Practitioner's Corner
Lessons from the field—what we see building at scale
Practitioner's Corner
Lessons from the field—what we see building at scale
One Price Every 36 Seconds
There's still someone at most companies checking competitor prices manually—open tab, copy number, paste into spreadsheet, repeat. One price every 36 seconds if they're fast and the sites cooperate. Building web automation makes you realize why this person hasn't been replaced yet: the web fights systematic monitoring at every turn. CAPTCHAs, authentication labyrinths, sites that redesign overnight. What looks simple—"just check the website"—turns into infrastructure problems most teams don't see coming.
Going to dig into this gap over the next few weeks. What it actually takes to replace human adaptability with reliable automation, and why the distance between "we should automate this" and production-ready systems is further than the demos suggest.
One Price Every 36 Seconds
There's still someone at most companies checking competitor prices manually—open tab, copy number, paste into spreadsheet, repeat. One price every 36 seconds if they're fast and the sites cooperate. Building web automation makes you realize why this person hasn't been replaced yet: the web fights systematic monitoring at every turn. CAPTCHAs, authentication labyrinths, sites that redesign overnight. What looks simple—"just check the website"—turns into infrastructure problems most teams don't see coming.
Going to dig into this gap over the next few weeks. What it actually takes to replace human adaptability with reliable automation, and why the distance between "we should automate this" and production-ready systems is further than the demos suggest.
When the Website Isn't in the HTML
Been watching this pattern repeat: someone needs to extract pricing data from a site, opens view source expecting to see the prices, finds nothing but script tags and empty divs. Then they look at what's actually rendering in the browser—full tables, interactive elements, everything—and realize the website isn't in the HTML anymore. It's assembled on-the-fly by JavaScript pulling from APIs. The weather's turning on this: we're drifting from "web as documents" to "web as runtime environment."
What's forecast is messier infrastructure requirements. The gap between parsing HTML (cheap, fast, simple) and executing JavaScript in full browsers (expensive, slow, adversarial) determines what's actually possible at scale. That split is widening.
When the Website Isn't in the HTML
Been watching this pattern repeat: someone needs to extract pricing data from a site, opens view source expecting to see the prices, finds nothing but script tags and empty divs. Then they look at what's actually rendering in the browser—full tables, interactive elements, everything—and realize the website isn't in the HTML anymore. It's assembled on-the-fly by JavaScript pulling from APIs. The weather's turning on this: we're drifting from "web as documents" to "web as runtime environment."
What's forecast is messier infrastructure requirements. The gap between parsing HTML (cheap, fast, simple) and executing JavaScript in full browsers (expensive, slow, adversarial) determines what's actually possible at scale. That split is widening.
Theory Meets Production Reality
When the Tenth Session Breaks Everything
Keep seeing the same pattern: pilot runs at ten sessions, someone asks about scaling to a hundred, system just stops. Not gradually—it stops. Same code, same logic. The infrastructure gaps were always there, but pilot conditions never exposed them. Cascades that couldn't happen at low volume. Rate limits you never hit. Session management that worked fine until it didn't. This piece walks through what breaks and why those failures were inevitable. Second piece covers the other side—the manual work keeping pilots alive that nobody tracks.
The Three Hours Nobody Saw
Saw a pilot where someone cleaned data three hours every morning before automation ran. Demo never showed those hours. Pilot succeeded partly because that person existed. Edge cases that show up once in test data appear a hundred times daily at production scale. Manual oversight that's feasible for ten sessions becomes impossible for thousands. This examines what pilots hide—not infrastructure that breaks under load, but human dependencies that can't scale. Pairs with the technical failures piece because both reveal why pilot success masks what production actually demands.
When the Tenth Session Breaks Everything
Keep seeing the same pattern: pilot runs at ten sessions, someone asks about scaling to a hundred, system just stops. Not gradually—it stops. Same code, same logic. The infrastructure gaps were always there, but pilot conditions never exposed them. Cascades that couldn't happen at low volume. Rate limits you never hit. Session management that worked fine until it didn't. This piece walks through what breaks and why those failures were inevitable. Second piece covers the other side—the manual work keeping pilots alive that nobody tracks.
The Three Hours Nobody Saw
Saw a pilot where someone cleaned data three hours every morning before automation ran. Demo never showed those hours. Pilot succeeded partly because that person existed. Edge cases that show up once in test data appear a hundred times daily at production scale. Manual oversight that's feasible for ten sessions becomes impossible for thousands. This examines what pilots hide—not infrastructure that breaks under load, but human dependencies that can't scale. Pairs with the technical failures piece because both reveal why pilot success masks what production actually demands.
When the Tenth Session Breaks Everything
Keep seeing the same pattern: pilot runs at ten sessions, someone asks about scaling to a hundred, system just stops. Not gradually—it stops. Same code, same logic. The infrastructure gaps were always there, but pilot conditions never exposed them. Cascades that couldn't happen at low volume. Rate limits you never hit. Session management that worked fine until it didn't. This piece walks through what breaks and why those failures were inevitable. Second piece covers the other side—the manual work keeping pilots alive that nobody tracks.
The Three Hours Nobody Saw
Saw a pilot where someone cleaned data three hours every morning before automation ran. Demo never showed those hours. Pilot succeeded partly because that person existed. Edge cases that show up once in test data appear a hundred times daily at production scale. Manual oversight that's feasible for ten sessions becomes impossible for thousands. This examines what pilots hide—not infrastructure that breaks under load, but human dependencies that can't scale. Pairs with the technical failures piece because both reveal why pilot success masks what production actually demands.
The Number That Matters
Imperva's network blocked 13 trillion malicious bot requests in 2024. Trillion with a T. Do the arithmetic: 35.6 billion requests per day, 413 million per hour, 6.9 million requests every single minute, all year long.
The number itself tells you something about what defending the modern web actually requires. Every one of those 13 trillion requests got identified, analyzed, and blocked in real-time. Each one cost computational cycles, network bandwidth, decision latency. The infrastructure processing that volume—making 6.9 million binary decisions per minute, continuously—exists at a scale most people never see.
For context: automated traffic crossed 51% of all web traffic in 2024, the first time bots outnumbered humans in a decade. The defensive infrastructure scaled accordingly. An entire parallel internet built for bot defense now operates at volumes that make consumer security tools look like toys.
Imperva's network blocked 13 trillion malicious bot requests in 2024. Trillion with a T. Do the arithmetic: 35.6 billion requests per day, 413 million per hour, 6.9 million requests every single minute, all year long.
The number itself tells you something about what defending the modern web actually requires. Every one of those 13 trillion requests got identified, analyzed, and blocked in real-time. Each one cost computational cycles, network bandwidth, decision latency. The infrastructure processing that volume—making 6.9 million binary decisions per minute, continuously—exists at a scale most people never see.
For context: automated traffic crossed 51% of all web traffic in 2024, the first time bots outnumbered humans in a decade. The defensive infrastructure scaled accordingly. An entire parallel internet built for bot defense now operates at volumes that make consumer security tools look like toys.
Nearly 44% of advanced bot traffic now hits API endpoints versus just 10% for traditional web applications, fundamentally shifting where defensive infrastructure must focus its attention.
Travel sites face 48% bad bot traffic, retail sees 59%, revealing how certain sectors bear disproportionate defensive costs simply because of what they sell.
Simple bot attacks increased from 40% to 45% year-over-year as AI lowered barriers to entry, meaning defensive systems handle both volume and variety simultaneously.
ATO attacks surged 40% in 2024 to roughly 330,000 incidents in December alone, adding another defensive layer beyond basic bot detection requirements.
Processing 6.9 million decisions per minute requires distributed systems, real-time analysis, and constant model updates. Complexity compounds at every scale threshold.
Field Notes from the Ecosystem
October delivered three Azure outages in thirty days. Same platform, different failure modes, millions affected each time. Cloudflare processed 100 million bot challenges without a breach. Scrapers still get caught reading 300 pages per minute.
Shared token buckets look elegant in architecture diagrams. In production, they become single points of failure. Stealth mode plugins promise invisibility but leave HTTP/2 fingerprints. These patterns only surface when systems hit scale.
October delivered three Azure outages in thirty days. Same platform, different failure modes, millions affected each time. Cloudflare processed 100 million bot challenges without a breach. Scrapers still get caught reading 300 pages per minute.
Shared token buckets look elegant in architecture diagrams. In production, they become single points of failure. Stealth mode plugins promise invisibility but leave HTTP/2 fingerprints. These patterns only surface when systems hit scale.