The Undercover
Agent Files
Guides, research, and field reports on AI agent testing, adversarial security, and conversational AI quality.
AI Accountability: The QA Challenge Ahead
As AI regulations tighten, companies must revamp their QA strategies to ensure compliance and build user trust. Here's how to get ahead.
AI Testing & QA
81AI Accountability: The QA Challenge Ahead
As AI regulations tighten, companies must revamp their QA strategies to ensure compliance and build user trust. Here's how to get ahead.
Read moreOvercoming AI Evaluation Hurdles: A Guide for Technical Leaders
Many leaders focus on AI capabilities, but real challenges lie in evaluation and integration. Explore actionable strategies for success.
Read moreRethinking Quality Assurance in Local AI Processing
As Nvidia brings AI to personal computers, organizations must adapt their QA strategies for localized processing to ensure reliability and trust.
Read moreExploring Apple's AI Innovations: Quality Assurance Impacts
Apple's upcoming AI advancements raise critical quality assurance challenges. Technical leaders must adapt their QA strategies to keep pace.
Read moreSecurity & Red Teaming
9Integrating Compliance and Security in the Era of GPT-4
Explore how GPT-4's new features impact compliance and security in AI development, and how to adapt your workflows accordingly.
Read moreWhy Ignoring Cybersecurity in AI Development Could Cost You Everything
Rising cybercrime demands that organizations integrate security into AI development, not as an afterthought but as a core component.
Read moreThe Checkmarx Breach Reveals AI Development's Security Blind Spot
When security companies can't secure their own AI pipelines, it exposes a critical gap: we're protecting production AI while leaving development infrastructure wide open.
Read moreWhen Your CI/CD Pipeline Becomes the Target
The Checkmarx attack exposes how development infrastructure has become business-critical infrastructure. Your CI/CD pipeline isn't just building code anymore.
Read moreCI/CD & DevOps
25AI in CI/CD: Balancing Speed and Quality Control
The rush to integrate AI in CI/CD pipelines is real, but are we sacrificing quality for speed? Here’s how to maintain standards amidst innovation.
Read moreThe AI Operations Debt Crisis: When Speed Kills Sustainability
Companies are accumulating AI operations debt faster than they realize. The rush to deploy is creating infrastructure complexity that traditional DevOps can't handle.
Read moreWhen Your CI/CD Pipeline Becomes More Complex Than Your Product
Most teams are unconsciously building distributed systems disguised as deployment pipelines. Here's how to recognize when your automation crossed the infrastructure threshold.
Read moreYour GitHub Actions Workflows Are Infrastructure Debt
Most teams treat GitHub Actions workflows as throwaway YAML, but they've evolved into mission-critical infrastructure code creating expensive technical debt.
Read moreIndustry & Insights
3The AI Code Quality Cliff: Why Enterprise Development Just Crossed a Dangerous Threshold
GitHub's latest Copilot Enterprise features signal a tipping point where AI generates more enterprise code than humans write, but QA infrastructure remains dangerously outdated.
Read moreWhy Your AI Budget Is About to Break (And It's Not Your Fault)
Microsoft's latest AI pricing changes expose the uncomfortable truth: enterprise AI costs don't scale like traditional software, and CFOs are demanding answers.
Read moreMystery Shopper Testing for Enterprise AI: Making the Business Case
How to quantify the ROI of adversarial AI testing and convince your leadership that proactive chatbot QA saves money.
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