Agent Systems: Architecture and Design

What this article covers

• Learn what an agent system is and how it differs from a single AI agent.
• Understand the core components: language model, tools, memory, planning logic, and communication layer.
• Explore the main architecture types, from single agents to multi-agent systems.
• See a concrete research example showing how multiple agents collaborate.
• Discover common pitfalls and when a simple agent is the better choice.

Introduction: Agent systems explained

A single AI agent can solve a task, call a tool, and remember earlier steps. But as tasks grow more complex, one agent is no longer enough. You need multiple agents that coordinate with each other, divide the work, and merge results. That’s what an agent system is.

Agent systems are the next step beyond the single agent. Instead of one instance handling everything alone, you have a network of agents with clearly defined roles. One plans, one researches, one summarizes, one validates the output. Each agent does what it does best, and the overall system delivers more than the sum of its parts.

This article is for beginners. You don’t need prior experience with multi-agent systems, just a basic understanding of what an AI agent is. If that’s unfamiliar, start with What is an AI Agent?.

Why do you need agent systems?

Imagine you want to generate a daily market report. The agent needs to search sources, extract prices, identify trends, write the text, and verify all numbers are correct. A single agent would have to handle all of this in one prompt. That quickly becomes unwieldy, the context window fills up, the agent loses focus, and mistakes creep in.

Without a structured system, here’s what happens: the agent starts researching, then jumps to writing, realizes it’s missing data, goes back to search, loses track of earlier results, and ends up in chaos with half-finished tasks. The output is unreliable, hard to debug, and barely extensible.

An agent system solves this through division of labor. A router agent takes the request and forwards it. A search agent handles research alone. A writing agent creates the report. A review agent checks the result. Each agent has a clear job, focused context, and its own tools. The entire system stays manageable, even as tasks scale up.

Agent systems in brief

An agent system is like a team of employees with different roles. Each person has their task, their tools, and their perspective on the project. A coordinator distributes work, a specialist handles their part, a reviewer ensures quality. The outcome is shared and cohesive.

Applied to AI agents: each agent is its own language model with its own prompt, tools, and memory. The agents communicate through defined message exchange or a shared state. A structure called topology determines who talks to whom and in what order.

In short: an agent system is the architecture that orchestrates multiple AI agents to solve a complex task together.

Who are agent systems for?

Agent systems are for developers who want to use AI agents in production. If you’ve already built simple agents with tool-calling and hit the limits of a single agent, this is for you.

Teams automating AI pipelines also find the tooling here for scalable architectures. Whether research, data processing, content creation, or customer support, any task that breaks into substeps benefits from an agent system.

Prerequisites: you should know the fundamentals. If you’re uncertain, start with AI Agent Basics and the article on Multi-Agent Systems.

Key terms in agent systems

Term	Meaning
Agent system	A network of one or more AI agents working together to solve a task
Single agent	One agent handling all tasks alone
Multi-agent	A system with multiple agents coordinating with each other
Orchestrator	An agent that controls and coordinates overall execution
Router	An agent that forwards incoming tasks to the right specialist agent
Worker	An agent that executes a specific subtask
Shared state	A common store that multiple agents can read and write
Message bus	A channel through which agents exchange messages
Topology	The structure that defines how agents connect to each other
Hierarchy	A topology with parent and child agents

Architecture of an agent system

An agent system consists of several components working together. Each component has a clear role. Here are the core building blocks:

Language model

The language model is the heart of every agent. It processes the prompt, plans next steps, and decides whether to call a tool. In an agent system, each agent can use its own model. A router might use a small, fast model; a complex worker might need a larger one. This keeps costs and latency in check.

Tools

Tools are an agent’s hands. They provide access to search engines, databases, APIs, file systems, and more. Through tool-calling, the agent decides which tool to use and when. In an agent system, each worker only has the tools it needs for its task. This prevents errors and keeps prompts clean.

Memory

Agent memory stores information about task history and earlier work. There’s short-term memory for the current task and long-term memory for recurring patterns. In a multi-agent system, shared state is especially important: a common store where agents share results.

Planning logic

Planning logic determines how an agent proceeds. Should it plan first, then act? Or iterate step by step? Some agents use planning and reflection to adjust their approach. In an agent system, the orchestrator handles high-level planning and breaks tasks into subtasks.

Communication layer

The communication layer connects the agents. It can work as a message bus where agents send messages, or as a shared state that all read and write. The choice of layer affects how flexible and scalable the system is. A message bus suits loosely coupled agents; a shared state suits tight collaboration.

Types of Agent Systems

Agent systems differ in their topology, meaning how agents are connected to one another. Here are the main patterns:

Single Agent

The simplest case: one agent handles everything. It invokes tools, plans, and reflects. Works well for small, clearly defined tasks.

[Task] -> [Single Agent with Tools] -> [Result]

Example: An agent that answers a question by performing a web search.

Linear Chain

Agents work sequentially, each passing its output to the next. Simple to build, but rigid. If one agent fails, the chain stops.

[Task] -> [Agent A] -> [Agent B] -> [Agent C] -> [Result]

Example: Agent A researches, Agent B summarizes, Agent C writes the final text.

Hierarchical

An orchestrator controls multiple workers. It splits the task, gathers results, and combines them. Flexible and well-suited for complex tasks.

          [Orchestrator]
           /     |     \
      [Worker] [Worker] [Worker]
           \     |     /
          [Result]

Example: An orchestrator distributes research tasks to multiple workers, each evaluating a different source.

Peer-to-Peer

Agents communicate directly with each other without central control. Any agent can talk to any other. Flexible, but harder to control and debug.

   [Agent A] <-> [Agent B]
      ^             ^
      v             v
   [Agent C] <-> [Agent D]

Example: Agents that work through a discussion to reach a shared conclusion.

Router-Based

A router receives the task and directs it to the appropriate specialist agent. Similar to a dispatcher. Effective when you have many different kinds of tasks.

            [Router]
           /   |   \
      [Spec A] [Spec B] [Spec C]

Example: A router decides whether a question goes to the code agent, the research agent, or the math agent.

Example: A Research Agent System

Here’s a concrete example of a multi-agent system for research. The task: Create a report on recent developments in local AI.

Step 1: Router Agent receives the task

The router analyzes the task and decides which agents are needed. It breaks the request into subtasks: search, summarize, review.

Step 2: Search Agent conducts the research

The search agent uses tool-calling to query sources. It searches the web, collects articles, and stores results in shared state. Its prompt focuses exclusively on finding relevant sources.

Step 3: Summarization Agent writes the report

The summarization agent reads the collected sources from shared state and writes a structured report. It has no search tools, only writing and formatting tools.

Step 4: Review Agent checks the result

The review agent reads the report, checking for gaps, contradictions, and errors. If it finds problems, it sends the report back to the summarization agent with feedback. If everything looks good, it approves the report.

Step 5: Orchestrator delivers the result

The orchestrator takes the approved report and returns it to the user. Shared state is cleaned up, and the system is ready for the next task.

[Task]
   |
   v
[Router] -> [Search Agent] -> [Shared State]
                                |
                                v
                        [Summarization Agent]
                                |
                                v
                        [Review Agent] -- feedback loop if needed
                                |
                                v
                        [Orchestrator] -> [Result]

This system is extensible. You can add another agent for fact-checking without modifying the others. Each agent stays focused, and the overall system remains manageable.

Agent Systems vs. Single Agents

When do you need an agent system, and when is a single agent enough? It depends on task complexity.

A single agent is the right choice when the task is clearly bounded, requires few tools, and fits in one prompt. It’s quick to build, easy to debug, and cost-effective.

An agent system becomes necessary when the task breaks down into multiple independent steps, requires different roles, or needs parallel processing. It takes more effort to build, but it’s scalable and more robust.

Rule of thumb: Start with a single agent. Once your prompt becomes unwieldy, context overflows, or the agent has to play multiple roles at once, switch to an agent system.

Common Pitfalls in Agent Systems

Agent systems are powerful, but they have their gotchas. Here are the most frequent problems:

1. Too many agents

More agents don’t automatically mean better results. Each agent costs latency and tokens. A system with ten agents for a task that three could handle is slower, more expensive, and more error-prone.

2. Unclear roles

When two agents have similar responsibilities, conflicts arise. One overwrites the other’s work, or results contradict each other. Define each role sharply and distinctly.

3. Infinite loops

A review agent sends the report back, the summarization agent improves it, the review agent finds something else, and so on. Without a stopping condition, the system runs forever. Always set a maximum iteration count.

4. Context loss

When agents communicate only through messages, information gets lost. Shared state helps, but it must be kept in sync, or agents work with stale data.

5. Missing error handling

What happens if a worker agent fails? Without error handling, the entire system stops. Each agent should catch errors and report them to the orchestrator.

6. Cost explosion

Every agent call costs tokens. In a multi-agent system, costs multiply quickly. Use small models for simple tasks like routing and large models only where needed.

7. Hard to debug

In a system with multiple agents, it’s not immediately clear who caused a problem. Logging is essential. Each agent should log its steps, tool calls, and results.

8. Neglected security

Agents with access to APIs, filesystems, and databases can cause harm if they make wrong decisions. Limit each agent’s tools to what’s necessary and use permission checks.

Hardware, Costs, and Security in Agent Systems

Agent systems demand more from hardware than single agents, since multiple agents run in parallel or sequence. If you use local models, you need sufficient VRAM. A router with a small model like Llama 3.2 3B runs on modest hardware, but a worker with an 8B model needs significantly more. Local AI with tools like Ollama makes it possible to run different model sizes in parallel.

Cloud model costs add up fast. A system with five agents, each called multiple times per task, can be much more expensive than a single agent. Use a mix: small models for routing and simple tasks, large models only for complex reasoning. Token limits per agent prevent an agent from spinning in circles and running up costs.

Security matters even more in agent systems because multiple agents have access to different systems. Each agent should only get the tools it needs for its role. A search agent doesn’t need write access to the database, and a review agent doesn’t need API keys for external services. Work with clear permissions, logging, and human checkpoints for critical actions.

Further Reading and Resources on Agent Systems

• What is an AI Agent? - Start here if you’re new to the topic
• Multi-Agent Systems - Deep dive into systems with multiple agents
• Tool Calling - How agents invoke tools
• Agent Memory - How agents retain and recall information
• Planning and Reflection - How agents think ahead
• Frameworks - Tools for building agent systems
• AI Agent Fundamentals - Overview of all foundational articles
• What is Local AI? - Running AI on your own hardware
• Ollama - Managing local models easily

FAQ: Agent Systems - Common Questions

What is an agent system?

An agent system is an architecture of one or more AI agents working together to solve a task. It includes the agents themselves, their tools, shared state, and the communication layer that connects them.

How does an agent system differ from a single agent?

A single agent handles all tasks by itself. An agent system distributes work across multiple agents with distinct roles, which is cleaner, more robust, and scales better for complex problems.

When should I use a multi-agent system?

Consider a multi-agent system when a task breaks into several independent steps, requires different specialized roles, or when a single agent’s context window becomes a bottleneck.

What is an orchestrator?

The orchestrator is an agent that directs overall execution. It breaks down the task, distributes it to workers, and combines their results.

What does a router agent do?

A router agent receives a task and forwards it to the appropriate specialist agent. It decides which agent is responsible for each subtask.

What is shared state?

Shared state is a common storage area that multiple agents can access. It allows agents to exchange results without direct communication between them.

Which topology is best?

It depends on your task. A simple linear chain works for straightforward workflows. Hierarchical structures suit complex tasks with distributed roles. Router-based designs work well when you have many diverse subtasks.

How do I prevent infinite loops in agent systems?

Set a maximum iteration count for each loop. A review agent should halt after a fixed number of attempts and return the best result found so far.

Which framework should I use for agent systems?

Several frameworks streamline agent system development. Your choice depends on your programming language, requirements, and experience level.

Can I run agent systems locally?

Yes. Tools like Ollama let you run local models and build agent systems on your own hardware. Ensure you have enough VRAM, especially if multiple agents run in parallel.

What are the costs for agent systems?

Costs depend on the number of agents, model size, and call volume. Cloud models can become expensive quickly. Mixing smaller and larger models keeps costs reasonable.

How do I debug an agent system?

Logging is key. Each agent should record its steps, tool invocations, and results. This lets you trace which agent contributed what and where failures occurred.

Sources and Further Reading

• What is an AI Agent? - Foundational article on BotServ.de
• Multi-Agent Systems - In-depth coverage on BotServ.de
• Tool Calling - Basics of tool invocation on BotServ.de
• Agent Memory - Storage and recall on BotServ.de
• Planning and Reflection - Planning logic on BotServ.de
• Frameworks - Overview on BotServ.de
• What is Local AI? - Local AI on BotServ.de
• Ollama - Local model management on BotServ.de