The First AI Agent for Systems Engineering
Space Agent is not a chatbot bolted onto an engineering tool. It is an agent that operates inside your traceability chain, your coverage model, your requirement structure, your domain context. It handles core systems engineering tasks 10 to 100x faster than doing them by hand.
Every systems engineer gets a personal agent. Chat with your full requirement set in context of your data model, your coverage rules, your product. Run analysis, check quality, find gaps, generate requirements, all through a conversation.
What Space Agent Does
Coverage Analysis
Ask where the gaps are in a subsystem. Space Agent maps coverage across your requirements in seconds. No more cross-referencing between documents and spreadsheets to find what has been tested, what has not, and what fell through the cracks.
Risk Assessment
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
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. Drag it into document view and keep moving.
Well-Formedness Checking
Real-time quality checks on requirement language. Ambiguity, incompleteness, testability, conformance to INCOSE and ISO/IEC standards. Actionable feedback on each item, not a pass/fail score.
Traceability Analysis
Full monitoring across the traceability chain: stakeholder needs through system requirements, design, implementation, and verification. Broken links and orphaned items get flagged automatically instead of waiting for a review milestone.
Trace Suggestions
Recommends trace links that should exist based on semantic and structural analysis. Accept them directly in the interface. No more manually linking requirements to tests across documents, one by one. Twenty suggestions at once, each with an accept button right there in the chat.
How Space Agent Works
Space Agent works directly with your data model: your traces, your coverage rules, your project structure. When it references a requirement, you can see the artifact. When it suggests a trace, you can accept it in one click. When it finds a gap, it shows you exactly where in the chain the gap lives.
Beyond the chat interface, Space Agent is deeply embedded into Trace.Space itself. Trace suggestions, quality checks, and review assistance surface directly inside the workflows where engineers already work, not in a separate window they have to remember to open.
Space Agent also accelerates onboarding to new projects. Instead of spending months building context on an unfamiliar system, engineers can ask Space Agent what exists, how things connect, and where the risks are. It puts an entire project's history and structure into perspective in minutes.
Your Data, Your Models, Your Environment
Space Agent is a conversational interface inside the platform. You chat with it the same way you would talk to a senior colleague who has read every requirement in your system and remembers all of them.
No training on customer data. Transient inference only. Organization-specific embeddings are isolated by design, never shared across customers.
Frequently Asked Questions
Is Space Agent training on my data?
No. Customer data is never used to train or improve AI models. Inference is transient. Your data stays yours.
Can Space Agent connect to my organization's LLM?
Yes. BYOM (Bring Your Own Model) is fully supported. Connect your Anthropic, OpenAI, or proprietary LLM endpoints. You control which model runs behind your agent.
How do I know I can trust what it produces?
Two ways. First, human in the loop: every suggestion goes through engineering review before acceptance. Space Agent recommends, you decide. Second, Trace.Space provides native tooling (graph view, matrix view, saved filters) that lets you manually verify anything the agent produces. You can audit its work the same way you would audit a colleague's work.
Does it only work with cloud deployment?
No. Space Agent runs in all deployment modes, including fully air-gapped environments where no data leaves your network.
See Space Agent Work With Your Requirements
Ready to See What Modern Requirements Management Looks Like?
Bring your own data. We will show you what changes when your AI agent actually understands your system.