How to build an effective operating AI Agent system? And how to identify potential issues during development that could lead to significant trouble after deployment?
To answer these questions, you need to break the Agent system down into three parts: tools, reasoning, and action. Each layer has its own challenges. An error in one layer can ripple through to others, causing failures in unexpected ways. The retrieval function may extract irrelevant data. Improper reasoning may lead to incomplete or circular workflows. Actions may fail in production.
The Agent system is only as strong as its weakest link, and this guide will show you how to design systems that avoid these pitfalls. The goal is to build a reliable, predictable, and resilient agent at the most critical times.
Architecture Overview
The Agent system operates on three logical levels: the tools layer, the reasoning layer, and the action layer. Each layer has its specific role, enabling the agent to effectively retrieve, process, and act on information. Understanding the interactions between these layers is crucial for designing systems that are both practical and scalable.
The chart below illustrates these three levels and their internal components:
Tools Layer: The foundation of the system. This layer interacts with external data sources and services, including APIs, vector databases, operational data, knowledge bases, and user interactions. It is responsible for retrieving the raw information that the system relies on. Well-designed tools can ensure that the agent efficiently retrieves relevant, high-quality data.
Action Layer: Sometimes referred to as the orchestration layer. This layer is responsible for coordinating the interactions between large language models (LLMs) and the outside world (tools). It handles interactions with users (if applicable). It receives instructions from the LLM about the next actions to take, executes those actions, and then provides the results back to the reasoning layer’s LLM.
Reasoning Layer: The core of the system’s intelligence. This layer uses large language models (LLMs) to process the retrieved information. It determines what the agent needs to do next, making decisions based on context, logic, and predefined goals. Improper reasoning can lead to errors, such as repeated queries or inconsistent actions.
Agentic Workflow
The action/orchestration layer is the main engine driving the behavior of the agent system forward. This layer provides a primary processing loop, roughly as follows:
The initial interaction between the Agent application and the large language model (LLM) defines the overall goals the system is trying to achieve. This can be anything from generating real estate listings to writing blog posts to handling open requests from users waiting in a customer support application.
In addition to these instructions, there is a list of functions that the LLM can call. Each function has a name, description, and a JSON schema for the parameters it accepts. This simple function example comes from OpenAI’s documentation:
{ "model": "gpt-4o", "messages": [ { "role": "user", "content": "What's the weather like in Boston today?" } ], "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } } ], "tool_choice": "auto"}It is up to the large language model (LLM) in the reasoning layer to decide which function to call next to get closer to achieving the specified goals.
When the LLM responds, it indicates which function should be called and what parameters should be provided to that function.
Depending on the use case and the capabilities of the LLM used in the reasoning layer, the LLM may be able to specify a set of functions that should be called (ideally in parallel) before moving on to the next step in the loop.
Providing an exit function is a good idea so that the reasoning layer can signal when processing is complete, indicating to the action layer that it can successfully exit.
Design Principles
On the surface, all of this seems straightforward. However, as the complexity of tasks increases, the list of functions grows as well. The more aspects there are to handle, the easier it is for the reasoning layer to make mistakes. Once you start adding new APIs, specialized sub-agents, and multiple data sources, you will find that managing is far more complex than just inserting a prompt and clicking “start.”
In Part Two, we will delve deeper into the concept of modularity. We will discuss why breaking your agent system down into smaller, more focused sub-agents can help you avoid the pitfalls of monolithic design.
Each sub-agent can handle its own specialized domain—returns, orders, product information, etc. This separation allows the parent agent to freely allocate tasks without having to manage all possible functions in a single massive prompt.
In Part Three, we will explore the interactions between agents. Even with good modularity, building a unified interface that allows sub-agents to interact in a consistent manner remains a real challenge. We will explore how to define clear, standardized handoff processes that allow each agent to do its job without creating a confusing web of calls and callbacks. You will see why having a consistent interface is important and how it helps you troubleshoot or upgrade issues when they arise.
In Part Four, we will examine data retrieval and retrieval-augmented generation (RAG). Without up-to-date, relevant data, what your language model can do is also limited, so we will discuss how to connect to databases, APIs, and vector stores to provide the context your agent needs. We will cover everything from extracting structured data from existing systems to indexing unstructured content like PDFs to ensure that your system remains fast and accurate as it scales.
Finally, in Part Five, we will discuss horizontal concerns. This includes aspects that are critical yet often overlooked when designing any robust agent system—observability, performance monitoring, error handling, security, governance, and ethics. These factors determine whether your agent can handle real-world traffic, protect user data, and maintain resilience as the architecture inevitably evolves.
This is the roadmap. By the time we finish, you will have the tools and patterns needed to build a reliable, scalable agent system—one that not only sounds good in theory but can truly withstand the pressures of a production environment.