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
The Hidden Economics of Retry Logic
One authentication check fails. The system retries. Within seconds, that single failure becomes fifteen authentication attempts, five rate limit violations, and a blocked IP address. The logic seems sound: if at first you don't succeed, try again. But in web automation, retry logic doesn't just repeat operations—it multiplies them across layers, amplifies costs, and can transform a recoverable failure into a multi-hour outage.
At what point does persistence stop being a recovery mechanism and start being the problem itself?
The Hidden Economics of Retry Logic
One authentication check fails. The system retries. Within seconds, that single failure becomes fifteen authentication attempts, five rate limit violations, and a blocked IP address. The logic seems sound: if at first you don't succeed, try again. But in web automation, retry logic doesn't just repeat operations—it multiplies them across layers, amplifies costs, and can transform a recoverable failure into a multi-hour outage.
At what point does persistence stop being a recovery mechanism and start being the problem itself?
When Twenty Services Pretend to Be One Website
A page loads. Product listings appear, prices populate, checkout button ready. The user sees one coherent website. Operationally, dozens of independent services just assembled themselves—each from different infrastructure, each on its own timeline, each capable of failing while the page still renders. Users never notice this coordination problem. The page looks functional. But is the payment processor actually ready? Has fraud detection finished? Are required scripts loaded? At scale, these questions become operational reality.
When Twenty Services Pretend to Be One Website
A page loads. Product listings appear, prices populate, checkout button ready. The user sees one coherent website. Operationally, dozens of independent services just assembled themselves—each from different infrastructure, each on its own timeline, each capable of failing while the page still renders. Users never notice this coordination problem. The page looks functional. But is the payment processor actually ready? Has fraud detection finished? Are required scripts loaded? At scale, these questions become operational reality.
Theory Meets Production Reality
Why Perfect Bot Detection Is Operationally Impossible
Block a legitimate customer and watch them abandon their cart. Let a scraper through and it extracts competitive intelligence. Websites must achieve precision that's operationally impossible: filtering half of all internet traffic without touching revenue. The bot security market hit $668 million in 2024. Building web agent infrastructure means encountering these detection systems thousands of times daily. We see what defenders actually pay for precision they can't fully achieve.
Why Reliable Automation Requires More Infrastructure Than Detection
We run millions of requests daily through enterprise web agent infrastructure, maintaining 98%+ success rates while detection systems evolve. The operational complexity concentrates entirely on the automation side. Defense should be harder than offense, right? But persistence at scale requires more infrastructure than precision. Getting through detection is just the beginning. Maintaining reliability across those millions of requests, adapting to whatever defenders deploy—that's where the real operational weight lives.
Why Perfect Bot Detection Is Operationally Impossible
Block a legitimate customer and watch them abandon their cart. Let a scraper through and it extracts competitive intelligence. Websites must achieve precision that's operationally impossible: filtering half of all internet traffic without touching revenue. The bot security market hit $668 million in 2024. Building web agent infrastructure means encountering these detection systems thousands of times daily. We see what defenders actually pay for precision they can't fully achieve.
Why Reliable Automation Requires More Infrastructure Than Detection
We run millions of requests daily through enterprise web agent infrastructure, maintaining 98%+ success rates while detection systems evolve. The operational complexity concentrates entirely on the automation side. Defense should be harder than offense, right? But persistence at scale requires more infrastructure than precision. Getting through detection is just the beginning. Maintaining reliability across those millions of requests, adapting to whatever defenders deploy—that's where the real operational weight lives.
Why Perfect Bot Detection Is Operationally Impossible
Block a legitimate customer and watch them abandon their cart. Let a scraper through and it extracts competitive intelligence. Websites must achieve precision that's operationally impossible: filtering half of all internet traffic without touching revenue. The bot security market hit $668 million in 2024. Building web agent infrastructure means encountering these detection systems thousands of times daily. We see what defenders actually pay for precision they can't fully achieve.
Why Reliable Automation Requires More Infrastructure Than Detection
We run millions of requests daily through enterprise web agent infrastructure, maintaining 98%+ success rates while detection systems evolve. The operational complexity concentrates entirely on the automation side. Defense should be harder than offense, right? But persistence at scale requires more infrastructure than precision. Getting through detection is just the beginning. Maintaining reliability across those millions of requests, adapting to whatever defenders deploy—that's where the real operational weight lives.
The Number That Matters
A Selenium-based scraper hits 4GB of RAM consumption after roughly 2,500 page accesses. Not 25,000. Not 250,000. Twenty-five hundred.
Run that scraper at 100,000 pages per hour and you're restarting infrastructure every few minutes. Memory accumulates like sediment. Each session leaves traces. Every JavaScript execution, every DOM manipulation, every cookie jar adds weight that never fully clears.
The math is brutal and predictable. What monitors a dozen competitor sites breaks completely when tracking inventory across thousands of SKUs. Your infrastructure doesn't crash spectacularly. It just consumes resources nobody budgeted for, forcing restart orchestration that becomes its own operational burden.
A Selenium-based scraper hits 4GB of RAM consumption after roughly 2,500 page accesses. Not 25,000. Not 250,000. Twenty-five hundred.
Run that scraper at 100,000 pages per hour and you're restarting infrastructure every few minutes. Memory accumulates like sediment. Each session leaves traces. Every JavaScript execution, every DOM manipulation, every cookie jar adds weight that never fully clears.
The math is brutal and predictable. What monitors a dozen competitor sites breaks completely when tracking inventory across thousands of SKUs. Your infrastructure doesn't crash spectacularly. It just consumes resources nobody budgeted for, forcing restart orchestration that becomes its own operational burden.
The documented scraper maintained 50% CPU utilization while processing pages at 2MB/second throughput, revealing the computational overhead behind each request.
Even headless browsers consuming 60-80% fewer resources than traditional browsers still demand substantial memory management infrastructure at production scale.
Infrastructure restarts become operationally necessary every few thousand pages due to predictable resource accumulation, not software failures or bugs.
The gap between testing environments and production reality appears around 2,500 page accesses, earlier than most engineering teams anticipate when scoping projects.
Memory management and restart orchestration represent operational costs absent from initial automation estimates, vendor comparisons, and build-versus-buy analyses.
Field Notes from the Ecosystem
November brought failures and adaptations in roughly equal measure. APIs without rate limits. Bot detection that detects nothing. Enterprises averaging ten observability tools while complaining about complexity.
Then the adaptations: Kubernetes learning to schedule GPUs efficiently. Organizations cutting observability costs 75% through smarter sampling. The usual pattern of systems breaking, then getting patched, then breaking differently.
One observation stands out. Your infrastructure provider can see exactly what you're building. API patterns, token usage, query types. The hyperscalers have comprehensive competitive intelligence about the application layer. Infrastructure position provides visibility that most customers haven't considered.
November brought failures and adaptations in roughly equal measure. APIs without rate limits. Bot detection that detects nothing. Enterprises averaging ten observability tools while complaining about complexity.
Then the adaptations: Kubernetes learning to schedule GPUs efficiently. Organizations cutting observability costs 75% through smarter sampling. The usual pattern of systems breaking, then getting patched, then breaking differently.
One observation stands out. Your infrastructure provider can see exactly what you're building. API patterns, token usage, query types. The hyperscalers have comprehensive competitive intelligence about the application layer. Infrastructure position provides visibility that most customers haven't considered.