Gate 1
Retrieval contract
Define queries, relevant items, hard negatives, top-k, recall or relevance threshold, latency objective, freshness, filters, empty-result behavior, and agent fallback.
Retrieval evaluation before migration
DynamoDB Vector Search Readiness
Describe your agent memory or RAG corpus, embedding, dimensions, access patterns, partition candidates, filters, quality labels, latency, freshness, security, and budget. Generate a shadow-test plan instead of treating a service benchmark as application proof.
Four readiness gates
Gate 2
Data and index
Record embedding model and version, dimension, normalization, item size, vector-index partition key, distribution, filter fields, write rate, updates, deletes, and backfill.
Gate 3
Security and lifecycle
Test tenant isolation, IAM, encryption, sensitive-data controls, retention, deletion proof, backups, logs, model migration, feature flag, and rollback.
Gate 4
Scale and cost
Measure concurrency, throttling, retries, index and table operations, storage, embedding generation, transfer, observability, and cost per accepted agent answer.
Minimum shadow evaluation
Freeze the corpus, embedding version, dimensions, partition and filter scheme, relevance labels, query set, top-k, and thresholds. Replay expected and burst traffic beside the current retriever; compare quality, latency, freshness, throttling, delete behavior, full cost, and final answer safety. Keep shadow results hidden from users and exercise the fallback.
Proceed
Quality, latency, freshness, isolation, operations, and cost all pass on representative data.
Redesign
Partition skew, filters, dimensions, relevance labels, deletion, or model migration is unresolved.
Keep current retriever
The new path does not improve accepted answers or operations enough to justify migration risk.
Read the architecture guide
DynamoDB vector search for agent memoryReview GA scope, vector-index partitioning, filtered ANN, AWS performance claims, evaluation, security, and rollback.Official facts checked August 15, 2026. Recheck current API, quotas, dimensions, consistency, pricing, and Regions.
Frequently Asked Questions
It turns your corpus, embeddings, partitioning, filters, relevance, latency, freshness, security, and cost requirements into an evaluation checklist. It does not create an index or access AWS.
AWS says approximate nearest-neighbor searches can filter on attributes. Validate filter correctness, selectivity, latency, and tenant isolation with your data.
No. Freeze a labeled evaluation set and measure the exact embedding, partition, filters, top-k, workload, and final agent answer.
It is a candidate for semantic retrieval over agent memory, but memory authorization, freshness, retention, deletion, poisoning, relevance, and fallback must pass first.