Domain 2: Implementation and Integration (26%)

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Exam Tip

This domain tests your ability to implement GenAI applications end-to-end. Know when to use Bedrock Agents vs. a simple Knowledge Base call, understand the three Bedrock APIs, and be able to explain chunking strategy trade-offs. Agents + Domain 1 together = 57% of the exam.

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2.1 Agentic AI & Amazon Bedrock Agents

What is an Agent?

Amazon Bedrock Agents enable multi-step, autonomous reasoning by orchestrating FM calls, tool invocations, and knowledge base retrievals to complete a user goal.

Core Components:

Component	Role
Agent	The orchestrator — receives the user request and plans the steps
Action Groups	Lambda functions the Agent can invoke to interact with external systems
Knowledge Bases	RAG pipeline the Agent can query for document-based context
Orchestration Trace	Step-by-step log of the Agent's reasoning and tool calls

Action Groups

Action Groups define what external actions an Agent can take:

• Each action group is backed by a Lambda function
• The Agent decides whether and when to call an action based on the user's request
• Actions are described with an OpenAPI schema — the Agent uses this to understand what parameters to pass

Examples:

• Look up a customer's order status in a database
• Send a confirmation email via an external API
• Write a record to an S3 bucket

Knowledge Base Integration

Agents can be connected to a Knowledge Base to retrieve document context:

• The Agent automatically decides when to query the Knowledge Base vs. call an Action Group
• Knowledge Bases use the RAG pipeline: S3 → chunking → embeddings → OpenSearch Serverless

Orchestration Trace

Enable with enableTrace: true in the InvokeAgent API call:

• Shows the Agent's step-by-step reasoning chain (which step, which tool, which decision)
• Critical for debugging unexpected Agent behavior

Exam Trap

Bedrock Agents vs. Knowledge Bases (RAG only):

• Use a Knowledge Base alone when: the task is document Q&A with no external action needed
• Use Bedrock Agents when: the task requires multi-step reasoning, calling external APIs, or taking actions beyond retrieval

The exam will present scenarios — choose Agents when action or orchestration is involved.

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2.2 Knowledge Base Architecture

End-to-End RAG Flow

1. Data Source: S3 bucket (PDFs, Word, HTML, Markdown, CSV)
2. Parser: Extract and clean text from documents
3. Chunker: Split text into smaller pieces (fixed, semantic, hierarchical)
4. Embedder: Titan Text Embeddings / Cohere Embed → convert text to vectors
5. Vector Store: Amazon OpenSearch Serverless (or Aurora pgvector)
6. At inference: embed query → vector search → top-K chunks → inject into FM prompt

Supported Data Sources

• Amazon S3 — primary ingestion source (most common exam scenario)
• Web Crawler — Bedrock Knowledge Bases can crawl websites
• Salesforce, Confluence, SharePoint — native connectors

Chunking Strategies

Strategy	How It Works	Best For	Trade-off
Fixed-size	Split by token count (e.g., 300 tokens)	Uniform documents	May break mid-sentence or mid-context
Fixed-size + overlap	Tokens overlap between adjacent chunks	Preserving cross-boundary context	Higher storage and retrieval cost
Semantic	Split by meaning/topic using NLP	Long-form, varied content	Higher processing complexity
Hierarchical	Parent chunk + child chunk structure	Complex docs needing broad + fine retrieval	More complex retrieval logic

TIP

Hierarchical chunking is best when you need both broad context and fine-grained retrieval. Fixed-size with overlap is the simplest option for preserving context across chunk boundaries.

Sync and Ingestion

• After updating S3 content, you must trigger a sync to update the vector store
• The Knowledge Base does NOT auto-update when S3 changes
• Ingestion status is visible in the Bedrock console

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2.3 API Integration Patterns

The Three Core Bedrock Runtime APIs

API	Use Case	Response Model
InvokeModel	Synchronous — get a complete response in one call	Blocking, full response at once
InvokeModelWithResponseStream	Streaming — receive response token by token	Non-blocking, chunk-by-chunk delivery
InvokeAgent	Multi-step agentic workflow	Streaming trace + final response

When to Use Each

User needs a complete answer in one shot?
└─ InvokeModel (synchronous, simple)

User interface needs a low-latency "typing" feel?
└─ InvokeModelWithResponseStream (streaming)

Task requires tool calls, external APIs, or multi-step planning?
└─ InvokeAgent

InvokeModel Request Body (Simplified)

{
  "modelId": "anthropic.claude-3-sonnet-20240229-v1:0",
  "body": {
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 1024,
    "messages": [
      { "role": "user", "content": "Summarize this document..." }
    ]
  }
}

Key parameters:

• modelId: The specific FM to invoke
• max_tokens: Cap on output tokens — controls cost and response length
• temperature: Controls creativity vs. determinism (0.0–1.0)

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2.4 Retrieval Configuration & Tuning

Top-K Retrieval

• At query time, the embedding of the user's question is compared against all vectors in the store
• The top-K most similar chunks are retrieved and injected into the FM prompt
• Higher K: More context but longer prompts (higher cost, risk of irrelevant chunks)
• Lower K: Focused context but may miss relevant information

Metadata Filtering

• Attach metadata to chunks during ingestion (e.g., department: "legal", year: 2024)
• At query time, filter retrieval to only return chunks matching specific metadata
• Allows scoped retrieval without maintaining separate vector stores per category

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Flashcards

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What is the role of an Action Group in a Bedrock Agent?

(Click to reveal)

💡

Action Groups are Lambda functions the Agent can invoke to interact with external systems (databases, APIs, storage). They are defined with an OpenAPI schema so the Agent understands what parameters to pass and when to call them.

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