Overview
- Practitioners are moving beyond demo code to hardened production stacks that combine orchestration, tool interfaces, memory, data stores, and monitoring using checklists and an AWS‑mapped reference architecture.
- Retrieval‑augmented generation is now common for private data but often breaks when chunking is wrong, embeddings become stale, reranking is missing, or metadata is poor, which produces confident but incorrect answers.
- Reliable production systems require explicit failure strategies such as timeouts, retries with backoff, queueing and backpressure, idempotency keys, dead‑letter queues, fallback models, and human review for risky actions.
- Engineers report the specific architectural patterns that matter most are plan‑then‑execute workflows, separating retrieval from reasoning, and structured, logged handoffs between agents rather than reliance on any single framework.
- Teams must manage two cost buckets—infrastructure and inference—by routing simple requests to smaller models, caching results, sizing chunks for retrieval cost, and enforcing data‑layer governance to limit prompt injection and sensitive data leaks.