Autonomous multi-agent DAG pipeline
A 7-node Directed Acyclic Graph generating localized product content for 11 enterprise clients across 5 countries. Human-supervised autonomous execution. Deterministic validation fires first; the LLM activates only if the gate passes.
Problem
Enterprise clients required localized, SEO-compliant product pages across dozens of cities. Manual creation is mathematically impossible at ~10.5M product-location combinations per cycle.
Approach
A fail-closed 7-node DAG where every generative node is constrained by a dedicated RAG policy index (Google SPAM policies + E-E-A-T standards). 80% of compute is deterministic; only 20% is probabilistic. Every node boundary is audited by a JIT integrity layer.
Result
400K+ impressions across 3 client properties, 68.9% average pass rate per boundary, $0.0006/PDP end-to-end. 234 managed websites across 5 countries.
Why this exists
Multi-location retailers need unique, localized product pages for every item in every city they serve. At enterprise scale, this means millions to billions of pages — each requiring localized copy, structured data, and policy-compliant content. Manual creation doesn’t get close.
Validated with SEO leadership at retailers operating 10–10,000+ store locations. The content gap is six orders of magnitude beyond manual capacity.
No CMS integration. No developer hours. No API setup from the client. Operates as an external, headless discovery layer that activates with a single switch.
Every node boundary is audited by a deterministic quality gate. Content below threshold is rejected before reaching production — fail-closed by design.
Verified evidence — Google Search Console
Aggregate results across 3 active client properties — verified via Google Search Console.
Hockey-stick from 0 → 99.9K in ~60 days. Autonomous pipeline output.
1.1% CTR, avg position 10.9. Zero manual content creation.
Steep growth curve from 0 → 130K. Largest property by page volume.
0.9% CTR, avg position 8.7. Highest impression volume.
Rapid ramp from 0 → 30.2K. Newest property in pipeline.
2.3% CTR, avg position 8.7. Highest-converting property.
Live demo
End-to-end pipeline experience — the self-serve interface for autonomous content generation with human-supervised quality gates
Engineering trade-offs
Quality vs. cost
O-R-A-V (Observe→Reason→Act→Validate) deterministic validation engine. Node 6 runs zero-LLM rule-based checks; Node 7 (DEMAS JIT) runs SLM-as-judge evaluation inline within content generation.
Decision: deterministic + SLM validation, 68.9% average pass rate per boundary.
Safety vs. throughput
Fail-closed design means ~31% of content is rejected. Intentional — no partial or low-quality content ever propagates downstream to production surfaces.
Decision: fail-closed at every boundary, zero tolerance.
Self-improvement
RLAIF data flywheel. DPO preference pairs generated from Node 6/7 evaluation signals for continuous model alignment.
Decision: closed-loop flywheel, autonomous preference pairs.
Scale vs. locality
5 countries require different linguistic, cultural, and regulatory context. Per-locale context injection increases prompt engineering complexity but eliminates fine-tuning per market.
Decision: context-first architecture, zero-shot locale adaptation.
Deterministic gates before probabilistic agents is non-negotiable. 80% of every cycle is rule-based; only 20% is inference. That ratio is the difference between an autonomous system and an unsupervised one.
The pipeline’s writer node runs Gemma 4 26B-A4B MoE on a single A100. Standing that up took 30+ deployment iterations and 20 named failure modes. Case Study 02 documents every one.