Hierarchical agent teams are an advanced way to structure and coordinate multiple AI agents to work together on complex tasks. Instead of a single, monolithic agent trying to do everything, a hierarchy consists of specialized agents and supervisors that delegate, review, and synthesize work. This approach mirrors a human organization, where a manager oversees a team of specialists, leading to more robust, scalable, and modular automated systems.

LangGraph, a library for building stateful, multi-agent applications, provides the framework to define and manage these complex interactions, making it possible to build sophisticated agentic workflows that can tackle multistep problems with greater control and reliability.

At its core, a hierarchical agent team in LangGraph is a graph where nodes represent agents (or tools) and edges represent the flow of information and control between them. The structure is typically organized into at least two levels: a supervisor and a team of worker agents.

• The Supervisor Agent: This is the “manager” of the team. Its primary role is to understand the overall goal, break it down into smaller sub-tasks, and route those tasks to the appropriate worker agent. It receives the initial request, dispatches work, and reviews the output from workers. Based on the results, it can decide the next step, ask for revisions, or conclude the task.
• Worker Agents: These are the “specialists.” Each worker is designed to perform a specific function, such as writing code, researching information, reviewing documents, or interacting with a specific API. They receive instructions from the supervisor, execute their task, and return the result.
• Shared State: A central concept in LangGraph is the shared state object. This is a persistent data structure that every agent in the graph can read from and write to. It acts as the single source of truth for the entire workflow, containing the initial request, intermediate results, conversation history, and the final output. The supervisor uses the state to track progress and make decisions.

This structure allows for complex logic. For example, a supervisor can create a “loop,” sending a piece of work back to an agent for revisions until it meets a certain quality standard. It can also manage parallel execution, where multiple agents work on different parts of a task simultaneously.

Hierarchical agent teams are ideal for tasks that are too complex for a single agent to handle reliably. They excel in scenarios requiring a mix of specialized skills, iteration, and structured oversight.

• Content Creation and Research: A supervisor agent could manage a team consisting of a “researcher” agent that scours the web for information, a “writer” agent that drafts an article based on the research, and an “editor” agent that reviews and refines the draft.
• Software Development: In a coding workflow, a supervisor could orchestrate a “developer” agent to write functions, a “code reviewer” agent to check for errors and style compliance, and a “tester” agent to write and run unit tests.
• Customer Support Automation: A top-level supervisor could first classify an incoming customer query and route it to a specialized sub-team. One sub-team might handle billing questions, while another could manage technical troubleshooting, with each sub-team having its own supervisor and set of specialized agents.
• Data Analysis and Reporting: A team could be tasked with generating a business report. One agent could be responsible for fetching data from a database, another for performing statistical analysis, and a third for creating visualizations and compiling the final report.

These examples highlight how breaking down a large task into a series of managed steps allows for more sophisticated and reliable outcomes.

While powerful, building hierarchical agent teams comes with its own set of challenges. It’s not as simple as just adding more agents to a problem.

• Overly Complex Routing: One of the biggest challenges is designing the supervisor’s routing logic. The supervisor must be able to accurately interpret the state of the workflow and decide the next best action. Poorly designed logic can lead to infinite loops, incorrect task assignments, or premature termination of the workflow.
• State Management: As workflows become more complex, the shared state object can become large and difficult to manage. It’s crucial to design a clean and predictable state structure so that agents can easily find the information they need and update it without causing conflicts.
• The “Too Many Agents” Fallacy: A common misconception is that adding more agents will always lead to better results. In reality, each new agent adds overhead and complexity. The key is to create a small team of highly specialized agents with clearly defined roles, rather than a large, confusing crowd.
• Error Handling and Failover: What happens when a worker agent fails? Or when a tool it relies on is unavailable? A robust hierarchical system must include patterns for error handling, retries, and failover. The supervisor needs the ability to catch exceptions, try an action again, or route the task to a different agent if one is struggling.

Building a reliable hierarchical agent team requires careful planning and adherence to best practices.

• Start with a Clear Flowchart: Before writing any code, map out your agent workflow. Define each agent, its specific role and tools, and the exact conditions under which the supervisor should route tasks to it.
• Define a Strict Schema for State: Enforce a clear, predictable structure for your shared state object. Use data validation libraries like Pydantic to ensure that agents are passing information in the correct format, which helps prevent errors and makes debugging easier.
• Build Incrementally: Don’t try to build the entire complex system at once. Start with a simple two-agent team (one supervisor, one worker) and get it working reliably. Then, incrementally add more agents and more complex routing logic, testing at each step.
• Implement Comprehensive Tracing: Use tools like LangSmith to trace the execution of your agentic graphs. Tracing provides a visual, step-by-step log of the entire workflow, showing the inputs and outputs of each agent, the decisions the supervisor made, and the changes to the state. This is invaluable for debugging and optimization.
• Design for Failure: Assume that agents and their tools will fail. Implement retry logic (e.g., using try/except blocks) within your agent definitions. For more critical failures, build logic into the supervisor to either try a different agent or gracefully exit the workflow.

When you build multi-agent systems, security and user context become critical. For instance, if your agent team interacts with user data or private APIs, you need a robust way to manage permissions and authentication for each agent.

Kinde provides the infrastructure to secure your agentic workflows. You could issue machine-to-machine credentials for your supervisor agent, allowing it to securely authenticate with your backend services. If agents act on behalf of a user, you can pass the user’s permissions into the workflow’s state, ensuring that agents only perform actions the user is authorized for.

For example, a supervisor agent could be granted a specific set of permissions to access certain APIs, while worker agents might have more restricted access, limited only to the tools they need for their specific tasks. This follows the principle of least privilege and adds a crucial layer of security to your automated systems.

As you build and scale your agent architectures, securing their interactions is a critical next step. To learn more about how to manage machine-to-machine authentication and permissions, explore the Kinde documentation:

• M2M overview
• Define user permissions