Systems engineering has barely changed in nearly three decades. The discipline that coordinates requirements, design, verification, and compliance across multi-domain programs is still being run on tools and methods that were set in the late 1990s. Document-centric requirements management, manual trace link maintenance, periodic audits, and weekly status meetings remain the default in industries that have otherwise modernized almost every part of how they build.

Now a second gap is opening. AI is moving into adjacent functions faster than into systems engineering, and the discipline is in danger of falling further behind. C-level executives across aerospace, automotive, defense, and medical devices have issued the mandate: bring AI into engineering. The clarity of how to do it has not arrived with the mandate. Most teams know they need to act and have no working reference for what acting looks like inside a regulated, multi-domain program.

Agentic systems engineering is that reference. It is the practice of running engineering work, requirements analysis, traceability, verification, change impact, compliance, through AI agents that operate inside an engineering data model under human review. This article defines the term, separates it from neighboring ideas, and shows what it looks like in the regulated programs where it has the most consequence.

What Is Agentic Systems Engineering?

Agentic systems engineering is an engineering discipline in which AI agents autonomously execute verification, analysis, and coordination tasks under human oversight, working from the same requirements, designs, and tests that engineers already manage. Two characteristics separate it from neighboring ideas. First, agentic does not mean reactive automation; the agent decides how to reach a goal rather than running a predetermined script. Second, agentic does not mean unstructured AI assistance; the agent operates inside a structured engineering data model, not against a free-text prompt.

The vocabulary is unsettled and the public conversation has run ahead of the working answers. There is little applied research on agents inside systems engineering, few case studies, and no shared benchmark for what good looks like in a regulated program. What does exist is the executive mandate to adopt AI and an absence of practical guidance for how to do it inside a program that has to ship a certified product. Agentic systems engineering is the methodology that fills the gap. It applies the agent loop, goal, plan, act, verify, review, to the work systems engineers already do.

Core Concepts of Agentic Systems Engineering

Four concepts carry most of the weight in any working definition. Each one originates in the AI literature and maps cleanly to something a systems engineer already does.

Goal-Driven Autonomy and Planning

An agent decomposes a goal into sub-tasks and plans an order of execution rather than running a fixed script. In systems engineering, the analog is work breakdown across V-model phases: a stakeholder need becomes system requirements, which become subsystem requirements, which become verification activities. A coverage analysis agent given the goal “find gaps in the propulsion subsystem” decides on its own which traces to walk and which artifacts to read. This is what distinguishes agentic behavior from deterministic automation, which only executes what it has been told to execute.

Memory and System of Record

Agents retain context across interactions. In systems engineering, the equivalent of memory is the system of record, the authoritative source for requirements, designs, baselines, and test results. The agent’s working memory is the engineering data model itself. As Matt Maclaine, Forward Deployed Systems Engineer at Trace.Space, put it, “Systems engineering is the ability to pull together what is in order to assess what ought to be.” An agent grounded in the system of record can do the “what is” work continuously, freeing the engineer to focus on the “what ought to be.”

Multi-Agent Coordination

Multiple specialized agents collaborate on different aspects of a problem. Systems engineering programs already operate this way with human agents. Mechanical, electrical, software, controls, and test engineers each hold a partial view of the system. Mikus Krams described the configuration plainly: “Each person is a big badass expert in their thing and they have their source of truth, and they’re not wrong to have their source of truth.” The agentic version of this team includes AI agents that hold specialized roles, a requirements quality agent, a coverage agent, an impact analysis agent, working alongside the humans.

Human-in-the-Loop Oversight

Engineers review, approve, and override agent outputs. This maps directly to the PDR and CDR review gates and the approval workflows that already exist in regulated programs. The agent proposes; the engineer disposes. In aerospace, automotive, defense, and medical devices, this is not a stylistic choice. A human must own the engineering decision for the record to be auditable.

Agentic vs. Traditional Systems Engineering

The fundamentals do not change. The execution does.

What Changes and What Stays

The science a systems engineer applies, falsifiable analytical methods like trade studies, impact analyses, requirements decomposition, V-model verification, does not change because an agent enters the workflow. Stage gates, traceability obligations, and configuration baselines remain. Matt Maclaine put it this way: “The scientifically rooted analytical principles of what systems engineers do, those will not change. Everything else around it can. How you coalesce the information, how you study the information, how you present the information, all of that can change and improve how quickly we arrive at the output of that analysis.”

What does change is the role. The systems engineer shifts from administrator, the person hand-coalescing data across windows, to architect, the person directing the agents and making the judgment calls the agents cannot make. The bottleneck in traditional systems engineering is volume: a single change to a requirement can demand walking 20 drawings, 15 specifications, and a regulatory document before the engineer has even reached the work the change is supposed to enable. Agents close that pre-work gap. They are good at coalescing information and bad at the extrapolation a human does when the situation is unfamiliar, which is why human review remains the anchor. The transition is methodical, not instant.

Where Agentic Approaches Add the Most Value

The high-value tasks are the high-volume, high-stakes ones: requirements quality checks, trace link maintenance, coverage gap detection, and change impact analysis. These are the tasks where human attention does not scale but accuracy requirements are absolute. They are also the tasks systems engineers most often describe as the bottleneck before the real engineering can begin.

Agentic Systems Engineering in Regulated Programs

This is where the methodology has the most operational consequence. In a regulated program, every requirement carries a verification obligation, every change cascades through trace links, and every audit demands a clean record. These are continuous-monitoring problems, and they are what AI agents are now starting to handle inside the engineering data model.

Requirements Management and Traceability

Requirements management and traceability are the highest-volume, highest-stakes tasks in regulated systems engineering. Modern programs ship with hundreds of thousands of requirements; software-defined hardware alone has driven roughly a 100x growth in specification items. Agentic systems engineering applies the agent loop to that volume. An agent ingests requirements, checks them for EARS notation, ambiguity, completeness, and testability, generates trace links to design artifacts and verification activities, flags missing coverage between levels, and detects broken traces when a parent requirement changes. What a team takes weeks to audit manually, an agent monitors continuously.

This is the work Trace.Space’s Space Agent handles inside the platform. As Janis Vavere, CEO and co-founder of Trace.Space, framed it: “Every systems engineer gets their personal agent. You can chat with your whole requirement set in context of your data model, coverage, best practices, your product. You can do quality analysis, coverage gap analysis, requirements creation in a way that you never were able to do before.” The structure matters as much as the chat: the agent reasons over an explicit graph of requirements, traces, attributes, and configurations rather than a blob of text. Structure is what lets the agent answer “where are the coverage gaps in the propulsion subsystem” in seconds instead of returning a paragraph that sounds plausible.

Compliance and Change Detection

Standards like DO-178C, ISO 26262, IEC 62304, and ASPICE require evidence that requirements are traced, verified, and reviewed end to end. Change detection is what triggers the work. When a single requirement changes in a regulated program, the agent identifies every affected test case, every dependent design artifact, every downstream trace, and every compliance obligation, and surfaces the impact before a human commits the change. Audit readiness becomes a continuous state rather than a sprint before the review.

Getting Started with Agentic Systems Engineering

The honest read is that the field is early. The opportunity is to understand it now, while the methodology is being shaped, and to put the foundations in place before the next program cycle.

Maturity Levels: From Manual to Agentic

A practical way to locate a team is along four stages. Manual: requirements and traces are maintained by hand in documents and spreadsheets. AI-assisted: AI features sit on top of existing tools, helping with drafting and search but not operating on the data model. AI-native: the platform was built around AI, with structured data and embedded reasoning. Agentic: AI agents act on the engineering data autonomously, under human review, against goals rather than scripts. Most systems engineering teams today sit between Manual and AI-assisted. The jump to AI-native and agentic requires a usable system of record as the foundation, because the agent’s reasoning is only as good as the structure it can read.

Practical Considerations and Current Limitations

Two limitations matter most. The first is the data layer. Agents reason over the system of record; if requirements are spread across documents, spreadsheets, and disconnected legacy tools, the agent has nothing structured to reason against. The second is governance. Regulated hardware programs cannot accept opaque AI decisions, which means provider transparency, deployment control (including air-gapped options for defense programs), and human-in-the-loop review are non-negotiable. The systems engineer who understands the shift now is the one who architects the transition later.

Conclusion

Systems engineering has spent thirty years waiting for an upgrade, and the upgrade is finally specific to it. Agentic systems engineering is not generic AI applied to engineering; it is a methodology built around the requirements graph, the trace chain, the verification record, and the compliance obligation. The fundamentals of the discipline hold; the execution evolves. Engineers who understand the agentic concepts and their own domain are positioned to architect what comes next, with Trace.Space as the working example of what an agentic platform for requirements, traceability, and systems engineering looks like today.