Agentic AI changed software development for good. Here’s why hardware engineering and electronics follow, starting with requirements.
By Mihajlo Djordjevic
Most tools now come with an AI chatbot built in. You ask it something, it answers, and that’s where it stops. Nothing in your project moves. An AI chatbot talks about your work; it doesn’t take part in it.
In software engineering, that changed. AI reads the code, proposes changes, and carries them out for you to approve. That’s agentic AI, and it changes your role from doing every step to directing the work across the team.
That same shift is now reaching hardware engineering. It starts with requirements, the one place the whole team traces back to.
Why It Started With Software Engineering
AI reached software development first for a simple reason: the people building these AI models are software engineers. They built them for their own work first, because better coding tools also help them build their next models faster.
The Anthropic Economic Index (Feb 2025) shows coding extremely over-represented.
But being first in line isn’t the whole story. Two things made software genuinely ready.
The first is access. An AI agent doesn’t sit off to the side offering suggestions. It works inside the real project, changes the actual code, and picks up where it left off, so no one has to re-explain the work each time.
The second is verifiability. Write code, and you find out fast if it works: it either compiles and passes its tests, or it breaks and tells you where. That speed is what lets an AI agent be trusted to do the work rather than just propose it. As Karpathy notes, an AI can be trained to do a task well when its results can be checked quickly and cheaply.
That’s really what sets code apart: it’s text an AI agent can access, read, and inspect on its own, all in one place. Hardware has never had that. A flaw in code shows up in minutes; a flaw in an electronics design can take months to surface, when it’s far costlier to fix.
What Needs to Happen for AI to Be Useful for Hardware?
In software, an AI agent can check its own work, and that verifiability is what made it ready for this shift. But hardware operates differently. Two things still stand between an AI agent and real hardware work: context and access.
A requirement never stands alone; it ties to plenty of others, so changing one can put it in conflict with something agreed elsewhere. An AI chatbot won’t catch that, because it only sees what you paste in, not the rest of the project. So the problem stays hidden until a review or an audit, long after the decision was made.
Knowing what to change isn’t the same as changing it. An AI chatbot can tell you what to fix, but it can’t make it, so the change waits for someone to enter it by hand. Until then, the project status you see is behind the real one, leaving you to ask around to find out where things stand.
Give an AI agent both, and it catches the conflict while the change is still a proposal, before it turns into a costly problem. You spend less time redoing work, and nothing changes without your approval.
First Step: Start with AI for Requirements
Why Requirements Matter
Everything on a project traces back to the requirements: decisions, disciplines, constraints, and design intent all connect there. They’re captured in text, which is exactly what AI reads well. Get them right, and the whole team builds better and iterates faster.
Requirements are the context an AI agent needs to understand a change, check it, and act on it. Kept in a requirements tool, they give an AI agent the same head start it had with code: text it can read, all in one place. Requirements engineering is the area of hardware work that comes closest to what coding agents already do.
What This Looks Like in Practice
Say a requirement changes mid-cycle due to a customer request or a design constraint. That one change can also affect other requirements linked to it. A change like that no longer has to wait until integration or testing to be seen. An AI agent notices the moment it happens, checks what it affects, and shows you where the project stands. You see the problem early, rather than asking each engineer for a status update.
Most AI today only talks about your work; an AI agent needs context and access to act on it.
Software got AI agents first because code is easy to check; hardware hasn’t had that until now.
Requirements are the starting point: an AI agent surfaces conflicts early while you stay in control.
Get Started with Agentic Requirements Engineering
This is what Agentic Requirements Engineering brings to Altium’s Requirements Portal, included with every Altium Develop and Agile Teams subscription.
It’s not another AI chatbot off to the side. These AI agents (Engineering Assistant, AI-Assisted Importer, and AI Skills) monitor your project and surface coverage gaps, compliance risks, and quality issues as early signals.
Watch the video below to see a preview of the tool in action.
To learn more and test out the tool we developed, follow this link and get started with Agentic Requirements Engineering.
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