# UndercoverAgent.ai

> Agent testing and evaluation platform. Red-team your AI agents before deploying to production.

UndercoverAgent is built by David Hurley (https://dbhurley.com), founder of Plasmate Labs and creator of the Adaptive Convergence Protocol (ACP) framework.

## About

UndercoverAgent is an automated testing platform that acts as a "secret shopper" for AI chatbots and agents. It simulates real customer interactions through multi-turn conversations, tests for security vulnerabilities, compliance violations, quality issues, and hallucinations, then delivers detailed reports with actionable recommendations.

## Key Capabilities

- Multi-turn conversational testing against deployed AI agents
- 11+ analysis passes: security, compliance, quality, adversarial safety, hallucination detection, escalation risk, and more
- Connectors for REST APIs, web chat widgets, and Slack bots
- Scheduled continuous monitoring with regression detection
- CI/CD integration via API with test quotas and billing
- YAML/JSON scenario library with 20+ pre-built test suites

## API

- Base URL: https://undercoveragent.ai/api/v1
- Authentication: Bearer token (API key)
- POST /api/v1/tests/run — Trigger a test run
- GET /api/v1/tests/{id} — Get test result

## Plans

- Free: $0/mo, 10 tests
- Operative: $29/mo, 100 tests
- Handler: $99/mo, 1,000 tests
- Director: $299/mo, 10,000 tests

## Blog Categories

- AI Testing & QA (52 articles)
- Security & Red Teaming (9 articles)
- CI/CD & DevOps (24 articles)
- Industry & Insights (3 articles)

## Recent Articles

- [The Legal Imperative for AI Quality Assurance](https://undercoveragent.ai/blog/legal-imperative-ai-quality-assurance) (2026-05-14) — As legal accountability for AI rises, integrating quality assurance into development is crucial for compliance and risk mitigation.
- [The Ethical Implications of AI Surveillance Today](https://undercoveragent.ai/blog/ethical-implications-ai-surveillance) (2026-05-13) — As AI surveillance technologies rise, we must address the ethical standards and privacy concerns surrounding their use. Here's what you need to know.
- [Why Your AI Development Pipeline Needs a Security Overhaul Now](https://undercoveragent.ai/blog/ai-security-overhaul) (2026-05-12) — Recent AI security breaches reveal the urgent need for robust security measures in AI development. Here's how to integrate security into your workflow.
- [Are Your AI Chatbots Misleading Users? The Misinformation Crisis](https://undercoveragent.ai/blog/ai-chatbots-misinformation-crisis) (2026-05-11) — As AI chatbots evolve, so does their potential to generate misinformation. Here’s what you need to know to mitigate these risks effectively.
- [Pragmatic Approaches to AI Compliance: What You Need to Know](https://undercoveragent.ai/blog/pragmatic-approaches-ai-compliance) (2026-05-11) — Explore essential strategies for integrating compliance into AI development workflows, addressing the urgent regulatory landscape for technical teams.
- [Integrating Compliance and Security in the Era of GPT-4](https://undercoveragent.ai/blog/compliance-security-gpt4) (2026-05-10) — Explore how GPT-4's new features impact compliance and security in AI development, and how to adapt your workflows accordingly.
- [Evolving QA Standards in the ChatGPT-4.5 Era](https://undercoveragent.ai/blog/evolving-qa-standards-chatgpt-4-5) (2026-05-09) — ChatGPT-4.5's advancements demand a reevaluation of QA practices to ensure reliability and safety in AI interactions.
- [Protect Your AI Projects from Legal Risks Today](https://undercoveragent.ai/blog/protect-ai-projects-legal-risks) (2026-05-08) — Learn how to integrate compliance frameworks into your AI development to mitigate legal risks and enhance credibility.
- [Proactive Quality Assurance in the Era of AI Accountability](https://undercoveragent.ai/blog/proactive-quality-assurance-ai-accountability) (2026-05-07) — As legal scrutiny increases, proactive quality assurance in AI development is essential to ensure accountability and transparency. Here’s how to adapt.
- [Why Ignoring Cybersecurity in AI Development Could Cost You Everything](https://undercoveragent.ai/blog/cybersecurity-ai-development) (2026-05-06) — Rising cybercrime demands that organizations integrate security into AI development, not as an afterthought but as a core component.
- [Gemini's Breakthroughs Demand New QA Standards](https://undercoveragent.ai/blog/gemini-breakthroughs-new-qa-standards) (2026-05-06) — Gemini's advancements in conversational AI highlight the urgent need for robust quality assurance practices to keep pace with evolving technology.
- [AI Privacy Compliance Is No Longer Optional: Insights from ACAPS](https://undercoveragent.ai/blog/ai-privacy-compliance-acaps-insights) (2026-05-05) — Growing public skepticism towards AI highlights the urgent need for compliance with privacy regulations to build trust and avoid backlash.
- [The Checkmarx Breach Reveals AI Development's Security Blind Spot](https://undercoveragent.ai/blog/checkmarx-breach-ai-development-security-blind-spot) (2026-05-04) — 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.
- [When Your CI/CD Pipeline Becomes the Target](https://undercoveragent.ai/blog/when-your-cicd-pipeline-becomes-the-target) (2026-05-03) — The Checkmarx attack exposes how development infrastructure has become business-critical infrastructure. Your CI/CD pipeline isn't just building code anymore.
- [Your GitHub Repo Is the New Crown Jewel: Lessons from Checkmarx](https://undercoveragent.ai/blog/github-repo-crown-jewel-checkmarx) (2026-05-02) — The Checkmarx breach exposes a hard truth: your GitHub repositories have become more valuable than your production systems.
- [The Checkmarx Breach: How AI Learns From Poisoned Code](https://undercoveragent.ai/blog/checkmarx-breach-ai-poisoned-code) (2026-05-01) — The Checkmarx GitHub breach reveals a hidden risk: AI development tools trained on compromised repositories are learning from poisoned data.
- [88% of Enterprises Had AI Agent Security Incidents Last Year. Here's What's Actually Breaking.](https://undercoveragent.ai/blog/enterprise-ai-agent-security-incidents) (2026-05-01) — A breakdown of the 2026 enterprise AI agent security landscape: what's going wrong, which attack vectors are hitting hardest, and how automated testing is the only practical defense.
- [While Retail Masters Digital Quality, AI Companies Fumble QA](https://undercoveragent.ai/blog/retail-masters-digital-quality-ai-fumbles) (2026-05-01) — The mystery shopping industry is rapidly adapting to evaluate digital experiences, while AI companies struggle with quality assurance basics that retail solved decades ago.
- [The AI Operations Debt Crisis: When Speed Kills Sustainability](https://undercoveragent.ai/blog/ai-operations-debt-crisis) (2026-04-30) — Companies are accumulating AI operations debt faster than they realize. The rush to deploy is creating infrastructure complexity that traditional DevOps can't handle.
- [When Your CI/CD Pipeline Becomes More Complex Than Your Product](https://undercoveragent.ai/blog/when-cicd-pipeline-becomes-more-complex-than-product) (2026-04-29) — Most teams are unconsciously building distributed systems disguised as deployment pipelines. Here's how to recognize when your automation crossed the infrastructure threshold.
- [Are AI Coding Assistants Creating Technical Debt Faster Than They Create Code?](https://undercoveragent.ai/blog/ai-coding-assistants-technical-debt) (2026-04-28) — GitHub's latest data shows 92% of developers use AI assistants, but debugging time is increasing. We're optimizing for velocity while creating system-level quality debt.
- [Your GitHub Actions Workflows Are Infrastructure Debt](https://undercoveragent.ai/blog/github-actions-workflows-infrastructure-debt) (2026-04-27) — Most teams treat GitHub Actions workflows as throwaway YAML, but they've evolved into mission-critical infrastructure code creating expensive technical debt.
- [The CI/CD Identity Crisis: When Your Pipeline Becomes Your Bottleneck](https://undercoveragent.ai/blog/cicd-identity-crisis-pipeline-bottleneck) (2026-04-26) — GitHub Actions adoption surged 40% this year, but AI-driven development is breaking traditional pipeline stages. The Ralph Loop reveals why linear CI/CD is becoming a velocity trap.
- [The AI Code Quality Cliff: Why Enterprise Development Just Crossed a Dangerous Threshold](https://undercoveragent.ai/blog/ai-code-quality-cliff-enterprise-threshold) (2026-04-25) — 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.
- [The Ralph Loop: Why AI Development Needs New Quality Control Patterns](https://undercoveragent.ai/blog/ralph-loop-ai-development-quality-control) (2026-04-25) — AI-accelerated development is breaking traditional code review. The Ralph Loop pattern emerging this week shows how teams are adapting quality gates for machine-generated complexity.
- [When Your CI/CD Pipeline Becomes Your Product](https://undercoveragent.ai/blog/when-cicd-pipeline-becomes-your-product) (2026-04-24) — Modern deployment pipelines are distributed applications with complex dependencies, but most teams test them like simple scripts. The operational risks are mounting.
- [The CI/CD Control Crisis: When GitHub Actions Becomes Your AI's Puppet](https://undercoveragent.ai/blog/cicd-control-crisis-github-actions-ai-puppet) (2026-04-23) — GitHub's AI-powered workflows are creating invisible attack vectors where AI decisions control your deployment pipeline. Traditional DevOps security can't protect you.
- [The AI Development Quality Cascade: When Productivity Hides Risk](https://undercoveragent.ai/blog/ai-development-quality-cascade) (2026-04-22) — GitHub's AI coding tools are creating a hidden quality crisis. While productivity soars, new categories of systemic bugs are embedding themselves deeper into enterprise codebases.
- [GitHub's API Crisis Reveals AI's Hidden Dependency Web](https://undercoveragent.ai/blog/github-api-crisis-ai-dependency-web) (2026-04-21) — GitHub's new rate limits exposed how AI applications depend on invisible infrastructure webs that traditional software doesn't—making them catastrophically fragile.
- [GitHub's Merge Queue Hides AI Development's Velocity Trap](https://undercoveragent.ai/blog/github-merge-queue-ai-velocity-trap) (2026-04-20) — GitHub's new merge queue promises faster deployments, but velocity-first thinking is creating invisible technical debt in AI applications that traditional debugging can't fix.

## Links

- Website: https://undercoveragent.ai
- Blog: https://undercoveragent.ai/blog
- Demo: https://undercoveragent.ai/demo
- Dashboard: https://undercoveragent.ai/dashboard
- GitHub: https://github.com/dbhurley/undercoveragent
- JSON Feed: https://undercoveragent.ai/api/blog/feed.json

## Founder

- Website: https://dbhurley.com
- Related writing: https://dbhurley.com/blog/the-evaluation-function-is-the-product
- Related writing: https://dbhurley.com/blog/what-an-ai-agency-actually-needs

## Contact

hello@undercoveragent.ai

## Part of the DBH Ventures portfolio

https://dbhurley.com/startups