Amazon Web Services

Building AI Agent Harnesses with Strands Agents

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Amazon Web Services

Building AI Agent Harnesses with Strands Agents

Morgan Willis

Instructor: Morgan Willis

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Construct an agent harness that orchestrates an AI model, manages tools, context, and state, and controls the agent loop from first principles.

  • Choose between and implement three multi-agent orchestration patterns (agents-as-tools, graph workflows, and swarms) to coordinate multiple agents.

  • Evaluate agent behavior with trajectory and multi-turn evaluators, then deploy to cloud environments with persistent memory and session management.

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Recently updated!

July 2026

Assessments

5 assignments

Taught in English

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There are 5 modules in this course

This module introduces the core concepts behind AI agents and agent harnesses. Learners explore how the agent loop enables reasoning, tool use, and response generation, while gaining hands-on experience configuring model providers and connecting external tools through MCP. By the end of the module, learners will understand the foundational building blocks that power agentic applications.

What's included

2 videos2 readings1 assignment1 plugin

This module focuses on customizing and extending agent behavior. Learners discover how callbacks, streaming, hooks, plugins, skills, and steering mechanisms can be combined to create more interactive, controllable, and adaptable agent experiences. The module emphasizes how these primitives fit together within an agent harness to influence execution and outcomes.

What's included

5 videos3 readings1 assignment

This module examines what context engineering is and how agents maintain context across interactions. Learners explore conversation history management, state persistence, context window considerations, and session management patterns. These concepts provide the foundation for building agents that can support longer-running interactions and multi-user environments.

What's included

2 videos2 readings1 assignment

This module introduces patterns for coordinating multiple agents to solve complex tasks. Learners build agent systems using delegation, graph-based workflows, and swarm architectures. The module demonstrates how individual agents can be combined into larger systems that distribute work, share context, and collaborate to achieve goals.

What's included

3 videos1 reading1 assignment

This module focuses on preparing agent systems for production use. Learners explore evaluation methodologies, deployment architectures, scalability considerations, and operational best practices. By the end of the module, learners will understand how to measure agent performance and deploy reliable agent applications in cloud environments.

What's included

2 videos2 readings1 assignment1 plugin

Instructor

Morgan Willis
Amazon Web Services
25 Courses691,693 learners

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