Trace.Space Just Launched the First AI Agent for Systems Engineers.

Trace.Space is launching Space Agent: the first AI agent built from the ground up for systems engineers. It lives inside the platform, works directly with your data model, and handles core engineering tasks 10 to 100 times faster than doing them by hand.

We built Trace.Space because we became engineers for a reason. The spark of watching a complex idea take physical shape. But the daily reality of modern systems engineering looks nothing like that spark. Most of the day disappears into coordination: tracing requirements, searching through databases by keyword, formatting reports, chasing colleagues for context that should already be in the system.

Space Agent exists to give that time back. And the story of how we got here, including the stretches where we weren’t sure we would, is worth telling.

The real problem we set out to solve

We kept seeing the same thing in engineering organizations. Smart, experienced people spending their days on work that had nothing to do with why they became engineers. Manual traceability audits. Keyword searches through requirement databases with tens of thousands of items. Walking down the hall to ask a colleague whether a particular specification already existed somewhere in the system, or waiting two weeks for that colleague to come back from vacation.

Products have gotten more complex. A modern aerospace or automotive program can involve hundreds of thousands of specification items across dozens of subsystems. But the deeper issue isn’t the volume. It’s what the volume does to the people managing it. Systems engineers are spending more time writing about making things than actually making things.

The tools most teams rely on were built for a different scale. They do what they were designed to do. The world moved past them. Space Agent was built to close that gap: not another tool that asks engineers to change how they work, but an agent that operates inside the process they already have.

Three years of getting it wrong before getting it right

We didn’t start building AI for systems engineering when it became a trend. The team has been at this for three years. And for most of that time, the results weren’t close to good enough.

“People don’t realize what we went through to get here. Three years. And for most of that time, the AI wasn’t giving us anything useful for real engineering work. We kept building because we believed the problem was worth solving. But we had plenty of moments where the honest answer was: this isn’t good enough yet.” — Janis Vavere, CEO of Trace.Space

The early versions couldn’t follow instructions reliably enough. General-purpose AI applied to a requirements database produced general-purpose answers, and systems engineers need precision. An output that’s 80% right is, in engineering terms, wrong.

The skepticism wasn’t just external. Matt Maclaine, our forward-deployed systems engineer, had been vocal about his doubts from the start, and he had every reason to be. He’d watched enough tools over-promise. Even some of our own developers, were deep skeptics about whether AI could do meaningful engineering work. That changed only when they saw the results with their own eyes, not from any pitch or presentation.

What finally worked was a fundamental shift in approach. We stopped trying to make a general AI do engineering work and started building an agent that understands the engineering data model from the inside. Space Agent doesn’t sit outside the system and answer questions about it. It operates inside the traceability chain, the coverage model, the requirement structure, the domain context. That’s the difference between a chatbot bolted onto an engineering tool and an agent that actually belongs there.

“We are the only company in this market with an AI agent that actually works inside the engineering workflow. Not a chatbot bolted on. A working agent.” — Janis Vavere, CEO of Trace.Space

“Now I can do what I got my degree for”

The moment that changed the team’s energy happened on camera, by accident.

Matt was recording a product walkthrough. Coverage analysis, quality checks, requirements creation. Standard capabilities, one after the other. Then he asked the Space Agent for trace suggestions. The feature had been added just days earlier. He hadn’t tried it before.

“The agent came back with twenty suggestions at once, each with an accept button right there in the interface. People have to feel how good this is.” — Matthew Maclaine, Forward-Deployed Systems Engineer

Anyone who’s worked in requirements management knows why that reaction makes sense. Manually linking requirements to tests, to design elements, to other requirements across documents is one of the most tedious, time-consuming parts of the job. It takes hours. Having an agent suggest those links and letting you review and accept them in the interface changes what an afternoon looks like.

Matt had spent months as the team’s most outspoken AI skeptic. But when the Space Agent found related requirements with no formal traces and still surfaced the right connections, he couldn’t dismiss it. The agent understood the structure of what he was working on.

Amanda, Account Executive at Trace.Space, was watching the session. She asked one question that stuck with everyone in the room: “We couldn’t do that before?” The answer was no. Literally the day before, they couldn’t do what Matt had just done in thirty seconds.

“ I can do what I got my degree for. Actually make things, instead of writing about making things.” — Matthew Maclaine, Forward-Deployed Systems Engineer

What Space Agent actually does

Space Agent handles seven core tasks that systems engineers deal with daily. Each one used to be manual, slow, and prone to the kind of quiet errors that only surface months later during integration. Here’s what changes.

Coverage analysis

You ask Space Agent where the gaps are in a subsystem, and it maps coverage across your requirements in seconds. No more cross-referencing between documents and spreadsheets to figure out what’s been tested, what hasn’t, and what fell through the cracks.

Risk assessment

The agent identifies which requirements carry the most downstream risk based on dependency patterns, change frequency, and structural position in the trace chain. Problems surface before they cascade into redesign cycles.

Impact analysis

When a requirement changes, Space Agent traces the blast radius: every downstream artifact, test case, and sibling requirement affected by that change, visible before you commit to it.

Requirements and test creation

Space Agent drafts new items based on context from parent requirements, related specs, and domain patterns. Engineers review and refine a draft instead of starting from a blank page. Six months ago, this kind of contextual generation wasn’t possible inside an engineering workflow.

Well-formedness checking

Real-time quality checks on requirement language. Ambiguity, incompleteness, testability, conformance to INCOSE and ISO/IEC standards. You get actionable feedback on each item, not a pass/fail score.

Traceability analysis

Full monitoring across the traceability chain, from stakeholder needs through system requirements, design, implementation, and verification. Broken links and orphaned items get flagged automatically, instead of waiting to be discovered during a review milestone.

Trace suggestions

The feature that changed Matt’s mind. Space Agent recommends trace links that should exist based on semantic and structural analysis, then lets you accept them directly in the interface. No more manually linking requirements to tests across documents, one by one.

Every systems engineer gets a personal agent

The vision behind Space Agent is direct: every systems engineer gets their own agent. You chat with your full requirement set, in context of your data model, your coverage rules, your best practices, your actual product. You run analysis, check quality, find coverage gaps, generate requirements, all through a conversation.

The speed difference isn’t abstract. Coverage analysis that took a morning now takes seconds. Trace creation that required manual linking across documents now happens through a chat. Requirements that started from a blank page start from an intelligent draft built on your project’s own data.

Complexity is not a liability, it’s leverage. But only when you have a coordination layer that works at the speed and scale complexity demands. Space Agent is that layer. And it’s not here to replace engineers. Engineers are the ones who decide what to build, how to evaluate tradeoffs, where to push back on a requirement. Space Agent handles the coordination overhead so they can focus on those decisions.

This is just the beginning

Space Agent is live in Trace.Space today. It is a working product that systems engineers can use with their own data, in their own projects, right now.

The agent is growing fast, and everything we’re building sits on a foundation of three years spent learning what engineers actually need, not what looks good in a demo.

We started Trace.Space because we believed that when engineers are unblocked, they don’t just perform tasks faster. They rethink what’s achievable. Space Agent is the first real step toward that future.

If you want to see what it does with your requirements, book a walkthrough with our team.