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09-20

How Anthropic scaled test impact analysis as agentic coding broke CI

Anthropic's CI absorbed a 25x increase in jobs over six months: Claude now writes 80% of the code, per-engineer quarterly output is 8x the 2021-2025 rate, and the test suite grew 10x while headcount barely moved. The bottleneck moved from writing code to PR review to CI, landing on the test impact analysis service that picks which tests run on each change. Because v0 needed a single writer to keep per-test history ordered, it ran as one process and could not be sharded. Three patches followed: doubling cores bought 70 days, per-package sharding 29 days, and daily restarts under a day. The rewrite moved history into an in-memory data store: any listener worker appends results to a journal and exits stateless, a small consumer rolls the journal into per-test history every few seconds, and the selector queries it. One engineer finished in three weeks.

claude.com · 10 min · Agentic Coding · CI/CD · Engineering Productivity
06-23

From Prompting Agents to Loop Engineering

The AI coding community is shifting from prompting agents manually to designing loops that prompt agents for you. This is the most practical, production-oriented guide available: what an agent loop is, why it matters, and what one looks like in the real world. The author breaks down the six mandatory components (trigger, isolation, written-down context, tool reach, second-agent checker, on-disk state), then illustrates with two concrete examples: a PR babysitter that checks every 15 minutes and auto-fixes CI failures, and Claude Code's /goal command. It also covers where cost actually goes (iterations, not tokens), when not to loop (one-shot edits, unbounded exploration), and predictable failure modes (verification burden stays human, comprehension debt, silent drift).

x.com · 15 min · Agent Engineering · Agent Loop · CI/CD