Capstone Project: Enterprise AI Assistant
The Final Challenge. Building a production-grade, secure, and observable AI system on AWS Bedrock.
60 articles
The Final Challenge. Building a production-grade, secure, and observable AI system on AWS Bedrock.
Understanding the foundations of AWS Bedrock, the managed service for foundation models.
Hands-on: Analyzing model suitability and understanding the serverless AI paradigm.
The Autonomous Brain. Understanding how Bedrock Agents use reasoning to solve multi-step problems.
Defining the Mission. How to write effective 'Agent Instructions' that guide an autonomous AI through complex workflows.
Hands-on: Design the mission statement and tool-set for your first Bedrock Agent.
Defining the Capability. How to describe your APIs to a Bedrock Agent using OpenAPI schemas.
Connecting to Code. How to use AWS Lambda to execute the actual logic behind your agent's action groups.
Hands-on: Write a Lambda function and a schema for an agent that can track package deliveries.
How Agents Think. Understanding the ReAct (Reason + Act) cycle that powers Bedrock's autonomous decisions.
Resilient Autonomy. How to design agents that can recover from API errors and tool failures gracefully.
Hands-on: Trace the execution of a multi-step agent and identify reasoning bottlenecks.
Division of Labor. How to split 'Thinking' and 'Doing' between two different agents for higher reliability.
The AI Manager. How to build a supervisor agent that routes user queries to the correct specialized sub-agent.
Hands-on: Design the architecture for a multi-agent system that handles a complex business workflow.
Safe Autonomy. How to implement 'Pause and Approve' patterns to ensure humans sign off on high-stakes AI actions.
Knowing your Limits. How to design agents that realize they are stuck and hand over the conversation to a human expert.
Hands-on: Design a human-in-the-loop workflow for a high-value financial transaction agent.
Sseeing the Thoughts. How to use CloudWatch and Tracing to debug your Bedrock Agents and see their step-by-step logic.
Protecting the Wallet. How to track token usage and set up alerts to prevent unexpected AWS bills from your GenAI apps.
Hands-on: Create a CloudWatch dashboard that tracks your agent's success rate and token spend.
Locking Down the AI. How to use IAM roles and AWS Secrets Manager to ensure your agent can only access exactly what it needs.
Prompt Injection Defense. Advanced strategies for preventing users from tricking your agent into tool misuse.
Hands-on: Design a secure architecture that involves IAM, Secrets Manager, and Guardrails.
Safe Deployments. How to use Bedrock Agent Versions and Aliases to update your AI without breaking your production app.
Handling the Peak. Advanced strategies for dealing with Bedrock's rate limits using exponential backoff and request queuing.
Hands-on: Design a deployment strategy for a mission-critical AI agent.
The Enterprise Orchestrator. Understanding why AgentCore exists and how it brings deterministic control to AI workflows.
Choosing the Right Tool. Comparing the strengths of autonomous Bedrock Agents and controlled AgentCore workflows.
Hands-on: Identify a business workflow that requires the control and state management of AgentCore.
The Blueprint. Understanding how AgentCore uses 'Nodes' and 'Edges' to create complex, manageable AI graphs.
AI with Memory. How AgentCore preserves state across days or weeks, allowing workflows to survive crashes and wait for human input.
Hands-on: Design a multi-node AgentCore graph that includes AI reasoning and data validation.
Mastering Identity and Access Management for AI. Creating policies that allow your apps to talk to Bedrock securely.
The 'Edit Model Access' step. How to manually request access to foundation models in the AWS Console.
Hands-on: Verify your AWS configuration and enable your first foundation models.
High-Fidelity Search. How to use AgentCore to make your RAG systems more reliable by adding pre-search and post-search nodes.
Chain of Thought Orchestrated. How to build complex reasoning pipelines where multiple AI models check each other's work.
Hands-on: Design an AgentCore workflow that uses a critic node to verify RAG output.
Going Raw. How to use InvokeModel to send model-specific JSON payloads to Bedrock.
Unified AI. How to use AWS Bedrock's standard interface to write model-agnostic code.
Hands-on: Write a script that compares the outputs of two different models using the same prompt.
Defining the Persona. How to use the 'System' role in Bedrock to control AI behavior and safety.
Saving Money by Design. How to optimize your prompts to use fewer tokens and reduce your AWS bill.
Hands-on: Robust prompt engineering to reduce hallucinations and maximize cost-efficiency.
The Typing Effect. How to use converse_stream to send tokens to your UI as they are generated.
Handling the Load. Understanding Bedrock's rate limits and how to optimize for the fastest response times.
Hands-on: Build a streaming CLI chat application that handles tokens as they arrive.
Creating the Backend. How to wrap your Bedrock logic in a high-performance REST API.
Memory in the Cloud. How to maintain conversation context across multiple API requests.
Hands-on: Build a functional REST API with FastAPI that exposes multiple Bedrock models.
Foundations of RAG. Why Knowledge Bases are the secret to building AI that 'knows' your private business data.
Slicing the Data. Understanding how Bedrock breaks docs into 'Chunks' and turns them into 'Vectors' of meaning.
Hands-on: Design your first Knowledge Base and select your chunking strategy.
The RAG APIs. Understanding the difference between raw retrieval and the fully managed 'Answer' API.
Proving the Answer. How to extract citations and references to show users exactly where the AI found its information.
Hands-on: Build a Python script that answers questions about your S3 documents and prints the citations.
Setting the Safety Net. How to use AWS Bedrock Guardrails to filter sensitive content and block inappropriate prompts.
Fighting the Hallucination. Advanced techniques to ensure the AI stays strictly within the retrieved documentation.
Hands-on: Implement a Bedrock Guardrail and verify your grounding instructions.