Foundations
Foundations
What Reliability Actually Means in Adversarial Environments
We've seen web agent systems maintain 100% availability while delivering zero correct results. Every request completes successfully. Every HTTP response returns 200. The infrastructure is "up." But every extraction triggers detection and returns error pages instead of data. Traditional uptime metrics can't capture what's actually broken. When the web actively resists automation, reliability means something different than availability percentages—something most teams don't measure until after deployment.
What Reliability Actually Means in Adversarial Environments
We've seen web agent systems maintain 100% availability while delivering zero correct results. Every request completes successfully. Every HTTP response returns 200. The infrastructure is "up." But every extraction triggers detection and returns error pages instead of data. Traditional uptime metrics can't capture what's actually broken. When the web actively resists automation, reliability means something different than availability percentages—something most teams don't measure until after deployment.
The Paradox of Web Transparency
View Source still works on every website. The HTML arrives readable, transparent, exactly as Tim Berners-Lee designed it in 1991. The web's founding principle of radical openness never disappeared. The architecture still assumes anyone can see how things work.
Yet building reliable automation on this transparent foundation now requires infrastructure most enterprises can't afford. The same openness that enabled the web's growth created conditions for something unexpected. The architecture remained universal. Who could act on it did not.
The Paradox of Web Transparency
View Source still works on every website. The HTML arrives readable, transparent, exactly as Tim Berners-Lee designed it in 1991. The web's founding principle of radical openness never disappeared. The architecture still assumes anyone can see how things work.
Yet building reliable automation on this transparent foundation now requires infrastructure most enterprises can't afford. The same openness that enabled the web's growth created conditions for something unexpected. The architecture remained universal. Who could act on it did not.
Pattern Recognition from the Field
I keep seeing the same thing: companies run successful AI agent pilots, then production deployment stalls. Pilots jumped from 37% to 65% in one quarter. Production deployment? Still stuck at 11%. MIT found only 5% of custom enterprise AI tools actually make it to production.
Look at what's breaking. Apple delayed Siri features. Amazon stripped down Alexa+. Salesforce's Einstein hits data silos. The pattern isn't about AI capability. Eighty percent of enterprises can't connect their systems. Legacy infrastructure wasn't built for autonomous agents.
The companies getting agents into production? They redesigned workflows first. McKinsey found they're twice as likely to see real ROI. This is an infrastructure problem that happens to involve AI, not an AI problem that needs infrastructure.
