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The developer asked for the app to be optimised for iPhone Duo, a foldable newer than the model's training data. In 40 minutes the agent copied the design tokens and drew 10 screens for both displays. How it researched the device, and what's still open.
In fifteen minutes the agent moved 90 notes out of its personal memory and into skills, docs and code comments, and the memory index dropped from 15.5 KB to 5.7 KB. Part 2 of 2: where each kind of fact lives now, what stayed in memory, and the rule that keeps it small.
Three times in twelve days, our agent shortened every line of its memory index to get back under the size limit, and it never removed a single entry. Then the developer asked for a fix that would last. Part 1 of 2: why trimming fails, and what we found when we sorted the index.
The agent did most of the typing. The developer did the parts that decide whether the product is right. The corrections, the prompting style that emerged, and why we built an apprentice mode.
We ran agents in auto-permission mode for months. The guardrails that mattered: a classifier that blocked credential hunting and shared-environment writes even after we said yes, secret hygiene in every command, and humans holding the keys.
Agents open the pull requests; a human merges them. One worktree per change, a fixed cleanup ritual, feature flags per environment, and release scripts that resolve conflicts before they happen.
We used surprisingly few subagents. Long-lived peer sessions, briefed like contractors, did the heavy lifting, while cheaper-model subagents ran QA behind a write guard. What worked, what stalled, and what it cost the human.
In one month an agent drew hundreds of screens through the Figma MCP. What made it reliable: requirements split into rule files, two-pass design, a craft rulebook, hide-don't-delete, and checking every change by looking at it.
Green unit tests are not a launched app. How we split mobile device debugging between a human who can tap a screen and an agent that can read a 5 MB log in milliseconds.
'Build green, runtime launch unverified' turned out to be one of the most valuable lines an agent wrote all month. Tests first with proof they fail, live API probes with safety rules, Checked / Not checked reports, and bugs that only exist in the agent's shell.
The tracker sat behind the client's VPN and its tickets contained personal data. How we built a redacted local mirror agents could read, the triage loop on top of it, and a monthly client report generated from tickets and commits.
Our developer cleared the agent's context close to 300 times in a month, and the agent never lost the plot. What we keep in agent memory, how each note is written, and why memory needs regular pruning.
We wrote about 120 specs and 115 plans before deciding most changes didn't need one. What heavy process bought us, what replaced it, and how we decide how much process a change gets.
Our CLAUDE.md files read like incident logs, on purpose. How we structure agent instructions, why each rule carries the incident behind it, and the standing order that keeps the rules growing.
Five repositories, two mobile apps, a web portal and a year of agent-assisted delivery. The phases we went through, the numbers behind them, and why the harness mattered more than any single model.
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