--- title: "Practitioner's Corner" canonical: "https://current.tinyfish.ai/issue/30/practitioners-corner" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/30/practitioners-corner" issue: 30 section: "practitioners-corner" description: "Lessons from the field—what we see building at scale" authors: ["Rina Takahashi","Nora Kaplan"] published: 2026-05-25 updated: 2026-05-21 articles: 9 --- # 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 30 — Practitioner's Corner Lessons from the field—what we see building at scale ### The Web's Second Layer - [The Instruments Before the Agents]() — By [Rina Takahashi]() · Published 2026-05-20 · Format: The Web's Second Layer Every layer of web infrastructure, from crawling to indexing to ranking, was designed for a user who clicks and scans. Agents don't click or scan. They need structured, verifiable text delivered into a context window, and the web doesn't offer that natively. Parag Agrawal founded Parallel Web Systems around this gap. The work looks like solving one bottleneck every few weeks and immediately hitting another: APIs too slow, indexing at insufficient scale, content economics with no measurement layer. Each instrument that makes one dimension legible reveals the next one that isn't. ### The Wrong Instrument - [The Instrument That Doesn't Exist Yet]() — By [Nora Kaplan]() · Published 2026-05-20 · Format: The Wrong Instrument A production agent runs for twelve minutes, calls nine tools, and returns a clean result. Every dashboard is green. The result is wrong — subtly wrong in a way that passes validation and flows into tomorrow's decisions unchallenged. The observability market now has over 66 tools, most built to answer whether an agent completed its work. Almost none can answer whether the work was correct. The gap between those two questions is wider than it looks, and the instrument that would close it has design requirements that pull against each other in ways we haven't resolved. ### Operating in the Gap - [Kit Voss Debugs Systems That Don't Know They're Broken]() — Published 2026-05-20 *Kit Voss is not a real person, which is the only way we could get someone in this role to speak this candidly. But every detail here — the failure modes, the workarounds, the 2 AM trace archaeology — is drawn from published practitioner accounts and production data. Kit is a Staff AI Platform Engineer at a B2B data enrichment company, responsible for roughly 80 million agent executions per month.* ### The Metric That Hides the Architecture - [The Metric You Pick Determines the Architecture You Ship]() — Published 2026-05-20 Agentic workflows burn 5 to 30 times more tokens per task than a chatbot interaction. Context compounds with every loop, every tool call, every retry. By step 50, you're paying for the same conversation history 50 times over. ### Further Reading - **AI Agent Observability Tools: 2026 Buyer's Guide for Production Teams** — [Source]() — Published 2026-05-20 Sixty-six observability tools and counting, yet most inherited their architecture from LLM debugging rather than agent operations. The LLM-first vs. agent-native distinction matters because it determines whether you're watching the model think or watching the system behave. - **LangChain's CEO argues that better models alone won't get your AI agent to production** — [Source]() — Published 2026-05-20 Harrison Chase makes the case for context engineering, arguing that most agent failures trace back to what the model was given, not what the model did with it. Worth reading alongside the journal's observation that "the model absorbs blame structurally." - **Without controls, an AI agent can cost more than an employee** — [Source]() — Published 2026-05-20 Named-source cost figures ground the abstract economics: tight deployments and sloppy ones diverge by 10x. A useful reminder that the sensing gap has a dollar sign attached, even when nobody's tracking it under that name. - **Agents Work. Now the Hard Part: Notes from LangChain Interrupt 2026** — [Source]() — Published 2026-05-20 Practitioner observations from 1,000+ attendees at the production threshold, with instructive details from Lyft, Cisco, Toyota, and LATAM. The recurring theme: evaluation in simulation and evaluation in production are different activities that share a name. - **Parag Agrawal's Parallel wants to pay publishers when AI agents use their work** — [Source]() — Published 2026-05-20 Former Twitter CEO Parag Agrawal is building infrastructure for what happens when machines, not humans, become the primary consumers of web content. The economics of agent web access reshape incentives on both sides of the request. ## Other sections in Issue 30 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Echoes]() - [Vision]() ## More - [Publication guide]() - [Issue archive]()