk.

E Local models are illegal or unusable for protecting IP, so they should be avoided.

When designing your own agents and workflows, what structure does Jacob advise you to follow?

A Create a structured list of required steps (a workflow), pick models and tools aligned to each step, validate outputs, and consider multiple cooperating agents where appropriate.

B Write one vague prompt and let a single agent handle the entire project end‑to‑end without decomposition.

C Always pick the largest, most expensive model for every task because model selection doesn't affect results.

D Avoid templates, examples, or explicit instructions—give the agent completely unstructured inputs to maximize creativity.

E Only build agents in languages other than Python because Python cannot support agents.

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Upvotes Newest Oldest

Trevor

Score: 0 | 8 months ago | 1 reply

Thank you Jacob, I found that very useful!

Jacob_BeningoSpeaker

Score: 0 | 8 months ago | no reply

I'm glad to hear it!

PaulClarke

Score: 0 | 9 months ago | 1 reply

Where is the Discord link?

Stephane.Boucher

Score: 0 | 9 months ago | no reply

The Discord link has been sent in the conference newsletters but expires every week. We'll send a new newsletter very soon with the new live link.

MilicaKostic

Score: 0 | 10 months ago | 1 reply

Will this also be available later on demand?

Stephane.Boucher

Score: 0 | 10 months ago | no reply

Yes! This workshop will be recorded and made available on demand a few hours after the conclusion of the presentation.

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