Designing Effective AI Agent Systems: A Practical Guide for Developers

Since relocating to Silicon Valley in 2025, I’ve witnessed AI’s pervasive influence. After attending NVIDIA GTC 2025, one insight stood out from countless discussions: many companies now have AI agents operating successfully within specific projects or departments. Yet, almost none have managed to scale these solutions across entire organizations. Even where agents are deployed, they’re frequently poorly structured, with teams shipping systems almost by intuition.

Common questions I encountered include:

• What is the ideal number of AI agents in a team?
• Which model provider performs best?
• Should agents have a “boss” agent supervising them, or coordinate peer-to-peer?

In essence, the central challenge is: What is the best organizational structure for a team of AI agents? This article aims to answer that without diving into heavy mathematics—instead focusing on practical organization for real business use cases. We’ll draw heavily from a recent paper by Google Research, Google DeepMind, and MIT titled Towards a Science of Scaling Agent Systems: When and Why Agent Systems Work. For code demonstrations, I’ll use a Jupyter notebook in Google Colab.

Prerequisites

You don’t need to be an expert developer to create AI agents. Numerous no-code tools can guide you, but to fully benefit from the examples here—and to verify your agents’ behavior—you should have:

• A general grasp of Python and what an LLM is.
• Ollama installed locally to run large language models for free.
• A Jupyter Notebook setup; Google Colab is recommended if local hardware is limited or you need cloud GPUs.

What Is an LLM?

Think of an LLM (Large Language Model) as an exceptionally well-read intern who has never left the library. It can quote, summarize, translate, and mimic virtually any style—it can write a Python script and a Shakespearean sonnet in the same breath! However, it has limitations. When uncertain, it often fabricates information with the same confidence as accurate facts—a phenomenon called hallucination. Additionally, LLMs lack memory between conversations by default and cannot act independently. For example, an LLM can explain how to send an email, but it cannot actually send one. This is where AI agents come in.

What Are AI Agents?

If an LLM is like an intern, an AI agent is that intern given a desk, a laptop, and a to-do list—plus the ability to act. An agent is essentially a combination of an LLM with tools, memory, and autonomy. It can call APIs, retrieve information, execute code, and more, all under the guidance of the LLM’s reasoning. In the prerequisites section, we noted the need for Python and Ollama; these are the building blocks to run such agents.

A Decision Algorithm for Creating Optimal AI Agents

Based on the Google/MIT paper, we can derive a systematic approach to building agent teams. The key is to start simple—often a single agent with a well-defined toolset—and only add complexity (like multiple agents or hierarchical structures) when performance metrics demand it. The paper suggests an iterative process:

1. Define the task and success criteria.
2. Build a baseline with one agent using a general-purpose model.
3. Evaluate and identify failure patterns (e.g., hallucinations, missing tools).
4. Incrementally add agents, tools, or a supervisor agent if needed.
5. Measure against the baseline using standardized evaluations.

This decision algorithm prevents over-engineering and keeps agent systems lean and effective. The three code examples that follow demonstrate this principle in action.

Three Code Examples

1. Installing Utilities, Python Libraries, and Config

We begin by setting up the environment. Install Ollama, then use a Python environment (e.g., a Jupyter notebook) to install libraries like ollama, langchain, or openai. Configure API endpoints and model names—typical for connecting to a local Ollama server or a cloud provider.

2. Starting the Ollama Server, Getting the Model and Tools

Launch the Ollama server on your machine (or use Colab with a tunnel). Download a model like llama3 or mistral. Define tools: these are functions the agent can call, such as a calculator, web search, or email sender. Each tool is registered with a description so the LLM knows when to invoke it.

3. Testing the Model

Run a few prompts to verify the model responds correctly. For example, ask it to perform a simple calculation or summarize text. This step ensures both the model and tools are working before building the agent loop.

4. Running AI Agents

Now we create an agent loop: the LLM receives a user request, decides which tool to call, executes it, feeds the result back to the LLM, and iterates until a final answer is produced. We can run one agent, or multiple agents collaborating via message passing. The decision algorithm guides us on when to scale.

Conclusion: The Future of AI Is Evals

The landscape of AI agents is evolving rapidly. The key insight from the Google/MIT paper and hands-on experiments is that evaluations (evals) drive success. Instead of guessing architectures, developers should define clear metrics, test incrementally, and iterate. Future advances will likely focus on better evaluation frameworks, automated optimization, and tools that integrate seamlessly. By following a structured approach—starting simple, adding complexity only when needed—you can build AI agent systems that actually work at scale.