Challenges & Solutions
Challenges
- LLM costs explode without routing, caching, and context discipline
- RAG quality depends on ingestion, chunking, and vector hygiene
- Prompt injection and data exfiltration are live attack surfaces
- Agents need durable state across long multi-step workflows
- Every enterprise asks where prompts and completions are stored
CloudFormation Solves
- Model router with semantic caching cuts token spend 40-70%
- Managed ingestion: parse → chunk → embed → upsert with drift re-index
- Guardrail layer scans prompts and outputs for PII and injections
- Step-function agent orchestration with checkpointed state
- Regional data pinning with full prompt/completion audit trail
Architecture Patterns
LLM Gateway
Single API for all model providers. Semantic cache for repeated intents. Cost and latency routing between frontier and small models. Token budgets per tenant.
RAG Pipeline
Document ingestion through parsing and chunking Lambdas. Embeddings into OpenSearch/vector store. Retrieval with reranking. Citations returned with every answer.
Agent Orchestration
Step Functions drive multi-step agent plans. Tool calls run in isolated Lambdas. Human approval gates for high-impact actions. Full trace stored per run.
Safety & Observability
Guardrails on input and output. Prompt/completion logging with PII redaction. Eval harness scores answer quality nightly. Drift alarms on embedding stores.
Business Outcomes
Supported Providers
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