Making AI Safe Shouldn't Be This Hard
After watching hundreds of companies get burned by AI failures,
we decided someone had to fix this.
The Story
Leon Melamud, Founder
GenAI Lead • AWS Expert • AI Community Builder
Miami, Florida
I'll never forget the day I watched Chevrolet's chatbot agree to sell a $70,000 SUV for $1.
It wasn't funny. It was terrifying. Because I knew that could be any of us.
I'm a GenAI Lead and full-stack engineer with years of experience building AI-powered products at scale for major enterprises in the finance and cybersecurity industries. From serverless architectures on AWS to complex data pipelines, I've shipped systems used by millions of people every day.
Beyond engineering, I'm passionate about growing the AI ecosystem. I'm a co-founder of several AI communities, including MCP Israel, A2A, WebMCP, n8n Israel, and AI Transformation Leaders. I'm also an active mentor and public speaker, sharing practical knowledge on GenAI adoption, agent architectures, and AI safety at conferences and meetups.
But I'd also seen the dark side. The late-night Slack messages about hallucinations in production. The support tickets from confused users who got nonsensical answers. The security reviews that found prompt injection vulnerabilities nobody had thought to test for.
The problem? Everyone was building AI agents. Nobody was testing them properly.
Not because they didn't care. Because they didn't know how. The tools were scattered. The knowledge was locked in research papers. The playbooks didn't exist.
So I started documenting every AI failure I could find. Chevrolet. Air Canada. DPD. Google Bard's $100 billion mistake. ChatGPT's lawyer citing fake cases. The list kept growing.
500+ failures later, the patterns became crystal clear:
- 90% of failures were preventable with proper testing
- Most companies had no testing process beyond "try it and see"
- The few who tested well caught vulnerabilities before launch
- Nobody was sharing what worked — everyone was learning the hard way
That's when it hit me: this is fixable.
Not with another complex enterprise platform. Not with academic papers nobody reads. But with something simple: help people understand their risks, learn from others' failures, and get a clear path forward.
That's why I built InspectAgents.
Communities & Leadership
MCP Israel
Co-FounderModel Context Protocol community connecting developers with the latest in AI agent tooling.
A2A
Co-FounderAgent-to-Agent protocol community advancing multi-agent collaboration standards.
WebMCP
Co-FounderWeb-based MCP community pushing browser-native AI agent capabilities forward.
n8n Israel
Co-FounderCommunity for the open-source workflow automation platform — AI-powered automations at scale.
AI Transformation Leaders
Co-FounderExecutive community for leaders driving AI adoption and transformation in enterprise organizations.
Mentoring & Speaking
Active SpeakerSharing practical GenAI knowledge at conferences — agent architectures, AI safety, and cloud-native AI.
Our Mission
Make AI agent testing accessible, practical, and transparent for every business — not just tech giants.
Accessible
No PhD required. No enterprise contracts. Start with a free quiz and get actionable insights in 5 minutes.
Practical
Learn from real failures, not theory. Get step-by-step playbooks, not abstract frameworks.
Transparent
Share what works. Build in public. Help the entire industry learn faster together.
What We Believe
Every AI failure is a lesson — document it, learn from it, prevent it next time
Testing should be simple — if it's complicated, nobody will do it
Knowledge should be shared — keeping AI safety secrets doesn't help anyone
Prevention beats reaction — catch issues before they reach customers
Small teams can build safely — you don't need a 50-person safety team
What We're Building
📚 The Failure Database
500+ documented AI failures with root causes, business impact, and prevention strategies. The most comprehensive public collection of AI incidents anywhere.
Browse the database →🎯 The Risk Quiz
Free 5-minute assessment that identifies your biggest AI vulnerabilities based on 1,000+ real-world failure patterns.
Take the quiz →📖 Testing Playbooks
Step-by-step guides for hallucination detection, prompt injection testing, security audits, and more — written for developers, not researchers.
Read the guides →🔍 The Glossary
Plain-English definitions of 20+ AI safety terms with real examples. No jargon, no academic papers — just clear explanations.
Explore the glossary →Join the Movement
We're building a community of founders, developers, and product leaders who care about deploying AI safely.