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●2.1.301 — Adds the vim % motion, LaTeX math rendering in the VS Code panel, and a retry-watchdog limit for usage-limit waits (10/15)●2.1.299 — Conversations over 200K tokens left idle about 54 minutes get compacted; MCP allow rules now match only the server they name●SONNET 4.5 — Retires on the Claude API on 11/30, 45 days left. Migrate to Sonnet 5.5●WINDOWS — Reports say hooks break silently only on Japanese-locale Windows; check the character encoding first●FAST MODE — Opus 5.5 fast mode is out of research preview and no longer needs the beta header (10/15)●NEW — I Put a Ten-Line Promise File at the To…●2.1.301 — Adds the vim % motion, LaTeX math rendering in the VS Code panel, and a retry-watchdog limit for usage-limit waits (10/15)●2.1.299 — Conversations over 200K tokens left idle about 54 minutes get compacted; MCP allow rules now match only the server they name●SONNET 4.5 — Retires on the Claude API on 11/30, 45 days left. Migrate to Sonnet 5.5●WINDOWS — Reports say hooks break silently only on Japanese-locale Windows; check the character encoding first●FAST MODE — Opus 5.5 fast mode is out of research preview and no longer needs the beta header (10/15)●NEW — I Put a Ten-Line Promise File at the To…
Articles/API & SDK
⬡ API & SDK/2026-06-03Advanced

An Anti-Corruption Layer for Claude API Models — Keeping Generation Changes Out of Your Business Logic

Hard-coding model strings into business logic means production breaks quietly every time a generation is retired. Here is an anti-corruption layer that separates logical roles from physical model IDs, with working TypeScript and Python, migration costs, and the judgment calls behind it.

claude-api83architecture10anti-corruption-layermodel-migration5production111design5

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In the spring of 2026, when I received the email announcing that the legacy Haiku 3 model strings were entering deprecation, the first thing I opened was not the API dashboard but a grep across my own app source. The string claude-3-haiku was sitting, hard-coded, in twelve places: the review summarizer of a wallpaper app, the daily push copy of a manifestation app, the auto-draft generator for a personal blog. Every one of them was code I had written years ago meaning only to "get it working," and it had quietly stayed in production ever since.

I have been building apps solo under the name Dolice since 2014, twelve years now, running a portfolio that has passed 50 million cumulative downloads. Once your AdMob revenue reaches a meaningful scale, the scariest failure is the silent one. If something throws, you notice. But a model retirement arrives wearing a different face: responses drift gradually from some day onward, or a 400 starts coming back the moment a deadline passes. What this moment drove home was that pulling a model name — an identifier that changes at an external vendor's convenience — into the inside of my own business logic was itself a design debt.

This article is about paying down that debt with an anti-corruption layer. I take this pattern from domain-driven design, apply it to the Claude API, and show a concrete implementation that separates logical roles from physical model IDs, in working TypeScript and Python.

What "quiet corruption" really is

The real problem with hard-coding model names into business code is not that grep finds twelve hits. If that were all, a single replace would fix it. The deeper problem is that each call site carries the model's implicit assumptions along with the string.

The review-summary code, for example, was written on the assumption that "Haiku is fast but weaker at following long instructions, so keep the prompt short." The blog-draft code, meanwhile, assumed "a little slower is fine because I want quality, so set max_tokens generously." Behind the very same claude-3-haiku string, the expectations differed at every call site.

Run a naive find-and-replace in that state and you drop a high-quality model into latency-sensitive code and blow the latency budget, or drop a lightweight model into quality-sensitive code and watch the output degrade. The model string, in other words, was never just an identifier — it was a symbol that compressed several non-functional requirements: speed, quality, cost, context length. What the anti-corruption layer needs to isolate is exactly that compressed bundle of assumptions.

Designing logical roles as a "translation vocabulary"

At the center of the anti-corruption layer sits a dictionary that translates between the business vocabulary and the API vocabulary. I call the business side "logical roles." The app code knows nothing about physical model names; it requests only the property it actually wants, expressed as a role.

Across my portfolio, collapsing everything into these three roles turned out to be neither too many nor too few.

  1. fast — lightweight tasks that run synchronously with a user action. Latency first. Review-summary previews, input assistance.
  2. balanced — tasks that run asynchronously but whose quality I will see the same day. Push copy generation, tagging.
  3. deep — high-quality tasks that a human will definitely review afterward. Blog drafts, long-form outlines.

The crucial point is that these role names are cut by the nature of the use case, not by a performance ranking like "fast vs. smart." It never happens that "fast eventually wants to be upgraded to deep." fast simply means "a place where I do not want to keep the user waiting," forever. No matter how much smarter the physical model becomes, that boundary stays stable. The reason this abstraction does not rot over long-term operation is that I placed its axis on requirements, not on performance.

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WHAT YOU'LL LEARN
✦A ModelRegistry in both TypeScript and Python that separates logical roles (fast / balanced / deep) from physical model IDs, shrinking a generation change to three lines in one file
✦The real-world rescue of a 50M-download app portfolio from 12 hard-coded model strings during the Haiku 3 retirement and the Sonnet 4.5 1M-context migration, cutting migration effort by 80%
✦A capability contract test that notices a generation change before the retirement date, plus fallback-aware token-cost recalculation
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