Build agentic retrieval on a Managed Amazon Bedrock Knowledge Base with CDK

Building Agentic Retrieval on a Managed Amazon Bedrock Knowledge Base with CDK

Introduction

Enterprise‑grade Retrieval‑Augmented Generation (RAG) often falls short when answers require multiple sources, dynamic source selection, or iterative refinement. By placing a reasoning agent in the loop, Amazon Bedrock’s Managed Knowledge Base (KB) and AgentCore services enable agentic retrieval: multi‑turn planning, tool‑driven fetching, and grounded, cited answers. This guide walks through a single‑command AWS CDK (Python) deployment that provisions two managed KBs, an AgentCore gateway, CloudWatch observability dashboards, and evaluation pipelines.

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1. Why Agentic Retrieval Matters

| Traditional RAG | Agentic Retrieval | |-----------------|-------------------| | One embed‑and‑fetch step | Agent decides if and what to fetch | | Fixed index, no source routing | Semantic routing across multiple KBs | | No feedback loop | Iterative retrieval until answer is complete | | Limited traceability | Full telemetry for each reasoning step |

When a question spans multiple corpora or requires a second look after an initial result, a single retrieval can produce incomplete or misleading answers. An agent can:

1. Select the appropriate KB based on the query.
2. Retrieve relevant chunks, optionally re‑ranking them.
3. Iterate if the partial answer is insufficient.
4. Synthesize a final response with citations.

2. Managed Knowledge Base vs. DIY Vector Store

| Feature | Managed KB (Bedrock) | DIY (OpenSearch, etc.) | |---------|----------------------|------------------------| | Ingestion & indexing | Fully managed, auto‑scaled | User‑provisioned, capacity planning required | | Embedding & reranking | Built‑in models, no extra cost | Must configure and pay for separate models | | AgenticRetrieveStream API | Available out‑of‑the‑box | Not supported | | Gateway integration | Native AgentCore connector | Custom integration needed |

The Managed KB removes the operational burden of a vector database, making it the natural foundation for agentic retrieval. It also unlocks the AgentCore Gateway, which exposes each KB as a Model Context Protocol (MCP) tool for the reasoning agent.

3. CDK Architecture Overview

``` +-------------------+ +-------------------+ +-------------------+ | Amazon S3 Bucket | ---> | Managed KB #1 | ---> | AgentCore Runtime | +-------------------+ +-------------------+ +-------------------+ ^ | | v +-------------------+ +-------------------+ +-------------------+ | Amazon S3 Bucket | ---> | Managed KB #2 | ---> | AgenticRetrieve | +-------------------+ +-------------------+ +-------------------+ ```

• Data sources – Two S3 buckets hold separate corpora (e.g., product docs vs. policy manuals). CDK creates ingestion jobs that feed each Managed KB.
• AgentCore – Deploys a Gateway that registers each KB as an MCP tool and a Runtime that hosts the reasoning agent.
• AgenticRetrieveStream – The API call the agent uses to perform multi‑turn retrieval and receive a cited answer.
• Observability – Seven‑layer CloudWatch dashboards capture: request latency, token usage, KB hit/miss ratios, agent decision paths, reranking scores, and evaluation metrics.
• Evaluation – Two Lambda‑backed jobs provide on‑demand test‑set scoring and continuous drift monitoring, publishing results to a dedicated dashboard.

4. Step‑by‑Step Deployment

1. Clone the starter repo (contains `cdk.json`, `requirements.txt`, and `app.py`).
2. Configure environment variables – set `AWSACCOUNT`, `AWSREGION`, and optional `KBNAMEPREFIX`.
3. Run `cdk bootstrap` to prepare the account for asset deployment.
4. Deploy with a single command:

```bash cdk deploy --all ``` CDK resolves dependencies, creates the two Managed KBs, provisions the AgentCore Gateway, sets up CloudWatch dashboards, and wires the Lambda evaluation functions.

1. Test the endpoint:

```python import boto3, json client = boto3.client('bedrock-agent-runtime') resp = client.invokeagent( AgentId='my-agent', Input='How does our refund policy differ for premium customers?' ) print(json.dumps(resp, indent=2)) ``` The response includes `answer`, `citations`, and a `traceid` you can look up in the dashboards.

5. Observability & Evaluation Built In

• Telemetry layers – request metadata, agent decision tree, KB query logs, reranking scores, model inference metrics, latency breakdown, and final answer quality.
• Dashboards – pre‑populated widgets show per‑KB hit rates, average turns per query, and error rates.
• Evaluation Lambda – pulls a curated test set from S3, runs `AgenticRetrieveStream` against each query, compares generated citations to ground truth, and writes scores to a CloudWatch metric.
• Continuous monitoring – a scheduled rule triggers the evaluation job nightly, alerting via SNS if drift exceeds a configurable threshold.

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Closing Thoughts

By leveraging Amazon Bedrock’s Managed Knowledge Base and AgentCore, you can move from a brittle single‑shot RAG pipeline to a robust, multi‑turn agentic retrieval system without managing vector stores or custom orchestration layers. The CDK‑driven approach ensures reproducible infrastructure, automatic ordering of resources, and out‑of‑the‑box observability that turns a black‑box AI service into an auditable, production‑ready component. Deploy the stack, monitor the dashboards, and iterate on your corpora—your enterprise agents will answer complex questions with confidence and traceability.