Foundations
Conceptual clarity earned from building at scale
Foundations
Conceptual clarity earned from building at scale
When Web Agents Need to Remember What They're Doing
A web agent halfway through extracting hotel inventory across regional booking systems hits a rate limit. Should it remember where it was? Compare that to another agent checking a single price in three seconds. One workflow needs to preserve an hour of authentication and navigation state. The other can restart faster than you could save its progress.
Operating web agents at scale, this distinction shapes infrastructure complexity, recovery mechanisms, what you can reliably run. State persistence isn't binary. It's a spectrum, and understanding where your workflows fall determines what you actually need to build. Most teams make architectural commitments before they have that map.
When Web Agents Need to Remember What They're Doing
A web agent halfway through extracting hotel inventory across regional booking systems hits a rate limit. Should it remember where it was? Compare that to another agent checking a single price in three seconds. One workflow needs to preserve an hour of authentication and navigation state. The other can restart faster than you could save its progress.
Operating web agents at scale, this distinction shapes infrastructure complexity, recovery mechanisms, what you can reliably run. State persistence isn't binary. It's a spectrum, and understanding where your workflows fall determines what you actually need to build. Most teams make architectural commitments before they have that map.
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.
The Twenty-Four Hour Problem
During a site migration, our web agents encounter something strange: different parts of our infrastructure see different versions of reality. DNS resolvers return different IP addresses for the same domain. Requests time out. Authentication fails. A 1983 architectural choice, working exactly as designed.
The number that protected root servers on a research network now creates windows where truth is relative and time-delayed. Operating web automation at scale, we can't assume DNS resolution returns consistent answers. We build around it instead.
The Twenty-Four Hour Problem
During a site migration, our web agents encounter something strange: different parts of our infrastructure see different versions of reality. DNS resolvers return different IP addresses for the same domain. Requests time out. Authentication fails. A 1983 architectural choice, working exactly as designed.
The number that protected root servers on a research network now creates windows where truth is relative and time-delayed. Operating web automation at scale, we can't assume DNS resolution returns consistent answers. We build around it instead.
An Interview With 600 Mbps, the Bandwidth Threshold Where HTTP/3 Stops Working
An Interview With 600 Mbps, the Bandwidth Threshold Where HTTP/3 Stops Working
Pattern Recognition from the Field
Six vendors launched AI agent identity management in November 2025. Microsoft, AWS, CyberArk, Ping Identity, Oasis Security, Akeyless. All solving the same problem: agent sprawl.
Here's what that timing tells you. 82% of companies already run AI agents. 53% of those agents touch sensitive data daily. But the infrastructure to track them, secure them, manage their lifecycles? Just showing up now.
Watch what happens in practice. Agent ownership changes hands four times in the first year. Developer leaves, agent stays. Still holding credentials, still accessing systems. No owner, no lifecycle, no audit trail.
The industry shipped agents first, asked governance questions later. Now we're retrofitting identity management onto deployed systems. Build it in from the start. Your future security team will thank you.
Six vendors launched AI agent identity management in November 2025. Microsoft, AWS, CyberArk, Ping Identity, Oasis Security, Akeyless. All solving the same problem: agent sprawl.
Here's what that timing tells you. 82% of companies already run AI agents. 53% of those agents touch sensitive data daily. But the infrastructure to track them, secure them, manage their lifecycles? Just showing up now.
Watch what happens in practice. Agent ownership changes hands four times in the first year. Developer leaves, agent stays. Still holding credentials, still accessing systems. No owner, no lifecycle, no audit trail.
The industry shipped agents first, asked governance questions later. Now we're retrofitting identity management onto deployed systems. Build it in from the start. Your future security team will thank you.
Pilots jumped from 37% to 65% in one quarter, but full production stuck at 11% because governance infrastructure wasn't ready.
Agents pass from executive sponsor to AI team to Cloud Ops during year one, creating gaps when original builders leave.
Human and application identities are well understood. Agent identities remain unsettled, with standards catching up to what's already deployed.
Teams spin up agents independently. Result is overlaps, redundancies, runaway API calls, agents looping endlessly and overloading systems.
Google donated Agent2Agent to Linux Foundation in June. Microsoft announced broad MCP support across platforms in May.
Questions Worth Asking
The questions you ask before choosing a tool matter more than the vendor answers you get. We've watched organizations stumble not because they picked the wrong technology, but because they asked the wrong questions. Or skipped the hard ones entirely.
Teams focus on features and demos while missing what predicts success at scale. They evaluate tools in isolation instead of asking how those tools will interact with their actual processes, data, and people.
These six questions cut through that noise. They're what we return to when evaluating anything meant for production, because they reveal what actually matters once the demo ends and real work begins.
The questions you ask before choosing a tool matter more than the vendor answers you get. We've watched organizations stumble not because they picked the wrong technology, but because they asked the wrong questions. Or skipped the hard ones entirely.
Teams focus on features and demos while missing what predicts success at scale. They evaluate tools in isolation instead of asking how those tools will interact with their actual processes, data, and people.
These six questions cut through that noise. They're what we return to when evaluating anything meant for production, because they reveal what actually matters once the demo ends and real work begins.