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 works across all deployment modes: cloud, VPC, on-premise, and fully air-gapped. Bring your own LLM keys (Anthropic, OpenAI, or your organization's proprietary models) or use models hosted by Trace.Space. Connect into frontier models or your own fine-tuned endpoints.
No training on customer data. Transient inference only. Organization-specific embeddings are isolated by design, never shared across customers.
Frequently Asked Questions
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.
What tasks can Space Agent do for systems engineers?
Space Agent handles core systems engineering tasks through conversation: coverage analysis, risk assessment, impact analysis, requirements and test creation, well-formedness checking, traceability analysis, and trace suggestions. Ask where the gaps are in a subsystem and it maps coverage in seconds; ask it to suggest trace links and it recommends twenty at once. It runs these tasks 10 to 100x faster than doing them by hand.
How is Space Agent different from a general-purpose AI chatbot?
Space Agent isn't a chatbot bolted onto an engineering tool; it operates inside your traceability chain, coverage model, and requirement structure. A general chatbot answers from outside your data, while Space Agent works directly with your project, so when it references a requirement you can open the artifact and when it suggests a trace you accept it in one click. It's embedded in the workflows where engineers already work, not a separate window to remember to open.
How does Space Agent suggest trace links, and can I accept them directly?
Space Agent recommends trace links that should exist based on semantic and structural analysis of your requirements. You accept them directly in the interface, often twenty suggestions at once, each with an accept button right there in the chat. That replaces manually linking requirements to tests across documents one by one.
How does Space Agent speed up onboarding to a new project?
Space Agent speeds up onboarding by putting an entire project's history and structure into perspective in minutes. Instead of spending months building context on an unfamiliar system, you can ask it what exists, how things connect, and where the risks are. New engineers get oriented through conversation rather than weeks of manual digging.
What does it mean that Space Agent works inside your data model?
Working inside your data model means Space Agent operates on your actual traces, coverage rules, and project structure, not a generic copy. When it finds a gap, it shows you exactly where in the chain the gap lives; when it suggests a trace, you accept it in one click. Its analysis reflects your product, so you can verify anything it produces with graph view, matrix view, and saved filters.
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.
See Space Agent Work With Your Requirements
Bring your own data. We will show you what changes when your AI agent actually understands your system.