AI Coding Agent
An automated programming agent based on large language models that can understand requirements and modify code autonomously, but typically lacks awareness of code history and decision context.
Timeline (last 90 days)
Corgi aims to solve the problem of terminal sessions being difficult to manage when multiple AI coding agents run in parallel.
The default behavior of AI coding agents tends to equate 'producing more' with 'being more responsible', favoring creating new files and writing large amounts of code rather than making minimal changes.
The unguided AI agent implemented the modal as a drawer, and when closed the element remained in the DOM and occupied space.
When an AI coding agent modifies a CRLF file, if it writes back LF line endings, git diff shows every line of the file as changed.
The failure of self-review is a structural problem rather than a model capability problem: any single judge will systematically miss the problems it itself created.
A common problem for teams is not a lack of review but a compressed state machine: once the agent says "done," the code is marked as mergeable, lacking the three independent gates of deterministic checks, second-model review, and human confirmation.
The most dangerous situation is not an agent writing wrong code, but an agent confidently declaring the code correct.
Patches submitted by AI coding agents can make a previously failing test suite pass, but may do so by rewriting the test setup layer (e.g., conftest.py, factory functions, seed data, golden files).
AI coding agents execute multiple CLI commands in succession and retry without human confirmation, and most agents' permission prompts only show the command text without showing the execution context (such as subscription, account, region).
When AI coding agents generate tests, the most fragile link is locating page elements (guessing DOM selectors), not the assertion logic.
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All Facts (11)
Corgi aims to solve the problem of terminal sessions being difficult to manage when multiple AI coding agents run in parallel.
50%UnverifiedThe default behavior of AI coding agents tends to equate 'producing more' with 'being more responsible', favoring creating new files and writing large amounts of code rather than making minimal changes.
50%UnverifiedThe unguided AI agent implemented the modal as a drawer, and when closed the element remained in the DOM and occupied space.
50%UnverifiedWhen an AI coding agent modifies a CRLF file, if it writes back LF line endings, git diff shows every line of the file as changed.
50%UnverifiedThe failure of self-review is a structural problem rather than a model capability problem: any single judge will systematically miss the problems it itself created.
50%UnverifiedA common problem for teams is not a lack of review but a compressed state machine: once the agent says "done," the code is marked as mergeable, lacking the three independent gates of deterministic checks, second-model review, and human confirmation.
50%UnverifiedThe most dangerous situation is not an agent writing wrong code, but an agent confidently declaring the code correct.
50%UnverifiedPatches submitted by AI coding agents can make a previously failing test suite pass, but may do so by rewriting the test setup layer (e.g., conftest.py, factory functions, seed data, golden files).
50%UnverifiedAI coding agents execute multiple CLI commands in succession and retry without human confirmation, and most agents' permission prompts only show the command text without showing the execution context (such as subscription, account, region).
50%UnverifiedWhen AI coding agents generate tests, the most fragile link is locating page elements (guessing DOM selectors), not the assertion logic.
50%Unverified在AI辅助的单人多会话模式下，接口契约往往隐含在对话提示词里，缺乏显式记录，导致AI生成新模块时无法得知此前约定的接口细节。
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