Foundations
Conceptual clarity earned from building at scale
Foundations
Conceptual clarity earned from building at scale
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.
Rina Takahashi
Rina Takahashi, 37, former marketplace operations engineer turned enterprise AI writer. Built and maintained web-facing automations at scale for travel and e-commerce platforms. Now writes about reliable web agents, observability, and production-grade AI infrastructure at TinyFish.
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.
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.
Connecting existing systems consumes 40% of IT resources before agents can operate, with 80% of enterprises struggling with basic connectivity.
LangChain dominates at 55.6% adoption while OpenAI powers 73.6% of projects, showing clear winners emerging despite vendor proliferation.
Organizations seeing significant ROI are twice as likely to have rebuilt end-to-end processes with explicit human handoff points.
Very large models like GPT-4o deliver accuracy but become prohibitively expensive when running multi-agent systems at production scale.
Gartner estimates only 130 of thousands of vendors claiming "agentic AI" actually deliver autonomous capabilities versus rebranded automation.
Questions Worth Asking
Most evaluation criteria focus on what works in controlled conditions. Feature completeness. Benchmark performance. API elegance. Then production happens.
The expensive mistakes come from asking the wrong questions early. Teams evaluate capabilities instead of operational burden. They test performance with clean data instead of the messy reality their systems will face. They assume debuggability instead of verifying it.
After enough deployments, you learn which questions actually predict success. They're not the ones in vendor comparison matrices. They're the ones that reveal what happens when demos meet real traffic, when requirements change, when something breaks at 3am and you need answers fast.
Most evaluation criteria focus on what works in controlled conditions. Feature completeness. Benchmark performance. API elegance. Then production happens.
The expensive mistakes come from asking the wrong questions early. Teams evaluate capabilities instead of operational burden. They test performance with clean data instead of the messy reality their systems will face. They assume debuggability instead of verifying it.
After enough deployments, you learn which questions actually predict success. They're not the ones in vendor comparison matrices. They're the ones that reveal what happens when demos meet real traffic, when requirements change, when something breaks at 3am and you need answers fast.