Routine‑Powered Performance — Structured Planning for Agentic Marketing
tl;dr — Routine - LLM agent planning for enterprise enforces step‑by‑step, parameter‑passing workflows that cut tool‑call errors by >30 pts. This guide shows media teams how to clone that rigor for search, programmatic, social and e‑commerce pipelines.
📑 Quick Map
- Why Structured Agents Beat Prompt Soup
- Routine in 90 Seconds
- Channel Workflows & Sample Plans
- Open‑Source Build Stack
- DIY Implementation Guide
- Guard‑Rails & RL Metrics
- Next Steps
- Sources
## 🎯 Why Structured Agents Matter
| Problem in the Wild | Impact on Marketing Ops | Routine‑Style Fix |
|---|---|---|
| Plan hallucination → skipped tools | CPA spikes, missed bids | Immutable JSON plan listing all steps |
| Context bleed between sub‑tasks | Wrong SKU, wrong budget | Typed parameter passing output → input |
| Opaque execution path | Hard to audit & debug | Separate Planner & Executor logs |
| LLM cost for every run | Prohibitive at scale | Distill to cheap 7 B model after plan bank |
🔧 Routine 101 — How It Works
- Planner (Large LLM) — outputs array of
{step, tool, inputs, outputs}. - Executor (Code or small LLM) — reads plan, calls tools, pipes outputs.
- Distiller — fine‑tunes 7 B model on successful plan ↔ execution pairs.
Result: GPT‑4o accuracy on 5‑step enterprise tasks ↑ 34 pts; Qwen3‑14B ↑ 51 pts.
📊 Channel‑by‑Channel Recipes
🔍 Search Bid Guardian
Purpose
Keep paid-search spend efficient by nudging bids down whenever yesterday’s ROAS < 3 (or any threshold you set).
Daily Flow
- Pull GA4 ROAS for each campaign in the last 24 h.
- Compare to threshold (3× by default).
- If ROAS is low → lower bids –20 % via Google Ads API.
- Log every decision with timestamp, old bid, new bid for audit & RL training.
Why it matters
Stops slow bleed on under-performers before Finance sees the invoice; simple guardrail that pays for itself in the first week.
Expand ideas
- add an upper guard to raise bids when ROAS > 6.
- pipe the “flag” event to DV360 audiences as 1P signal (poor search fit → push awareness).
📈 DV360 Deal Optimiser
Purpose
Maintain healthy CPMs on programmatic private deals.
Every 6 h
- Stats Pull – fetch 7-day average CPM for each deal.
- ΔCPM Calc – compare to your baseline in BigQuery or other data store.
- If CPM inflated > 15 % → patch bid down (–10 %).
- Log to BigQuery – stores before/after + deal metadata for off-policy eval.
KPI lift
Shaves excess CPM without pausing delivery; BigQuery log lets data-science teams back-test different thresholds with RL.
Next upgrades
- parallel path to raise bids when CPM plunges (win more cheap inventory).
- connect to a creative-quality checker to ensure brand safety on cheap impressions.
📣 Meta Fatigue Swapper
Purpose
Prevent audience burnout and keep creatives fresh inside Meta ads.
Hourly
- Pull Ad-set Metrics – freq & remaining audience size.
- Fatigue Check – flag if frequency > 6 OR audience < 50 k.
- Rotate Creative – swap in the next asset in the queue (or request a new render from your GenAI creative agent).
- Abort if brand-safety labels fail (critic layer).
Business impact
Less creative decay → stable CTR & lower CPA.
Teams spend time on high-concept ideation, not manual swaps.
Suggested extensions
- integrate sentiment scoring to swap sooner on negative reactions.
- multi-arm bandit that increases spend on best-performing new creatives.
🛒 AMC Look-Alike Builder
Purpose
Turn SKU-level purchasers into fresh, high-propensity audiences inside Amazon’s Marketing Cloud (AMC) & Ads console.
Weekly
- Query AMC – collect 30-day purchasers for each target SKU.
- Create Look-Alike – size 2× seed audience.
- Export to Ads console, ready for Sponsored Products / DSP campaigns.
Why marketers love it
Hands-off pipeline that fuels always-green remarketing / cross-sell pools; zero manual SQL.
Level-up ideas
- build “cold-start” variant: use product-detail-page dwellers when purchases are < 100.
- feed back conversion results → refine seed quality (reward loop).
🛠️ Open‑Source Stack
| Layer | Pick 1‑2 Tools | Notes |
|---|---|---|
| Workflow Graph | Mastra (TypeScript) • LangGraph | Explicit nodes, strong typing |
| Planner LLM | GPT‑4o • Mixtral‑8x22B | Planner only runs once per job |
| Executor | Python micro‑service • 7 B distilled Llama | Cheap, repeatable |
| RL & OPE | Ray RLlib • TF‑Agents | Doubly‑Robust value estimates |
| Observability | Langfuse • OpenTelemetry | Token & cost tracking |
🚀 DIY Plan (Sprint 0‑1)
- List 3 pain‑point playbooks (e.g., daily ROAS guard, creative fatigue, feed gaps).
- Draft plain‑English steps — verbs + tools, max 7 steps each.
- Prompt Planner → “Return strict JSON plan with typed params”.
- Code Executor — iterate until 95 % plan success.
- Log & Distill small model for cheap hourly runs.
- Add Reflexion Critic — veto plans missing mandatory KPIs.
Time‑to‑MVP: 2 weeks.
🛡️ Guard‑Rails & Metrics
| Risk | Mitigation | KPI |
|---|---|---|
| Bid overshoot | Bound updateBid ±25 % | ΔCPC |
| Off‑brand copy | Tone‑checker critic before publish | Violation rate |
| Data leakage | Use short‑lived tokens, mask PII | PII incidents |
| RL reward hacking | Hold‑out slice + OPE (CV, IPS) | Trust gap |
Next Steps
- Clone our Mastra starter repo (coming Q3) or adapt snippets above in LangGraph.
- Join the All‑Hands Reading Club — first session covers Routine & Plan‑and‑Act patterns.
- Vote on the next build‑in‑public mini‑hack in our Teams channel.
📚 Sources & Deeper Reads
- Routine — https://arxiv.org/pdf/2507.14447
- Plan‑and‑Act — https://arxiv.org/html/2503.09572v2
- Anthropic — “Best Practices for Tool‑Using Agents” (2024)
- Mastra Docs — https://mastra.ai/docs
Prepared by Performics Labs — translating frontier AI into actionable marketing playbooks.