AI

Commerce AI Fragmentation Benchmark

Micro-Agent Pipelines vs. Monolithic LLM Orchestration

Topology Model Fragmented Multi-Agent

6 decoupled specialized micro-models (Embedding, Q-allocator, Edge GBDT, 8B Quantized Reasoner) routing tasks sub-second.

Workload Simulation 1,200 req/s
100 - 5,000 /s
450k SKUs
Chaos & Failure Injection
Stage 2: Dynamic Catalog Ranking Fine-Tuned Ranker
P95 Latency: 68 ms
Token/Op Cost: $0.00042
Fallback Strategy: Cached Vector HNSW
Status: Healthy (p95 Bottleneck)

Scores 450k SKU candidate pools with multi-objective business relevance before inventory and margin filters.

p95 Latency
184 ms
-82% vs Monolith (1020ms)
Cost / 10k Sessions
$14.80
-78% compute savings
Conversion Lift
+3.4%
Sub-200ms latency impact
Availability SLA
99.98%
Bottleneck: Catalog Rank
Commerce Orchestration Graph (Click node to inspect stage)
Edge / Micro Agentic 8B Degraded
p95 Latency Waterfall 184 ms Total
Economics & Isolation Breakdown 10k Transactions
Prompt & Embedding Tokens 2.1M tokens ($2.40)
Edge Classifier Inferences 20.0k ops ($0.80)
Agentic Reasoning Calls (8B) 10.0k ops ($11.60)
PII / Payment Data Boundary Strict PCI-DSS Edge Isolation
Architectural Insight: Decoupled routing isolates fraud and pricing to lightweight models, preventing monolithic 70B token lockup on high concurrency.
184 14.80 3.4 99.98 Dynamic Catalog Ranking
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