Run, build, and govern AI agent workflows for DevOps

Hacker News

Hey HN, Two things kept coming up when we talked to teams building internal AI automation: rebuilding the same integrations, and automation touching production with no visibility into what it actually did. So we built Retroshift, run and govern AI agent workflows in your existing tools. Every action is logged, nothing touches production without approval, and we use a local first approach so everything can be run straight on your local machine Things we're actively working on: - Shadow execution: test workflows against mocked dependencies before hitting production - Import runbooks into workflows - Trace to workflow: convert a chat session into a reusable workflow - Workflow marketplace - Evals - Slack integration Invite-only alpha now, beta next week. Would love feedback from anyone running ops automation or starting to think about AI agents supporting production systems.

Visit product View on Hacker NewsMar 27, 2026

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

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

50%50% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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