Customize agent workflows with advanced orchestration techniques using Strands Agents

Large Language Model (LLM) agents have revolutionized how we approach complex, multi-step tasks by combining the reasoning capabilities of foundation models with specialized tools and domain expertise. While single-agent systems using frameworks like ReAct work well for straightforward tasks, real-world challenges often require multiple specialized agents working in coordination. Think about planning a business trip: one agent is needed to research flights based on schedule constraints, another to find accommodations near meeting locations, and a third to coordinate ground transportation—each requiring different tools and domain knowledge. This multi-agent approach introduces a critical architectural challenge: orchestrating the flow of information between agents to ensure reliable, predictable outcomes. Without proper orchestration, agent interactions can become unpredictable, making systems difficult to debug, monitor, and scale in production environments. Agent orchestration addresses this challenge by defining explicit workflows that govern how agents communicate, when they execute, and how their outputs integrate into cohesive ...