Uploaded May 2026 | Updated September 2026, 3 weeks ago
Join the next cohort and build something like what you see in this demo:
https://ai.science/products-services/llm-agents-bootcamp
This is a software requirements traceability agent designed to automate the mapping between requirements, source code, unit tests, and build verification artifacts. The system ingests structured requirement files along with application source files, then uses AI to identify which functions and tests implement specific requirements while flagging uncertain mappings for human review.
The demo shows both a graphical interface and command-line workflow, along with an automated configuration system that allows new applications to be onboarded quickly. The agent is intentionally designed to remain transparent and honest, leaving gaps blank instead of hallucinating missing information.
The long-term vision includes RAG-powered performance improvements, automatic retracing after requirement updates, and intelligent scaffolding for future applications and verification workflows.
Built with: Python + Claude API + cFS heritage codebase
Target users: NASA and commercial companies using cFS (Ex: Blue Origin, aerospace primes)
https://aieonit.com
ru@aieonit.com
linkedin.com/in/ru-perera
#AI #SoftwareEngineering #Aerospace #RequirementsTraceability #Automation #SystemsEngineering #ArtificialIntelligence #NASA #FlightSoftware #DeveloperTools #MachineLearning #Engineering #RAG
Join the next cohort and build something like what you see in this demo:
https://ai.science/products-services/llm-agents-bootcamp
This is a software requirements traceability agent designed to automate the mapping between requirements, source code, unit tests, and build verification artifacts. The system ingests structured requirement files along with application source files, then uses AI to identify which functions and tests implement specific requirements while flagging uncertain mappings for human review.
The demo shows both a graphical interface and command-line workflow, along with an automated configuration system that allows new applications to be onboarded quickly. The agent is intentionally designed to remain transparent and honest, leaving gaps blank instead of hallucinating missing information.
The long-term vision includes RAG-powered performance improvements, automatic retracing after requirement updates, and intelligent scaffolding for future applications and verification workflows.
Built with: Python + Claude API + cFS heritage codebase
Target users: NASA and commercial companies using cFS (Ex: Blue Origin, aerospace primes)
https://aieonit.com
ru@aieonit.com
linkedin.com/in/ru-perera
#AI #SoftwareEngineering #Aerospace #RequirementsTraceability #Automation #SystemsEngineering #ArtificialIntelligence #NASA #FlightSoftware #DeveloperTools #MachineLearning #Engineering #RAG
