The Agentic Testing & Traceability Handbook

Q: Does Agentic AI replace human judgment in software verification and safety-critical decisions?

No. Core agentic principles enforce a strict human-in-the-loop requirement. While AI agents automate manual research, draft test cases, and execute tools multi-step workflows, any output must be reviewed and approved by a qualified engineer or reviewer before becoming part of the official engineering baseline or certification record.

Q: How do AI agents integrate with existing ALM tools and testing frameworks?

Agentic AI connects directly to live engineering repositories and tools, such as requirements platforms, test runners, and static analysis utilities (e.g., LDRA or Visure), allowing agents to fetch context, trigger test runs, and update status records seamlessly without requiring engineers to copy and paste data across systems.

Learn how to use Agentic AI for Requirements, Software Verification, and Certification.

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What You'll Learn

Leveraging Agentic AI for Requirements, Verification, and Certification

Agentic AI refers to an AI system that doesn’t just respond to a single prompt with a single answer, but can take a goal, break it into steps, call tools to retrieve or change information, and carry a task through to completion, with a person checking the result at the right points.

Core principles that distinguish a genuinely agentic workflow from a simple AI chatbot:

It connects to live engineering data, not just the text typed into a chat window
It can take multiple steps toward a goal without a person re-prompting it at every stage
It can call tools (test runners, requirements platforms, version control) as part of completing a task
It still requires a human decision point before anything it produces becomes part of the official record

Full ROI within the first year

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FAQ

Agentic Testing & Traceability FAQs

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What is Agentic AI, and how does it differ from traditional AI chatbots in engineering?

Unlike basic AI chatbots that simply answer isolated prompts or generate static text, Agentic AI operates autonomously within live engineering environments. It can break high-level goals into execution steps, call external engineering tools (such as requirement managers, test execution engines, and version control systems), and manage complex workflows independently, all while keeping humans in the loop for final approval and compliance certification.

How does Agentic AI help with regulatory compliance and standards (e.g., DO-178C, ISO 26262, IEC 62304)?

Agentic AI accelerates compliance by automatically mapping end-to-end traceability between requirements, test cases, and code changes. It reduces manual verification overhead, ensures complete audit trails, and flags trace gaps or quality defects early in the lifecycle, drastically cutting time spent preparing for regulatory audits.

Does Agentic AI replace human judgment in software verification and safety-critical decisions?

How do AI agents integrate with existing ALM tools and testing frameworks?