@h100envy: This paper completely changed how I think about an autonomous engineer agent: Give the agent an interface, not bash -> …

This paper completely changed how I think about an autonomous engineer agent: Give the agent an interface, not bash -> Navigate the repo -> Edit files through agent commands -> Run tests -> Feed environment feedback back into the loop Here is the 5-step blueprint: ACI: the agent gets not a raw terminal but a purpose-built agent-computer interface with commands fit to its needs, the way a human gets an IDE. Navigation: file and repository-wide search commands replace blind grep, so the agent finds the right spot without wandering. Editing: file edits go through agent commands with a bounded window, instead of dumping whole files. Guardrails: a linter blocks a syntactically broken edit before it applies, cutting a whole class of silent agent errors. Compact feedback: environment responses are kept short and informative, so context is not flooded and the loop stays clean. Key insight: the ceiling on agentic coding is set not by the model but by the interface it touches code through; design the ACI and the same model jumps higher. On SWE-bench, SWE-agent reaches 12.5% pass@1 and 87.7% on HumanEvalFix, both state-of-the-art against non-interactive LMs. Read this, then check the article below.