How Quality Teams Are Using AI Agents to Scale Their Impact
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Agentic AI is quickly becoming a critical enabler for regulated teams that want to scale quality and compliance without slowing down development.
AI gives the Quality team superpowers: AI agents can surface issues early, flag gaps in documentation, and automate tedious manual work, while keeping a human in the loop. This frees up Quality for what they do best: focusing on mitigating the most critical risks in their product.
AI Purpose-Built for Quality
Quality teams tell us that they feel like they’re playing catch-up. Once development is done, they must review all the documents, double-check that every risk is traced to the right spec, and manually audit test coverage.
AI agents, in contrast, are embedded as the work is happening, so Quality can catch issues long before release time.
Agents make product quality visible, so Quality teams can act faster and more confidently.
Use Cases
Here are some examples of how we’ve seen real teams use AI to drive item-centric development practices that improve product quality:
- Template Generation & Optimization: AI analyzes existing document templates and recommends content structures to accelerate the generation of the DHF.
- Conflict & Impact Analysis: AI detects requirement conflicts, summarizes dependencies, and evaluates the downstream impact of changes.
- Test Case Verification: AI verifies that the test plan is comprehensive by validating the presence of all required integration-level tests. This validation process confirms that every test case and its subsequent execution align with the company’s QMS, strengthening the integrity of release documentation.
- Design Inputs: By converting "vibe coding" documentation (like a short brief about a new product) into design controls, this AI agent automatically decomposes Markdown files into distinct, traceable items in minutes.
- Requirement Redundancy: AI acts as an automated quality control tool for requirements documentation. One team used this agent to analyze a set of around 1,000 requirements imported from Word, and the agent successfully uncovered 100 findings (a 10% find rate), which provided concrete opportunities to improve the clarity and consistency of the documents.
- Cyber Risk Traceability: AI identifies missing links between vulnerabilities and mitigations to keep the team on top of cybersecurity risks in their product.
Let Engineers Code
AI doesn’t replace domain expertise. It keeps teams focused on the product quality issues that matter most in order to keep patients safe.
- Engineers spend more time coding and solving complex problems
- Quality leaders spend less time chasing documents
- Teams ship faster, with less risk
Item-centric development is a virtuous cycle: by shipping faster, you actually improve safety for patients, and by spending more time on product quality than document quality, you can ship faster.
Speed is safety. Quality teams that leverage agentic AI to streamline workflows find that their documentation reflects the work their teams have already done. By shifting quality processes left, teams can bring life-saving innovations to market faster while maintaining the highest standards of quality and compliance.
This is the fourth and final post in our series on moving from a document-centric approach to an item-centric approach. Check out the first three posts:
Lee Chickering is a Client Operations Manager at Ketryx and an expert in quality assurance and regulatory compliance, specializing in bridging quality management and customer success to drive operational excellence in the life sciences industry. With a diverse background spanning manufacturing, project management, and compliance at companies like Amgen, he has led the implementation of Quality Management Systems (QMS) aligned with ISO 13485, ISO 14971, and IEC 62304. Passionate about advancing quality in life sciences, he thrives on collaborating with organizations to enhance efficiency, compliance, and innovation.
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