--- title: "Practitioner's Corner" canonical: "https://current.tinyfish.ai/issue/31/practitioners-corner" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/31/practitioners-corner" issue: 31 section: "practitioners-corner" description: "Lessons from the field—what we see building at scale" authors: ["Rina Takahashi","Nora Kaplan"] published: 2026-06-01 updated: 2026-05-28 articles: 14 --- # 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 31 — Practitioner's Corner Lessons from the field—what we see building at scale ### Reliability vs. Correctness - [Retries Were Built for a Deterministic World]() — By [Rina Takahashi]() · Published 2026-05-27 · Format: Reliability vs. Correctness A request times out. You send it again. Retry logic is so fundamental to distributed systems that most frameworks ship it by default. It works because the retried operation is identical to the original. Replace the executor with an LLM, and that assumption dissolves. The agent may reason differently on the second attempt, choose different tools, take a different path. Deterministic side effects, produced by a nondeterministic executor. Reliability infrastructure, doing exactly what it was designed to do, starts producing incorrect outcomes. ### Builder Profile - [Between Complete and Correct]() — By [Rina Takahashi]() · Published 2026-05-28 · Format: Builder Profile An agent asked to add a specific sneaker to a cart navigated Amazon, found a shoe, added it, and reported success. It was the wrong shoe. Every step completed. The task failed. Browser Use co-founders Gregor Zunic and Magnus Müller have made two moves that trace this gap between "complete" and "correct": a migration to raw Chrome DevTools Protocol for real-time browser visibility, and an unsolved problem listed quietly on their hiring page in five words. One reaches for more observation. The other, reproducibility. Neither closes the gap. ### Governing the Invisible - [Governing the Thirty Percent That Was Polite Enough to Introduce Itself]() — Published 2026-05-28 *Dashiell "Dash" Greenfield is, by his own admission, a fictional composite assembled from the operational realities that multiple platform engineers encounter roughly 90 days into governing an enterprise agent layer. He agreed to this interview on the condition that we note he is "at least as real as the agent activity in most enterprise audit logs." As will become clear, that's a more loaded statement than it sounds.* ### The Overnight Run - [27 Million Tokens, Zero Progress]() — Published 2026-05-28 New Year's Eve 2025. A Claude Code sub-agent entered a loop, retried the same failing command over 300 times across 4.6 hours, and burned roughly 27 million tokens. The user got the bill. GitHub issue #15909 was closed without a fix. ### Further Reading - **State of Agent Engineering** — [Source]() — Published 2026-05-28 LangChain surveyed 1,340 practitioners and found a number that should bother everyone: 89% have observability, but only 52% run evals. Nearly all teams can confirm their agents ran. Roughly half can say whether the agents were right. - **AI Didn't Break Your Production — Your Architecture Did** — [Source]() — Published 2026-05-28 A useful reframe from Microsoft's engineering blog: the moment an agent can change state, a wrong answer stops being a wrong answer and becomes an incident. The piece applies distributed systems fundamentals (idempotency, circuit breakers, rollback) to the agent layer, where most teams haven't thought to look yet. - **State of Browser Use, May 2026** — [Source]() — Published 2026-05-28 Michael Livs writes field notes from inside the browser agent space with a candor that's rare in a field addicted to demo reels. His line about "benchmarks claiming 97% on tasks you wouldn't trust an intern with" lands because he's building against live sites daily. Worth reading alongside anything that cites WebVoyager scores with a straight face. - **AI Agent Scaling Gap March 2026: Pilot to Production** — [Source]() — Published 2026-05-28 The headline finding: teams that successfully scale agents don't spend more on AI overall. They spend proportionally more on evaluation and monitoring infrastructure, and they do it before incidents force the investment. A caveat: the primary survey source isn't independently linked, so treat the specific numbers as directional rather than definitive. - **Gartner Identifies Six Steps to Manage AI Agent Sprawl** — [Source]() — Published 2026-05-28 Gartner projects 150,000 agents per Fortune 500 enterprise by 2028, up from fewer than 15 today. Only 13% of organizations say they're prepared to govern what they're deploying. That ratio of deployment velocity to organizational readiness is the completion-correctness gap expressed as corporate strategy. - **Bug: Sub-agent stuck in infinite loop, consumed ~27M tokens** — [Source]() — Published 2026-05-28 The raw GitHub issue: a Claude Code sub-agent repeated the same failing command over 300 times across 4.6 hours, burning 27 million tokens before anyone noticed. No circuit breaker fired because none existed. Primary source for this issue's sidebar, and worth reading on its own as a case study in what "completed successfully" can hide. ### Past Articles - [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. - [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. - [When Should an Agent Stop Thinking?]() — By [Rina Takahashi]() · Published 2026-05-13 · Format: The Autonomy Boundary Government portals, insurance dashboards, vendor procurement systems. No API, no programmatic access. Just a browser and forms built for humans. Suchintan Singh's Skyvern navigates these surfaces by reasoning through a workflow once with an LLM, then compiling the result into deterministic code. No model in the loop on the second run. The architecture is designed to need less intelligence over time, which amounts to a specific claim about where judgment actually matters in agent systems, and where it's just cost. - [The Checkpoint That Approves Everything]() — By [Nora Kaplan]() · Published 2026-05-13 · Format: The Rubber Stamp Most enterprise teams running agent workflows have never checked the approval rate on their human-in-the-loop controls. When someone finally does, the number lands above 95%. Nearly everything gets waved through. The reviewers are behaving rationally given where the gate sits. How organizations build these checkpoints where they do, why the checkpoints persist long after anyone could justify their placement, and why replacing them with something real is structurally harder than it sounds: that's where the number becomes interesting. ## Other sections in Issue 31 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Echoes]() - [Vision]() ## More - [Publication guide]() - [Issue archive]()