--- title: "Foundations" canonical: "https://current.tinyfish.ai/issue/2/foundations" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/2/foundations" issue: 2 section: "foundations" description: "Conceptual clarity earned from building at scale" authors: ["Rina Takahashi"] published: 2025-11-05 updated: 2025-11-07 articles: 12 --- # TinyFish > CURRENT is a journal about AI agents in production, for the engineering and product leaders who have to make them work. Drawing on experience building enterprise web agents, it explains why the demo succeeded and the deployment didn't, and what that means for choosing vendors, briefing the board, and avoiding mistakes others have already made. [Read on the website]() ## Issue 2 — Foundations Conceptual clarity earned from building at scale ### What Building Teaches Builders - [Testing Tools Are Not Production Infrastructure]() — By [Rina Takahashi]() · Published 2025-11-04 · Format: Field Guide Watching teams discover their Playwright setup won't scale past demos—it's like watching clouds gather before rain hits. You know what's coming. The industry lumps "browser automation" into one bucket when it's really two architectures solving completely different problems. What's ahead: way more companies hitting this wall as agents move from proof-of-concept to actual production. The gap isn't subtle when you're suddenly managing thousands of browser sessions and everything that seemed simple in staging breaks against real sites. - [The Category Confusion Costing Enterprises Millions]() — By [Rina Takahashi]() · Published 2025-11-07 An enterprise legal team writes an RFP for AI agents. They specify speed benchmarks, accuracy metrics, cost savings against their existing three-person team. Six months later, they've automated 200 annual contract reviews perfectly. The 50,000 historical contracts that could reveal upsell patterns and compliance risks? Still sitting untouched. No one ever had budget to read them anyway. - [The Category Mistake Behind 42% of Failed AI Deployments]() — By [Rina Takahashi]() · Published 2025-11-07 An enterprise legal team issues an RFP for AI agents to improve contract analysis. Evaluation criteria: speed metrics, cost per document, accuracy benchmarks against three existing analysts. Six months later, the project handles their 200 annual contracts fine. But 50,000 historical agreements sit untouched—full of upsell patterns and compliance risks no human team ever had budget to analyze. ### Web Evolution's Lessons - [When Developer Tools Made Selectors Disposable]() — By [Rina Takahashi]() · Published 2025-11-04 · Format: Web Archaeology Last week someone asked why their web automation keeps breaking even when sites look exactly the same. Got me thinking about this frontend tooling shift around 2015 that nobody building automation saw coming. Component frameworks solved real developer problems by making CSS selectors disposable—regenerating them on every build. Brilliant for internal teams, absolute nightmare for systematic monitoring. ### Pattern Recognition from the Field - [Why Agent Demos Keep Failing in Production]() — Published 2025-11-04 I keep seeing the same thing: agents that shine in demos fall apart when you run them repeatedly. Superface's benchmarks tell the story. Simple CRM tasks like creating Salesforce leads or updating HubSpot pipelines fail 75% of the time when agents chain them together. Single actions might work half the time. String six together and you're looking at 10-20% success rates. ### Questions Worth Asking You learn which questions matter by watching things break in production. Feature lists look great in vendor decks. Demos impress in Monday meetings. Then you deploy, and different factors determine whether you're sleeping through the night or debugging at 3am. These six questions cut through the marketing. They're what we ask when evaluating tools because we've seen the patterns. What sounds minor in a demo becomes critical under load. What vendors gloss over becomes your team's operational burden. Ask these before you commit. Not as a comprehensive checklist, but as a way to think more clearly about what actually predicts success when you're building at scale. - **What's Your Absolute Maximum Latency?** — [Source]() — Published 2025-11-04 Tag: Latency Constraints This number determines everything else. A chatbot can wait two seconds. Self-driving systems need milliseconds. Your answer dictates edge versus cloud, model size limits, whether your cost structure even pencils out. Get this wrong and you're rebuilding the entire stack. - **Can You Actually Roll Back?** — [Source]() — Published 2025-11-04 Tag: Recovery Path When production breaks at 2am, what happens? Simple rollback sounds perfect until you've deleted a database column. Some teams burn weeks enabling rollbacks that never work cleanly. Others fix forward. Know which your architecture supports before you're debugging in the dark. - **Do Your Environments Match?** — [Source]() — Published 2025-11-04 Tag: Environment Drift Code works in development, fails in production. Not a mystery. Environmental drift. The worst debugging sessions trace back to subtle differences nobody documented. If CI, staging, and production aren't genuinely similar, you're guessing whether deployments will work. - **Will This Still Exist Next Year?** — [Source]() — Published 2025-11-04 Tag: Vendor Health Every component inherits its vendor's health. Can you hire people who know this technology? Will it get security patches? Components become liabilities when vendors fade. Evaluate the ecosystem and community momentum, not just today's feature list. - **How Far From Demo to Real Deployment?** — [Source]() — Published 2025-11-04 Tag: Production Gap Monday's impressive demo becomes Friday's emergency. Missing GPU drivers. Unhandled edge cases. Dependencies that work on one laptop. The gap between "works in the demo" and "runs reliably at scale" tells you whether you're weeks away or months. - **Does Your Team Have These Skills?** — [Source]() — Published 2025-11-04 Tag: Team Bandwidth ML teams excel at models, not production infrastructure. Before choosing deployment approaches, assess actual engineering capacity. The technically superior solution your team can't maintain becomes the wrong solution. Match architecture to team capabilities, not aspirations. ### In Dialogue With Complexity - [An Interview With Rate Limiting (Who Insists You've Been Misunderstanding Them)]() — Published 2025-11-04 We're sitting in what appears to be a dimly lit server room, though the setting keeps flickering between a corporate office, a diplomatic negotiation table, and occasionally what looks like a nightclub velvet rope. Our guest—who insists we call them simply "Handshake"—seems to exist in multiple states simultaneously, which they assure us is "perfectly normal for a protocol." ## Other sections in Issue 2 - [Daily Brief]() - [Market Pulse]() - [Practitioner's Corner]() - [Echoes]() - [Vision]() ## More - [Publication guide]() - [Issue archive]()