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Introduction to Generative AI and Amazon Bedrock

Building Generative AI Workflows with Amazon Bedrock

Takes 150 mins

Generative AI transforms how applications interact with users by enabling dynamic, context-aware, and highly personalized responses. Amazon Bedrock provides a powerful platform for building and managing foundation models, prompt flows, and resilient AI-driven workflows. This Cloud Lab uses Amazon Bedrock to design intelligent orchestration that supports user interactions within a cloud learning platform.

In this Cloud Lab, you will explore Amazon Bedrock’s foundational components, including prompt management, managed Knowledge Bases, and pretrained foundation models. Crucially, you will also get hands-on with the modern AgentCore architecture, learning to build AgentCore Harnesses and AgentCore Gateways to replace legacy Bedrock Agents. You’ll understand how Bedrock integrates with other AWS services to provide a seamless environment for managing AI capabilities. Moreover, you’ll set up a Lambda function to analyze the query’s category.

Moving forward, you’ll design and deploy Prompt Flows tailored to various user queries. You will learn the standard approach for bridging Prompt Flows to your modern AgentCore architectures using an intermediary Lambda function node. These flows will ensure that responses align with user intents and context, and you’ll implement strategies for dynamically adapting prompts based on query complexity.

Finally, you’ll simulate real-world scenarios by building a support system for Cloud Labs. This system will leverage AgentCore Harnesses to respond intelligently to user queries, ranging from basic information requests to assisting them with setting up their infrastructure via AgentCore Gateways for different scenarios.

After completing this Cloud Lab, you will gain hands-on experience creating AI-driven workflows that enhance user engagement. You’ll also better understand how foundation models, Prompt Flows, AgentCore orchestration, and data storage solutions work together to deliver robust and scalable AI applications.

The Prompt Flow architecture
The Prompt Flow architecture

Why Amazon Bedrock workflows matter in real applications

Generative AI gets interesting when it stops being a single prompt-and-response demo and starts behaving like part of a product. Real applications need to route requests, retrieve context, apply guardrails, and coordinate multiple steps before returning an answer a user can trust.

That’s the core value of building workflows with Amazon Bedrock: you can combine foundation models with structured orchestration so your app responds consistently, even as user requests vary from “quick question” to “multi-step support issue.”

What “workflow” means in Bedrock Flows

In practice, a workflow is a sequence of connected steps, often including:

  • A prompt (or prompt template)

  • A model invocation

  • Optional retrieval for grounding (for example, via Knowledge Bases)

  • Business logic (like classification or routing via Lambda)

  • Orchestrated tool execution (via AgentCore Harnesses and Gateways)

  • A final response that matches the user’s intent

Bedrock Flows is designed to help you build, test, and deploy these workflows using a visual builder, linking prompts, models, and integrations into an end-to-end system.

Common building blocks you’ll use again and again

When you build AI workflows that scale beyond a prototype, you’ll repeatedly rely on a few patterns:

  • Prompt management and reuse: As soon as you have multiple intents (billing questions, troubleshooting, onboarding, “how-to” help), you’ll want prompts that are versioned, consistent, and easy to update without breaking everything downstream.

  • AgentCore Harnesses & Gateways for tool use: AI orchestration becomes useful when the model needs to do more than “generate text.” AgentCore Harnesses manage the reasoning and execution strategy (deciding what to do next or following a structured plan), while AgentCore Gateways provide standardized, secure connections to your custom tools and AWS integrations.

  • Knowledge and retrieval for grounded answers: Many support and internal-assistant experiences aren’t about creativity; they’re about correctness. Grounding a model in relevant documents (RAG-style) reduces hallucinations and makes responses more context-aware, especially for product- or organization-specific information.

  • Routing and classification: A simple but high-impact step is to classify a query first (what is the user asking?) before sending it to the right prompt flow or AgentCore Harness. That’s why pairing a lightweight classifier (often via AWS Lambda) with your Bedrock workflow is a practical, repeatable design.

  • Vector storage for semantic retrieval: When you’re storing embeddings for retrieval, you need a place to keep them and query them efficiently. This lab specifically calls out Aurora Serverless as a vector embedding store, which is a common “production-ish” choice when you want relational reliability plus modern retrieval support.

How to know you built something “real,” not just a demo

If your workflow can:

  1. Handle different categories of user requests.

  2. Pull in relevant context when needed.

  3. Adapt its prompt strategy based on complexity.

  4. Use AgentCore Harnesses and Gateways to reliably perform multi-step reasoning or actions.

  5. Produce consistent responses across repeated runs.

…you’re much closer to an application pattern you can reuse at work. This Cloud Lab’s “support assistant” scenario is a solid model for that kind of end-to-end build.