Practitioner's Corner
Practitioner's Corner
When Documentation Becomes More Real Than the System
A workflow fails on a specific site. The team checks the documented selector pattern—unchanged for six months. They debug why the selector isn't working: testing syntax variations, reviewing recent code changes, checking if the site's anti-bot measures changed. Twenty minutes in, someone loads the actual site. The HTML structure changed three weeks ago.
Twenty minutes debugging a selector that stopped working three weeks ago. They knew sites change. But the documentation said it should work. Something about that detailed, authoritative runbook shifted how they approached the problem. What happens when the documented system becomes more real than the one actually running?
When Documentation Becomes More Real Than the System
A workflow fails on a specific site. The team checks the documented selector pattern—unchanged for six months. They debug why the selector isn't working: testing syntax variations, reviewing recent code changes, checking if the site's anti-bot measures changed. Twenty minutes in, someone loads the actual site. The HTML structure changed three weeks ago.
Twenty minutes debugging a selector that stopped working three weeks ago. They knew sites change. But the documentation said it should work. Something about that detailed, authoritative runbook shifted how they approached the problem. What happens when the documented system becomes more real than the one actually running?
Atindriyo Sanyal on Why Full Visibility Doesn't Mean You Can Debug
Dashboards report 98% success rates. Logs capture every decision. Traces follow execution paths. Customers report broken workflows.
Atindriyo Sanyal spent years bringing thousands of AI models into production at Uber. When he co-founded Galileo in 2021, the team assumed visibility would unlock reliability. They built the observability platform they thought production needed. Then they discovered why systems that produce different outputs from identical inputs require something observability alone can't provide—and why 94% of production agents have full tracing while quality issues remain the number one barrier.
Atindriyo Sanyal on Why Full Visibility Doesn't Mean You Can Debug
Dashboards report 98% success rates. Logs capture every decision. Traces follow execution paths. Customers report broken workflows.
Atindriyo Sanyal spent years bringing thousands of AI models into production at Uber. When he co-founded Galileo in 2021, the team assumed visibility would unlock reliability. They built the observability platform they thought production needed. Then they discovered why systems that produce different outputs from identical inputs require something observability alone can't provide—and why 94% of production agents have full tracing while quality issues remain the number one barrier.
The Number That Matters
Akamai tracked TLS fingerprints growing from 18,652 distinct signatures in August 2018 to billions today. That original count represented 0.00000159% of theoretical possibilities. Now? The fingerprint space has essentially become infinite as attackers continuously mutate SSL/TLS client behavior.
About 82% of malicious traffic runs over secure connections. Bot detection can't rely on TLS fingerprints alone anymore. Perfect mimicry of legitimate handshakes still fails when headless Chrome flags appear, when User-Agent strings don't match declared browsers, when canvas fingerprints show automation artifacts. The defense moved to multi-signal correlation: TLS data plus browser signals plus behavioral patterns. Static fingerprint blocking became obsolete years ago.
Practitioner Resources
