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The author reviews five AI coding tools based on real-world production incidents and proposes a filter for safely integrating them into development workflows.
The author shares a two-year experience deploying AI agents for investigating production incidents across team boundaries, highlighting that while the technical implementation was straightforward, the organizational politics posed the real challenge.
This paper presents an empirical catalog of 63 confirmed LLM-agent budget overrun incidents from 21 orchestration frameworks, organized into a failure taxonomy, and introduces a Rust crate using affine type ownership to prevent token/cost budget violations at compile time rather than runtime.
A commentary contrasts a Chinese trader's $2,000 course sales based on impressive setups with an engineer's high AI adoption rate that caused 9 of 11 production incidents, highlighting the gap between perceived and actual performance with AI tools.