Agentic Retrieval on Managed Amazon Bedrock Knowledge Base Using CDK
· Engineer's Notes · Cem Koyluoglu
Deploy a multi‑turn, tool‑driven retrieval system with built‑in observability and evaluation via a four‑stack CDK app on Managed Bedrock Knowledge Bases.
What happened
The blog post describes a production‑grade agentic retrieval system built on a Managed Amazon Bedrock Knowledge Base and deployed with the AWS Cloud Development Kit (CDK). Classic Retrieval Augmented Generation (RAG) performs a single embed‑and‑fetch operation, which can return incomplete answers when the required information spans multiple corpora or when the appropriate source depends on the question. The solution replaces that pattern with an enterprise‑level reasoning agent that decides whether to retrieve, selects the relevant knowledge base, and iterates retrieval until a grounded, cited answer is produced. Managed Knowledge Bases (type: MANAGED) now own ingestion, storage, indexing, and retrieval, eliminating the need for a separate OpenSearch Serverless collection or OpenSearch Compute Units (OCUs). AgentCore supplies the agentic capabilities through a Gateway, a Runtime, and the AgenticRetrieveStream API, which performs multi‑turn planning, internal reranking, and citation generation. The CDK application defines four inter‑dependent stacks—KnowledgeBasesStack, AgenticGatewayStack, AgentRuntimeStack, and DashboardsStack—each referencing constructs from the others so that CDK automatically resolves deployment order. At deployment (step 0) each S3‑hosted corpus is ingested into its own Managed Knowledge Base (financial and weather). For each user query, the runtime emits OpenTelemetry spans, token counts, and metrics to Amazon CloudWatch and AWS X‑Ray, while the agent may loop through retrieval steps before returning the final answer. The solution also provisions two CloudWatch dashboards covering seven telemetry layers and provides on‑demand and continuous evaluation of answer faithfulness and correctness.
Why it matters in production
The shift from single‑shot RAG to agentic retrieval improves answer completeness for queries that require multiple sources or iterative refinement, reducing the risk of fluent but inaccurate responses. Managed Knowledge Bases remove the operational burden of provisioning, scaling, and patching a vector store, which lowers cost and simplifies compliance by centralizing ingestion and indexing within Bedrock. However, the added reasoning loop introduces variability in latency and call volume, making observability essential. Built‑in telemetry across seven layers, combined with continuous evaluation, enables operators to monitor performance, token usage, and answer quality in real time, preventing drift and supporting responsible AI use. The CDK‑driven cross‑stack wiring eliminates manual export/import steps, reducing configuration drift and deployment errors, which is critical for maintaining reliability in complex, multi‑stack architectures.
Engineering takeaways
- Use Managed Bedrock Knowledge Bases for agentic retrieval to avoid managing OpenSearch Serverless collections and OCUs.
- Deploy the entire stack with CDK so that cross‑stack references automatically enforce correct dependency order and prevent drift.
- Instrument the AgentCore Runtime with OpenTelemetry to capture spans, token counts, and latency metrics for each retrieval loop.
- Provision CloudWatch dashboards covering all seven telemetry layers and enable both on‑demand and continuous evaluation to monitor answer faithfulness.
- Design the agent to loop only when necessary; excessive iterations can increase latency and cost, so set sensible thresholds based on evaluation scores.
Related work on this site
- RAG & Grounded LLM Systems — LLM features that answer from the right source instead of from memory: document-grounded assistants, transcript-grounded chat and quality gates around generated output.
- Agentic & LLM Automation — Multi-step LLM pipelines that run unattended: model cascades with fallbacks, validation gates, scheduled automation and alerting when something breaks.
- YouTube AI Summarizer — Open-source Chrome extension on the Chrome Web Store: AI summaries, key points, deep analysis, a two-host AI podcast (Gemini TTS) and transcript-grounded chat for any YouTube video, using Groq or Ollama Cloud with the user’s own key.
- Ireland Expat Assistant — A custom GPT that answers from uploaded official documents: step-by-step guidance on Irish visas/IRP, employment permits, tax (PAYE/PRSI/USC), HSE/Medical Card and citizenship.
This note was drafted with AI assistance from the primary source credited on this page, and automatically checked against that source before publishing.