Structuring Agent Behavior: Agent Orchestration Patterns

In the previous lesson, we explored how agents perceive, remember, reason, and act over time, forming a loop of intelligent behavior. But as agent capabilities grow, so does the need for structure. How exactly does an agent decide what to do next? How does it sequence actions, choose tools, or collaborate with others?

That’s where orchestration patterns come in.

Orchestration defines how an agent manages its internal decision-making flow, and, in more complex setups, how multiple agents coordinate across a shared task. It’s not just about acting; it’s about structuring action in a way that’s reliable, flexible, and context-aware. These patterns are the architectural building blocks that can enable advanced system behaviors like the dynamic routing of tasks, and the parallel execution of sub-goals.

In this lesson, we’ll explore the following:

• Single-agent orchestration strategies: Tool calling loops, ReAct, and plan-and-execute patterns that help a single agent structure its thinking and execution.

• Multi-agent collaboration models: Manager-Worker setups, decentralized teams, and how agents can work together on shared goals.

• Decision criteria for choosing patterns: When should you go solo? When is coordination better? We’ll walk through how to choose based on task complexity and system needs.

• Frameworks that help: A look at orchestration tooling in popular frameworks like LangChain, CrewAI, and AutoGen.

By the end, you’ll understand how to move from a single agent loop to fully orchestrated workflows, whether they live in one brain or across many.

Single-agent orchestration patterns

When we think of an AI agent acting autonomously, we’re usually talking about a single-agent system. This means one model is in charge; it receives input, makes decisions, and performs actions, possibly using external tools along the way.

But even within single-agent setups, there are several patterns for how this decision-making unfolds. These patterns differ in how structured the reasoning is, how tools are chosen, and how many steps are taken before producing an output.

Let’s look at a few common orchestration patterns used in single-agent systems:

Tool calling loop

This is the most straightforward orchestration structure. The agent receives a task, decides which tool to use (if any), executes it, observes the result, and continues. This forms a loop until the goal is met or the agent stops. This pattern primarily leverages the LLM as the reasoning core, a tool invocation layer, and often short-term memory to track interaction state.

For example, an agent tasked with “Send an email summary of this document” might:

• Parse the document.

• Call a summarization tool.

• Use a messaging API to send the result.

• Exit.

This loop is commonly used in LangChain’s AgentExecutorIn LangChain, the AgentExecutor is a runtime that powers agents. It's responsible for taking an agent's input, deciding which tool to call based on the LLM's reasoning, executing that tool, and then repeating the process until a final answer is reached or a stopping condition is met. It effectively implements the tool-calling loop pattern. and OpenAI’s tool calling setups. However, while simple, complex tasks can lead to long, brittle chains of tool calls that are difficult to debug or recover from.

ReAct (reasoning + acting)

The ReAct pattern adds structure by alternating explicit reasoning steps with tool actions. The model is prompted to think step-by-step, decide what tool to use, reflect on the result, and repeat as needed. This pattern improves transparency and traceability. Each thought and action ...