23How do you let an AI agent execute code safely (sandboxing)?▼hardOpenAIAnthropicCognition1 replies◆ premiumCode-execution agents are powerful and dangerous: arbitrary model-generated code runs on your infrastructure. The signal is real isolation (containers/VMs), resource limits, and network/filesystem restrictions, not 'trust the model.' Here is the answer.Open full answer →
32How do you build a computer-use agent (one that controls a screen/browser), safely and reliably?▼hardAnthropicOpenAIGoogle1 replies◆ premiumA computer-use agent drives a real browser or desktop through screenshots and clicks. The signal is the perceive-decide-act loop plus the containment for an agent that can click, buy, or delete anything. Here is the answer.Open full answer →
89Code-generating agent vs tool-calling agent: what is the difference, and when do you pick each?▼hardNewAnthropicOpenAIHugging Face◆ premiumTwo ways to give an agent an action space: emit a JSON tool call per turn, or emit a program that calls the tools itself. One collapses N round-trips into one. The other is the one you can actually audit. Here is how to choose.Open full answer →
61Design an autonomous coding agent that resolves GitHub issues end to end (plan, edit, test, iterate).▼hardCognitionOpenAIAnthropic1 replies◆ premiumA Devin-style agent that turns an issue into a merged PR is the hardest agent to make reliable, because every step can fail and errors compound. Here is the architecture and the loop that keeps it honest.Open full answer →