AI Agents Don’t Need Complex Workflows. Build One in Python in 10 Minutes

Building an AI agent in Python can be as easy as giving a model some tools and letting it figure out the rest. Most agent setups start the same way: you wire up tool calls, manage retries, track state, and write the routing logic that decides what happens when. It works, but it's brittle. Every time the workflow changes, you're back in the code rewiring the sequence. Strands is an open-source Python SDK built around a different idea. Instead of you hardcoding the orchestration, you let the model handle it. You give it tools and a goal, and the SDK takes care of the agent loop, tool execution, and conversation state. You can go from zero to a working agent in about 10 minutes, and the same primitives that make a simple agent easy to build can be combined to give you more complex setups when you need them. A Model Driven Approach to AI Agents The Strands team calls this a model-driven approach. The LLM is the orchestrator and you define the capabilities it can use. In practice, your agen