Agent OS – Safety-first platform for building AI agents with VS Code

Hacker News

Hi HN, I built Agent OS because I was tired of the "orchestration tax" – writing the same safety checks, memory management, and tool-handling code in every AI agent project. What it does: - Visual policy editor in VS Code (no more YAML wrestling) - Multi-model verification (GPT-4 + Claude + Gemini consensus before risky actions) - Built-in compliance frameworks (GDPR, HIPAA, SOC2, PCI-DSS) - 50+ agent templates for common use cases - One-click deployment to GitHub Actions The core idea: Instead of building orchestration on top of your code, Agent OS provides an "operating system" layer that manages permissions, file access, and self-correction. Example: Instead of manually checking if your agent's SQL query is safe, you define a policy once and Agent OS enforces it everywhere. VS Code Extension: https://marketplace.visualstudio.com/items?itemName=agent-os.agent-os-vscode Docs: https://imran-siddique.github.io/agent-os-docs/ Would love feedback on the approach, especially from anyone dealing with AI safety in production.

Visit product View on Hacker NewsJan 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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