Routine-Powered Performance — Building Structured, Multi-Step AI Agents for Marketing
31 Jul 2025 • Facilitator: Performics Labs Newsroom
💡 TL;DR — Routine shows that explicit JSON plans + clean parameter passing slash tool-call errors and unlock enterprise-grade LLM agents. Use this guide to turn the paper into a working marketing framework.
Goal: ship a Routine-style agent that auto-pauses poor-ROAS ad sets on Meta in under 14 days.
Step | What to Deliver | Hints |
---|---|---|
1 | Fork the starter repo | Uses Mastra TS + LangGraph |
2 | Draft JSON plan (max 5 steps) | See Figure 3 in the paper |
3 | Implement Executor & Meta Marketing API calls | Re-use our typed wrappers |
4 | Log state→action→reward tuples | Ray RLlib buffer template included |
5 | Record a 90-sec Loom demo & share in comments | Top 3 voted demos get featured |
Join the Routine × Mastra Hackathon → · Register on Luma ↗
Timeline
• Submit by 8 Aug • Demo Day 11 Aug
Starter templates & helper code already in the repo — just fork and build!
🚀 Ready? Grab the repo, join the chat, and let’s build the next generation of performance agents — together.
Up-vote next week’s build idea by reacting with 👍 to any comment.
Published on Thursday, July 31, 2025