
Discover how Amazon Bedrock Agent Core maps POC pain points to seven components—runtime, gateway, memory, identity, tools, and observability—to enable scalable, secure production deployments.
Set up your AWS account and AWS Builder ID to access the free tier and credits, then install Amazon Key Row and log into Hero for free coding help.
Clone the Mastering Amazon Bedrock Agent Core repo from GitHub and set up a programmatic AWS IAM user with access keys for Cairo and local workflows.
Set up a Python environment with UV in Cairo, install dependencies, and configure the AWS SDK (boto3) to connect to AWS and run code for live flight and weather data.
Explore Cairo, an AI coding companion that supports quick chat and spec-driven planning, enabling MCP server setup for AWS docs and strands agent docs and Google Drive and Calendar workflows.
Deploy a strands SDK travel agent locally and package it for bedrock agent core runtime, then deploy the endpoint and test a ten-day trip to Italy with a $5,000 budget.
Learn to implement and deploy a Bedrock Agent Core runtime on AWS, select frameworks, configure payloads and custom headers, and optimize cost, performance, and session management.
Explore how Agent Core Gateway turns APIs into MCP endpoints, enabling secure inbound and outbound authentication, semantic tool selection, and scalable access to AWS services, REST APIs, and one-click integrations.
Connect real-world weather, flight, and currency APIs via a gateway to enable MCP endpoints with Agent Core Gateway and OpenAPI specs from three free-tier providers.
Learn to add agent core memory, enabling short-term and long-term memory with session, actor, and memory IDs, using semantic memory, summaries, user preferences, and episodic memories.
Implement three long-term memory strategies in Amazon Bedrock AgentCore memory—travel preferences, semantic, and session summary—by configuring AWS memory resources, populating events, and querying preferences.
Master agent core identity fundamentals by exploring agent identity, the token vault, and credential providers; compare two low and three low OAuth flows for secure machine-to-machine and user access.
Set up an outbound flow from the agent to Google Drive using Agent Core Identity and OAuth 2.0 credentials, configure Google Cloud, and save travel itineraries to Google Drive.
Explore agent core tools, running Python, TypeScript, and JavaScript in a code interpreter to process data with pandas, and use browser tool sessions with ephemeral, per-agent isolation.
Add the agent core tools code interpreter to run Python calculations, enabling travel budget breakdowns and cost-per-day analyses within a dedicated interpreter session.
Learn how to equip your agent with the Agent Core browser tool to spin up live web sessions, retrieve Louvre tour pricing, and check tomorrow's Paris Opera events for availability.
Connect the agent core runtime to the gateway, test MCP endpoints for flight, weather, and exchange rates, and deploy the travel companion with environment variables and OAuth for trip planning.
Execute a thorough cleanup of your Amazon Bedrock Agent Core project by deleting runtime agents, gateway targets, memory, identity, Cognito pools, ECR repositories, and S3 buckets.
Explore agent core observability for production, exporting logs, metrics, and traces to CloudWatch or third-party tools, and build dashboards with alerts for latency and cost.
Deploy a runtime agent and activate AgentCore observability to explore span traces and sessions, then analyze CloudWatch OpenTelemetry logs for end-to-end tracing of tool calls.
Build an AgentCore policy-enabled AI agent, connect a gateway to lambda targets, and deploy a policy engine with CDAP policies; test enforcement and explore NL to CIDA policy generation.
Master agent core evaluation by exploring on-demand and online modes that export via open telemetry to CloudWatch, and by using built-in and custom evaluators for span, trace, and session metrics.
Learn to deploy and evaluate an AgentCore runtime with on-demand and online evaluations, using built-in metrics, custom evaluators, and observability dashboards in CloudWatch.
This course contains the use of artificial intelligence.
Master Amazon Bedrock AgentCore and build production-ready AI agents in just 6 hours. This comprehensive course takes you from zero to deploying intelligent, multi-agent systems that integrate with real enterprise tools—all using AWS's newest agentic AI platform.
Whether you're an enterprise developer rushing to deploy AI agents in production, or a seasoned AWS engineer transitioning into agentic AI, this course gives you everything you need to build sophisticated AI systems that actually ship to production.
What You'll Learn - Foundation & Core Services:
AgentCore Quick Start – Deploy your first intelligent agent in 15 minutes using Amazon Bedrock
AgentCore Runtime Integration – Master ANY agent framework: Strands, LangGraph, CrewAI, or custom Python implementations
AgentCore Gateway – Connect agents to MCP servers, third-party APIs, and internal tools with secure credential management
AgentCore Memory – Implement conversation history, long-term memory, and context-aware agents that remember user preferences
AgentCore Identity – Handle OAuth flows, API key management, and IAM integration for secure, multi-user agent systems
AgentCore Observability - Leverage the Amazon CloudWatch GenAI Observability Dashboard and integrate with 3rd Party tools via OpenTelemetry
AgentCore Code Interpreter – Let agents write and execute Python code dynamically for data analysis and computation
AgentCore Browser Tools – Enable agents to navigate websites, extract data, and interact with web applications autonomously
AgentCore Evaluation – Measure, validate, and benchmark your agents with production-grade evaluation workflows
AgentCore Policy – Apply fine-grained guardrails to control what your agents can and can't do in production
Hands-On Learning: 10+ Production Labs
This isn't just lectures—you'll build real applications through comprehensive, step-by-step labs:
Lab 0: Setup your AWS Account, Amazon Kiro and use the AWS Free Tier
Lab 1: AgentCore Runtime – Deploy your first Bedrock AgentCore Runtime agent with Strands SDK and Claude Sonnet, test locally, and understand the core architecture.
Lab 2: Gateway – Connect your agent to real-world Weather, Flight and Exchange rate API sources using MCP and AgentCore Gateway with API Key authentication.
Lab 3: Memory – Build a customer service agent with conversation history, user profile memory, and context-aware responses across sessions.
Lab 4: Identity & OAuth – Implement secure 3LO OAuth agents with Google OAuth to create documents in your Google Drive, credential management, and per-user data isolation.
Lab 5: Code Interpreter Tools – Create a data analysis agent that writes Python code, generates visualizations, and performs statistical analysis on user data.
Lab 6: Browser Tools – Build a research agent that navigates websites, extracts information, and compiles reports automatically.
Lab 7: Integrate everything into one Agent - Create one AgentCore Runtime Agent that connects to Gateway with all MCP Tools for weather, flight and exchange rate.
Lab 8: AgentCore Observability – Instrument your agents with CloudWatch GenAI Dashboard and OpenTelemetry for full production visibility
Lab 9: AgentCore Evaluation – Run evaluations on your agents to measure quality, catch regressions, and validate behavior before shipping
Lab 10: AgentCore Policy – Deploy fine-grained policies that control agent behavior in production environments