Courses, deep dives, and events from the team building the SDK. Everything you need to go from your first agent loop to production.
Fourteen lessons, one production agent. Build up from the agent loop through tools, hooks, steering, memory, and multi-agent patterns — then deploy and evaluate it.
Tell us what you want to learn next on Discord.
Long-form explorations beyond the courses.
A production case study in constraining agent behavior — and the numbers behind the headline.
Tell us on Discord or open a discussion on GitHub.
Where to find the team.
A team at Amazon Ads built a multi-agent system that converts Figma designs into production ad layouts. Here's what their architecture looks like, why they chose Strands, and the lessons they learned shipping it.
Harness Optimizer is an open-source library that optimizes the context around your LLM agent (system prompt, tool docs, skills) the way you'd train a model: rollouts in, rewards out, parameters updated.
Strands Agents ships context management that cuts costs in half, Strands Shell for sandboxed agent execution, and chaos testing and red teaming in Strands Evals 1.0.
