---
title: "Practitioner's Corner"
canonical: "https://current.tinyfish.ai/issue/24/practitioners-corner"
publication: "TinyFish"
publisher: "TinyFish"
language: "en-US"
guide: "https://current.tinyfish.ai/llms.txt"
permalink: "https://current.tinyfish.ai/issue/24/practitioners-corner"
issue: 24
section: "practitioners-corner"
description: "Lessons from the field—what we see building at scale"
authors: ["Rina Takahashi"]
published: 2026-04-13
updated: 2026-04-09
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](<https://current.tinyfish.ai/issue/24/practitioners-corner>)

## Issue 24 — Practitioner's Corner

Lessons from the field—what we see building at scale

### The Self-Orienting Agent

- [Drop-Downs That Aren't Really Drop-Downs](<https://current.tinyfish.ai/issue/24/practitioners-corner/article/12825/markdown>) — By [Rina Takahashi](<https://current.tinyfish.ai/team/25/markdown>) · Published 2026-04-08 · Format: The Self-Orienting Agent

Delaware's government portal goes offline at night. A pharmacy invoice site hides its download link behind a field that looks like a drop-down but behaves like a text box. A county records office redesigns its layout without warning. None of these have APIs. Humans navigate the mess without thinking. Scripts shatter against it. Somewhere between those two facts sits a bottleneck that more model capability alone won't close. One engineer spent years watching it consume his ML pipelines before deciding to build directly against it.

### The Context Gap

- [Why Agent Failures Keep Getting Blamed on the Model](<https://current.tinyfish.ai/issue/24/practitioners-corner/article/12826/markdown>) — By [Rina Takahashi](<https://current.tinyfish.ai/team/25/markdown>) · Published 2026-04-08 · Format: The Context Gap

In surgical teams, individual surgeons hold 83% of the information they need. Anesthesiologists hold 87%. Competent people, by any measure. Yet a study of perioperative handoffs found only 27% of critical knowledge was shared across the full team. The failure lived in the gaps between skilled people, not in the skill itself.

### The Unnamed Role

- [The 340-Page Document Nobody Budgets For](<https://current.tinyfish.ai/issue/24/practitioners-corner/article/12827/markdown>) — Published 2026-04-08

*Dolores "Dee" Bridgewater spent fourteen years in hotel operations before moving to enterprise IT at a large Midwestern insurance company. She is now, by her own description, "the person who onboards the robots." Her actual title is Senior Process Analyst. Her actual job, documenting the unwritten rules of claims processing so that AI agents stop making expensive mistakes, has no title, no budget line, and no entry on the org chart. We spoke with her over video call from a conference room she'd booked under the name "Agent Training — DO NOT CANCEL," a trick she learned after facilities deleted her recurring hold three times. Dee Bridgewater is not a real person, but the role she describes is one that a growing number of enterprises have accidentally invented.*

### Reasoning Once

- [Compile to Code: The Pattern That Turns Agent Reasoning Into Reusable Institutional Memory](<https://current.tinyfish.ai/issue/24/practitioners-corner/article/12828/markdown>) — Published 2026-04-08

A workflow costing $0.50 per LLM-in-the-loop execution becomes $50,000 a month at scale. That math is why a quiet architectural pattern keeps surfacing: let the agent reason through a workflow once, compile that reasoning into deterministic Playwright code, then replay the script on every subsequent run. Think expensive thoughts exactly once.

### Further Reading

- **What Amazon Learned When It Tried to Evaluate Agents at Scale** — [Source](<https://aws.amazon.com/blogs/machine-learning/evaluating-ai-agents-real-world-lessons-from-building-agentic-systems-at-amazon/>) — Published 2026-04-08

Primary-source account of production evaluation at Amazon.

- **The Friction Isn't in the Model — It's in Everything Around It** — [Source](<https://machinelearningmastery.com/5-production-scaling-challenges-for-agentic-ai-in-2026/>) — Published 2026-04-08

Practitioner map of where production scaling actually breaks.

- **The Compounding Probability Problem Nobody Wants to Do the Math On** — [Source](<https://www.roborhythms.com/why-ai-agents-keep-failing-2026/>) — Published 2026-04-08

Simple arithmetic that reframes the entire adoption gap.

- **From Building Agents to Managing the Sprawl They Created** — [Source](<https://www.insightpartners.com/ideas/agentic-enterprise-ai-workflows/>) — Published 2026-04-08

The problem has shifted from construction to lifecycle governance.

- **Half of Enterprise Agents Can't Talk to Each Other** — [Source](<https://www.salesforce.com/news/stories/connectivity-report-announcement-2026/>) — Published 2026-04-08

Salesforce survey quantifies agent isolation with uncomfortable precision.

## Other sections in Issue 24

- [Daily Brief](<https://current.tinyfish.ai/issue/24/daily-brief/markdown>)
- [Market Pulse](<https://current.tinyfish.ai/issue/24/market-pulse/markdown>)
- [Foundations](<https://current.tinyfish.ai/issue/24/foundations/markdown>)
- [Echoes](<https://current.tinyfish.ai/issue/24/echoes/markdown>)
- [Vision](<https://current.tinyfish.ai/issue/24/vision/markdown>)

## More

- [Publication guide](<https://current.tinyfish.ai/llms.txt>)
- [Issue archive](<https://current.tinyfish.ai/issues/markdown>)
