--- title: "Practitioner's Corner" canonical: "https://current.tinyfish.ai/issue/37/practitioners-corner" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/37/practitioners-corner" issue: 37 section: "practitioners-corner" description: "Lessons from the field—what we see building at scale" authors: ["Rina Takahashi","Nora Kaplan"] published: 2026-07-12 updated: 2026-07-09 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 37 — Practitioner's Corner Lessons from the field—what we see building at scale ### The Naming Problem - [The Work That Has No Name]() — By [Rina Takahashi]() · Published 2026-07-08 · Format: The Naming Problem The conversation about agent delegation almost always starts with capability. Can the model reason? Handle ambiguity? These are real questions, but they skip past something more basic: most operational work has never been described precisely enough to hand off. A procurement specialist knows which line items are recurring rent and which are one-time logistics. She carries that knowledge without formalizing it, because no one ever asked her to. The delegation target is what creates the demand for naming. And naming turns out to be its own kind of hard. ### Builder Profile - [Alexandre Drouin and the Measurement Problem]() — By [Rina Takahashi]() · Published 2026-07-08 · Format: Builder Profile Browser agents recently produced enterprise research reports that read fluently, looked well-structured, and contained almost nothing useful. Insight recall: 1.11%. That result comes from a research program at ServiceNow that has been methodically exposing a problem the agent field keeps stepping over. For most enterprise tasks, nobody has defined "correct" precisely enough to know when the work is actually done right. Capability keeps advancing; the ability to verify it hasn't kept pace. ### The Workflow Cartographer - [tracker\_v7\_FINAL\_real.xlsx and the Analyst Who Makes Organizations Honest]() — Published 2026-07-08 Every enterprise that wants to deploy AI agents eventually hires someone like Nora Ledgerwood. Her actual title varies by engagement — "AI Readiness Analyst," "Process Formalization Lead," once, memorably, "Workflow Archaeologist" on a contract she wrote herself. Before she started preparing enterprise workflows for agent deployment, she spent eight years as a forensic accountant, tracing the distance between what financial records said happened and what actually happened. She describes the career change as "lateral." ### What Benchmarks Assume - [The Benchmark Is the Formalization]() — By [Rina Takahashi]() · Published 2026-07-08 Before an enterprise agent benchmark can measure anything, someone has to answer a question most organizations never have: what, exactly, does "done" look like? ### Further Reading - **The Process Mining Manifesto: What "Mineable" Actually Means** — [Source]() — Published 2026-07-08 Three fields — case, activity, timestamp — define the floor. Most enterprise workflows don't clear it. - **Imposing Rules in Process Discovery: The Knowledge That Logs Can't Carry** — [Source]() — Published 2026-07-08 Discovery algorithms reconstruct workflows from event data but systematically ignore what domain experts already know. - **Agentic AI in Practice: The Verification Problem Nobody Solved First** — [Source]() — Published 2026-07-08 Sixteen practitioners across twelve companies keep hitting the same wall: verifying agent output, not capability, blocks deployment. - **WorkArena: The Cost of Making Knowledge Work Testable** — [Source]() — Published 2026-07-08 Building a benchmark on ServiceNow surfaces how stubbornly real knowledge-worker tasks resist formalization. - **The 2025 AI Agent Index: What Thirty Agent Developers Chose Not to Share** — [Source]() — Published 2026-07-08 A survey across 30 deployed agents finds most developers disclose remarkably little about safety or evaluation. - **The Container Fallacy: When Logging Everything Explains Nothing** — [Source]() — Published 2026-07-08 Evidence can exist everywhere and still fail to answer the specific governance question someone actually needs answered. ### Past Articles - [The Dispute File Is the Spec]() — By [Rina Takahashi]() · Published 2026-07-02 · Format: Core Idea Piece When a cardholder calls their bank, it doesn't matter that the agent completed the purchase correctly. The file decides everything: the evidence a merchant must produce to defend the transaction. Visa and Mastercard have spent decades codifying what a defensible action looks like. Now that machinery applies to agent-mediated payments. And the questions it asks about authorization, scope, and remedy are indifferent to reasoning quality or benchmark scores. They ask whether what happened can survive challenge. Most agent systems aren't producing the artifacts that would let them answer. - [Shunyu Yao Taught Agents to Reason, Then Learned to Stop Trusting the Transcript]() — By [Rina Takahashi]() · Published 2026-07-02 · Format: Builder Profile In 2022, Shunyu Yao gave agents a grammar for reasoning. ReAct became the template: think, act, observe, repeat. The reasoning trace was the proof. Three years later, Yao built tau-bench, which ignores the trace entirely and checks the database. GPT-4o completes retail tasks about 61% of the time on a single attempt. Require it to succeed consistently across eight runs, and the number drops below 25%. The person who taught agents to show their work now measures how much distance sits between a convincing transcript and a correct outcome. - [What WorkArena's Task Design Makes Visible]() — By [Nora Kaplan]() · Published 2026-06-24 · Format: Builder Profile One benchmark task asks an agent to create a hardware asset in ServiceNow. The agent fills in fields, clicks submit, reaches a confirmation screen. WorkArena ignores the confirmation screen entirely. Its validator queries the database directly, checking whether saved values match the spec. An agent that navigated perfectly but corrupted data underneath would look like a success to anyone watching. That design choice, buried in validation code, surfaces a problem anyone deploying agents inside enterprise systems will eventually have to solve. - [What a Click Carries]() — By [Rina Takahashi]() · Published 2026-06-24 · Format: Central Idea Piece When a person clicks a button on a webpage, the infrastructure only sees the click. It never sees the reading, the context, the judgment, the acceptance of responsibility that preceded it. It never had to. The entire web assumed a human was present, so thoroughly that nobody wrote it down. Now agents produce identical HTTP requests without any of that invisible context. The sessions still fire. But the click has stopped carrying what every downstream system quietly depended on it to carry. ## Other sections in Issue 37 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Echoes]() - [Vision]() ## More - [Publication guide]() - [Issue archive]()