--- title: "Practitioner's Corner" canonical: "https://current.tinyfish.ai/issue/29/practitioners-corner" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/29/practitioners-corner" issue: 29 section: "practitioners-corner" description: "Lessons from the field—what we see building at scale" authors: ["Nora Kaplan","Rina Takahashi"] published: 2026-05-18 updated: 2026-05-14 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 29 — Practitioner's Corner Lessons from the field—what we see building at scale ### The Rubber Stamp - [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. ### The Autonomy Boundary - [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. ### Three Hundred Approvals - [Bev Okoro Approves 340 Items on a Tuesday]() — Published 2026-05-13 *Bev Okoro is a Procurement Operations Lead at a mid-market packaging and fulfillment company in the Midwest. She oversees agent-generated purchasing decisions — shipping routes, vendor payments, inventory reorders — approving upwards of 300 per day. Before operations, she spent six years as an EMT. She agreed to this conversation on the condition that we note she is, in her words, "a composite of every ops person who has ever stared at an approval queue at 3 PM and felt their soul leave their body." We have honored this request.* ### The Counter-Example - [BakerHostetler's ROSS Deployment: A Checkpoint Placed Where It Counts]() — Published 2026-05-13 BakerHostetler pointed ROSS Intelligence at 27 terabytes of bankruptcy filings and told it to surface citations, retrieve documents, and identify precedent. Attorneys kept one job: deciding what the research meant. ### Further Reading - **The Agentic Enterprise: Where Should Humans Stay in the Loop?** — [Source]() — Published 2026-05-13 The sharpest move here is the distinction between deliberate and reflexive checkpoints, and the diagnostic that follows: if your approval rate sits above 95%, the human in your loop has become decorative. That number should unsettle anyone building oversight into agent workflows, because it implies most checkpoints degrade into confirmation theater the moment they encounter real operational volume. The piece gives you a framework for asking where human judgment actually changes outcomes versus where it just adds latency and a false sense of control. - **Enforcing Human-in-the-Loop Controls for AI Agents** — [Source]() — Published 2026-05-13 Where the Board of Innovation piece asks where to place checkpoints, Prefactor asks what happens at the checkpoint itself. The practical controls perspective reveals a problem that policy-level thinking tends to skip: a well-placed approval gate still fails when the interface starves the reviewer of the context needed to make a real decision. Worth reading alongside the Board of Innovation piece as a reminder that "human in the loop" describes a design surface, not a solution. - **AI Reliability Is a Decade-Old Problem — And We're Still Only Solving Half of It** — [Source]() — Published 2026-05-13 The compound failure math here is the piece's real contribution: a 95% step-level success rate across a 10-step agent workflow yields roughly 60% end-to-end reliability. That arithmetic turns oversight from a governance preference into a structural necessity. It also explains why agent failures cluster in production rather than demos, since demo-length workflows never accumulate enough steps to surface the compounding. - **The 5 AI Agent Failure Modes: Why They Fail in Production** — [Source]() — Published 2026-05-13 A taxonomy built from 591 production incidents, and the headline finding is striking: 88% of failures traced to infrastructure gaps rather than model errors. That ratio matters for oversight design because it suggests most human-in-the-loop systems are watching the wrong layer. If your checkpoint is designed to catch the model doing something wrong, you're auditing the component responsible for roughly one in eight failures. - **EU AI Act Article 14: Human Oversight Requirements** — [Source]() — Published 2026-05-13 With binding enforcement arriving in August 2026, Article 14 transforms "meaningful human oversight" from an engineering best practice into a compliance obligation with real consequences. The legal text is worth reading directly because its requirements, particularly around the ability to "correctly interpret the high-risk AI system's output," presuppose a sensing capability that most deployed agent systems simply do not have. The gap between what Article 14 requires and what current oversight tooling delivers is about to become very expensive. ## Other sections in Issue 29 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Echoes]() - [Vision]() ## More - [Publication guide]() - [Issue archive]()