Cosmic AI Workflows – Chain AI agents to automate multi-step projects

Hacker News

Hi, I'm Tony, founder of Cosmic (AI-powered headless CMS and application development platform). We kept running into the same problem: create a blog post with the help of an AI agent, use the output for another prompt to create social posts, then manually post to X, LinkedIn, Facebook. Every single time. So we built AI Workflows — chain multiple agents together and let them run autonomously, with each step receiving outputs from previous steps. Three agent types you can chain: - Code Agents: Build features in GitHub with commits and pull requests. - Content Agents: Generate CMS content with context injection from previous steps. - Computer Use Agents: Automate browser workflows and record demos. How it works: 1. Define steps with agent type, prompt, and configuration 2. Steps run sequentially or in parallel (configurable) 3. Context passes automatically between steps 4. Trigger manually, on a schedule (cron), or via CMS and API events (object.created, object.edited, etc.) 5. Add approval gates for human review before critical steps Example: Autopilot feature development: Step 1: Content Agent writes a feature spec based on user feedback Step 2: Code Agent builds the feature, creates PR, and deploys to production Step 3: Content Agent generates documentation and a changelog entry Step 4: Computer Use Agent posts update to team Slack with the PR link and preview URL Currently in beta. Would love feedback on the workflow model and what use cases you'd want to automate.

Visit product View on Hacker NewsJan 27, 2026

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

99%99% 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: created, para · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, 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: io · Missing: https docs, excited, just released

22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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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