Why embedded development is a natural next frontier for AI-assisted software development
Coding agents have quickly become a key part of the software development process, helping developers understand codebases, generate code, fix bugs, and ultimately move through dev cycles much quicker than before. While they are not perfect (developers must remain vigilant about managing quality, design tradeoffs, token cost, and lots more), the direction is very clear at this point.
So far, the majority of coding agents have focused on the common / popular types of software development (apps, websites, cloud services, scripts, etc). This makes perfect sense because most training data exists about those tasks, making them natural places for coding agents to demonstrate their value. But, as you probably know if you’re reading this post, software does not stop at the edge of the screen.
A huge amount of the physical world already runs on software. Sensors measure the air quality, while actuators control industrial production systems. Everything from medical devices and energy systems to drones and robots use software to manage how they interact with the real world.
This isn’t typically what people mean when they say “software development”. Using software to interact with the real world is different enough to merit its own label, “embedded development”, and it has always been more of a specialized domain due to the complexity that arises when software meets the physical world. It’s also one of the most interesting places for coding agents to go next.
Embedded is different
It might seem like embedded development should be an easy extension for existing coding agents. Embedded chips still run code after all, and if an agent can write great Python or JavaScript, why not just ask it to write some C?
As it happens, that sometimes works pretty well. Major semiconductor vendors have started making their documentation available to coding agents, and that has improved code quality, but embedded development is a lot more than code generation.
First, embedded software depends on the specific hardware that it runs on (the chip, the board, the peripherals). This all determines the memory and power available, and it also dictates which SDKs and tools are applicable. A small mismatch between the generated code and the actual target environment can be the difference between a working application and hours of debugging.
Embedded developers typically spend a lot of time moving between datasheets, SDK documentation, example projects, build tools, configuration files, serial logs, and forum posts. This is because there’s a whole separate world of embedded specifics that need to be understood on a per-chip basis, even within a single vendor ecosystem. So while embedded doesn’t require a completely new kind of coding agent, it does require extensions to the typical agent workflow. Competent embedded coding agents need to know about hardware, SDKs, toolchains, build and flash loops, runtime behavior, and validation on physical hardware.
Why now?
The timing is really interesting here because several trends are converging:
- Coding agents are becoming more capable at controlling development tooling and helping developers move through real workflows i.e., they are not just generating code anymore.
- Embedded hardware is continuing to mature in terms of power and connectivity capabilities, and is playing an increasing role in everything from industrial automation and environmental sensing to connected consumer products and robotics.
- Embedded hardware vendors are starting to make their documentation, SDKs, and dev tooling more accessible to AI agents.
This all adds up to make agent-driven embedded development a reality, but documentation-aware code generation is only the beginning. The real opportunity is to help agents move through more of the embedded development lifecycle, from generating code, all the way through QA, to deployment and continuous improvement. That is where the embedded space becomes really interesting (and really different from general-purpose programming) because it forces coding agents to move beyond the codebase and into the space where software has to be tested against the physical world.
From generated code to observed behavior
In general-purpose software development, a coding agent can make impressive progress by editing files and running tests. In embedded development, the feedback loop is far more complicated.
Once you’ve actually gotten the code to build with the right compiler, and flashed it onto a physical chip, then you can start to tell if it’s doing what you want or not. It might have to interact with sensors and actuators, or it might rely on wireless radios and cloud services. Debugging involves a combination of serial logging and stack traces while you try to understand timing issues, quirky power consumption patterns, and the impacts of the real-world deployment environment.
This is why runtime awareness really matters. An embedded agent becomes much more useful if it can reason about what an embedded device is doing at runtime. This might mean helping a developer understand why a sensor is reading sporadically, why a wireless connection can’t be maintained, or why the generated code compiles but doesn’t behave as expected when the software hits the real world via the target hardware.
This gets to the heart of the shift that is required for coding agents to be truly useful in the embedded context i.e., we need to move from code generation to helping drive the full embedded lifecycle. The goal is to give developers a collaborator that understands their hardware and embedded toolchain, and can be of genuine value at each step from idea to deployed physical system.
Beyond apps & websites
If coding agents are truly going to help program the physical world, then mastering the embedded development lifecycle is one of the most practical places to start developing the necessary capabilities. Embedded systems are already the software layer that powers smart home devices, industrial automation, robotics, and lots more, so it’s an obvious place for coding agents to expand into.
Robotics is a really interesting domain to consider. Besides the crucial model component required for intelligence, robots also need embedded software, sensors, actuators, motors, edge processing, cloud services, and real-time behavior. That is, a robot isn’t immediately useful when it gains access to intelligence, it still needs to take action in the world, and it needs to continually update its model of that world based on what it observes. That requires lots of inherently complex embedded software, and ideally that software would be developed with the latest AI-driven coding tools, not with code that’s been copy/pasted from a forum post.
That is why embedded development is so interesting right now with respect to the future of coding agents. It serves as the perfect bridge from general purpose software projects into systems that can operate in and on the physical world.
How we think about this at Temboo
At Temboo, we’ve spent years working at the intersection of developer tools, embedded systems, and cloud workflows. It’s that background that shapes our views on how AI agents can play a role in embedded development.
We don’t see embedded AI as a simple matter of pointing general purpose agents at vendor documentation and hoping for the best. Embedded developers need and deserve agents that understand their hardware, SDKs, toolchain, project structure, and, crucially, their real-world runtime behavior. They need an experience that guides them through the full embedded workflow, not just code snippet generation.
That is how we’re building the Temboo Agent, and while we’re still early on our journey, the direction is clear. Coding agents are at their most useful when they are grounded in information about the environment they operate in, and when they have the ability to play an expansive role in the development lifecycle. For embedded development that means everything from knowing the hardware specifics and vendor tooling, and being able to keep contributing after the code has been written.
The most exciting version of this future is not one in which agents are let loose to blindly control the physical world – far from it. It is one where developers get access to excellent tools for building, testing, and improving physical world software, safely and reliably. Embedded development is a natural next step for coding agents, and we’re excited to be playing our part in that future.
