Play Browser · 101 plays
Solution plays
Production-grade FAI Protocol plays. Each ships with a manifest, infrastructure code, primitives, and an evaluation suite. All MIT.
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- 01-enterprise-raglargeOptimize config values
Optimize config values — chunking params, search weights, model routing, caching, evaluation thresholds
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 03-deterministic-agentsmallOptimize confidence thresholds, abstention rates, latency, model routing, caching
Optimize confidence thresholds, abstention rates, latency, model routing, caching
- primitives
- 10
- waf
- 3
- industry
- general
- azure
- 06-document-intelligencesmallOptimize model selection, confidence thresholds, batch throughput, cost per document
Optimize model selection, confidence thresholds, batch throughput, cost per document
- primitives
- 10
- waf
- 3
- industry
- general
- azure
- 100-fai-meta-agentlargeOptimize routing model, play matching, combination rules, recommendation feedback loop
Optimize routing model, play matching, combination rules, recommendation feedback loop
- primitives
- 10
- waf
- 6
- industry
- general
- azure
- 15-multi-modal-docprocsmallOptimize vision vs OCR routing per page type, image resolution, batch throughput, cost per document
Optimize vision vs OCR routing per page type, image resolution, batch throughput, cost per document
- primitives
- 10
- waf
- 3
- industry
- general
- azure
- 21-agentic-raglargeOptimize source selection weights, iteration thresholds, cache hit rates, cost per query, max retrieval hops
Optimize source selection weights, iteration thresholds, cache hit rates, cost per query, max retrieval hops
- primitives
- 11
- waf
- 5
- industry
- general
- azure
- 25-conversation-memory-layerlargeOptimize compression ratio, retrieval accuracy, storage costs, memory TTL, embedding model for recall
Optimize compression ratio, retrieval accuracy, storage costs, memory TTL, embedding model for recall
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 28-knowledge-graph-raglargeOptimize graph traversal depth, entity resolution thresholds, hybrid graph+vector retrieval weights, cost per query
Optimize graph traversal depth, entity resolution thresholds, hybrid graph+vector retrieval weights, cost per query
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 48-ai-model-governancelargeOptimize A/B test duration, traffic split ratios, drift detection thresholds, rollout speed, approval SLA
Optimize A/B test duration, traffic split ratios, drift detection thresholds, rollout speed, approval SLA
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 49-creative-ai-studiolargeOptimize creative temperature, variation diversity, brand voice fidelity, A/B test performance, cross-platform adaptation quality
Optimize creative temperature, variation diversity, brand voice fidelity, A/B test performance, cross-platform adaptation quality
- primitives
- 10
- waf
- 5
- industry
- general
- azure
- 50-financial-risk-intelligencelargeOptimize fraud decision thresholds, risk score accuracy, LLM usage ratio for edge cases, latency for real-time scoring, fairness constraints
Optimize fraud decision thresholds, risk score accuracy, LLM usage ratio for edge cases, latency for real-time scoring, fairness constraints
- primitives
- 10
- waf
- 6
- industry
- general
- azure
- 54-ai-customer-support-v2largeOptimize intent classification accuracy, reduce unnecessary escalations, improve CSAT scores, tune sentiment thresholds, minimize cost per resolution
Optimize intent classification accuracy, reduce unnecessary escalations, improve CSAT scores, tune sentiment thresholds, minimize cost per resolution
- primitives
- 10
- waf
- 5
- industry
- customer-support
- azure