Coding agents supercharge Murphy's Law

AI coding agents can create unpredictable demand on software services by discovering and automating against endpoints, SDK versions, and documentation that were not intended for broad or automated use. Drawing on experiences during GitHub’s outage and from the author’s own service, the account describes agents using undocumented but publicly visible API endpoints, generating traffic to deprecated endpoints from stale training data or outdated SDKs, and enabling opt-in features found through unlinked documentation pages. These behaviors can increase latency, monitoring load, error rates, and incident-response noise, even when the users or agents are not acting maliciously. While the author does not attribute GitHub’s reliability problems directly to AI agents, they argue that larger platforms with extensive historical API and documentation surfaces may face amplified operational risks, making it important to design APIs and tooling for increasingly agent-driven usage.

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