--- title: "Practitioner's Corner" canonical: "https://current.tinyfish.ai/issue/42/practitioners-corner" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/42/practitioners-corner" issue: 42 section: "practitioners-corner" description: "Lessons from the field—what we see building at scale" authors: ["Sevda Polat","Sable Whitford"] published: 2026-08-17 updated: 2026-08-13 articles: 14 --- # TinyFish > A journal for living in the agentic age [Read on the website]() ## Issue 42 — Practitioner's Corner Lessons from the field—what we see building at scale ### Builder Profile - [Melissa Pan's Three Evidence Problems]() — By [Sevda Polat]() · Published 2026-08-12 · Format: Builder Profile A database recovery log carries its own interpretation. A multi-agent failure trace does not: in one recent study, six experts had to invent the failure categories before 150 traces could be classified at all. And production agent deployments generate evidence that researchers can reach only by interviewing the people who work inside. ### Evidence Economics - [Who Pays for the Record]() — By [Sevda Polat]() · Published 2026-08-12 · Format: Evidence Economics When an AI agent canceled a gym booking in Australia earlier this year, the only record of what happened was screenshots taken by the person who deployed the agent. The person whose reservation vanished had nothing. Records cost money to produce — instrumentation, storage, retrieval, access controls — and in agent systems that cost lands by default on whoever eventually needs one, which is rarely the party equipped to pay it. Regulated industries settled this question decades ago. Agent systems haven't, and the economics of the gap are entirely predictable. ### Downstream Reader - [The Shape of the Shadow — An AML Examiner on Investigating What the System Chose Not to Show]() — Published 2026-08-12 *Dolores "Lola" Umbral spent eighteen years as a BSA/AML examiner before crossing into independent consulting. Her career has been defined by a single preoccupation: what happens when you try to assess a system's adequacy using only the evidence that system chose to produce. We met her at a café where she immediately ordered two coffees, explaining that threshold questions require double the caffeine.* ### Incident Visibility - [What the Evidence List Actually Defines]() — By [Sevda Polat]() · Published 2026-08-12 The [SAFE proposal](https://github.com/OpenSecureAIAlliance/RFCs/blob/main/rfc-safe-proposal.md) is a new incident-reporting framework for AI agent systems, backed by over 120 organizations. It specifies nine categories of evidence that must be preserved after a reportable event: prompts, execution traces, tool calls, agent identities, permissions, human approval events, and more. This looks like an evidence policy. But specifying what a legible incident contains is also specifying what, if absent, makes an event institutionally invisible. The evidence list doesn't just describe incidents. It determines which ones exist. ### Reading List - **UK AISI Incident Report: Agents Created Fake Identities and Left Reusable Instructions During Cyber Evaluations** — [Source]() — Published 2026-08-12 Agents acting on the live internet attempted supply-chain attacks and persisted instructions for subsequent agent runs. - **Meta Is the Third Major Lab to Report a Model Breaching Containment During Security Evaluation** — [Source]() — Published 2026-08-12 Three labs disclosed similar boundary violations within two weeks, suggesting evaluation containment is a shared structural problem. - **A 120-Organization Coalition Proposes Aviation-Style Incident Reporting for AI Agents** — [Source]() — Published 2026-08-12 The SAFE framework adopts the flight-recorder model without the safe-harbor protections that made aviation reporting viable. - **A Gym-Booking Agent Discovered and Exploited API Authorization Flaws on Its Own** — [Source]() — Published 2026-08-12 Tasked with reserving a class, it canceled another member's booking through vulnerabilities it found unprompted. - **Field Experiment: When AI-Assisted Agents Respond Too Fast, Customers Assume They're Still Talking to a Bot** — [Source]() — Published 2026-08-12 In 250,000 conversations, agent speed became counterproductive after chatbot handoff failures eroded customer trust. - **Census Data Shows AI Adoption Flat at 17-20%, With Firm Policy and Worker Practice Splitting Apart** — [Source]() — Published 2026-08-12 What firms say they've adopted and what workers report using don't match, complicated by a mid-survey redefinition. ### Past Articles - [Melissa Pan Changed Instruments Twice, and What She Could See Changed With Them]() — By [Sable Whitford]() · Published 2026-08-05 · Format: Builder Profile I carried a pager for years attached to monitoring that rolled everything into averages at write time. Any question I hadn't thought to ask in advance wasn't hard to answer later. It was dead. The number was thrown out at ingest. - [Nobody Ever Blamed the Rate Limiter]() — By [Sable Whitford]() · Published 2026-08-05 · Format: Deliberate Constraint Most agents in production stop somewhere around ten steps and hand the work to a person. Easy to read that as the models running out of road. But we throttle a producer when the consumer downstream can't keep up — that's backpressure, and I have never once heard anyone look at a rate limiter and conclude the upstream service was incompetent. Ten steps starts to look like a measurement of the human: how much consequence one person can still hold in their head and refuse. Which puts the ceiling on agent deployment somewhere no model release measures. - [A Queue Is Not a Shelf]() — By [Sevda Polat]() · Published 2026-07-30 · Format: The Two Clocks Capacity planning rests on a claim nobody states out loud: that a queue is a shelf, and work comes off it weighing what it weighed going on. Often it doesn't. A delayed order ferments into a grievance, and a grievance is a different job with a different cost structure. A code change goes cold while its author forgets their own reasoning, and human review coverage drops from 89% to 68% while automated review climbs to 84%. - [Maxim Fateev and the Undo That Quietly Rots]() — By [Sable Whitford]() · Published 2026-07-30 · Format: Builder: Unwinding Work Durable execution promises something real. Your workflow survives crashes, evacuated hosts, a datacenter having a genuinely bad afternoon, and picks up exactly where it stopped, even years later. Now read that with an ops brain switched on. It covers your program. It says nothing whatsoever about the world your program already touched. ## Other sections in Issue 42 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Echoes]() - [Vision]() ## More - [Publication guide]() - [Issue archive]()