This Anthropic article explains approaches to building reliable AI agents, including capabilities and safety considerations.
How to Build Optimal AI Agents That Actually Work
This handbook discusses how to build AI agents for software developers.
About How to Build Optimal AI Agents That Actually Work
This handbook, written by Tiago Capelo Monteiro, addresses how to organize teams of AI agents for real business use. It starts by explaining large language models (LLMs) and their limits, such as hallucination and lacking memory between conversations by default. It then describes AI agents as LLMs combined with tools, memory, and a loop that lets the model act, check results, and continue until a task is complete.
The central question is how to structure a team of agents: how many to use, whether to use a supervising "boss" agent or peer-to-peer coordination, and whether a single agent is enough. The article builds on a 2025 paper from Google Research, Google DeepMind, and MIT, which reports over 150 controlled experiments. The author presents its findings as experimental rather than definitive, and notes that more agents do not always produce better results. The article also includes a decision algorithm and three code examples in a Jupyter notebook that use Ollama to run models locally. Google Colab is recommended for readers with limited local hardware. Readers should understand basic Python and LLMs to get the most from the examples.
Key features
- Explains LLM limitations, including hallucination and lack of memory between conversations
- Defines AI agents as LLMs combined with tools, memory, and a loop
- Compares single-agent setups with independent, boss-led, and peer-to-peer multi-agent teams
- Presents a decision algorithm for choosing an agent team structure
- Includes three code examples in a Jupyter notebook
- Uses Ollama to run language models locally
Best for: Software developers who understand Python and want to decide how to structure teams of AI agents.
Good for
- Grounds its guidance in a named research paper with over 150 controlled experiments
- States that its findings are experimental rather than universal laws
- Uses Ollama, which the article describes as free for running models locally
Watch out for
- Readers need a general understanding of Python and LLMs to get the most from the examples
- Running the examples locally requires a machine that can run Ollama; Google Colab is recommended for limited hardware
- Model-family results come from one study, and the article suggests them only as strong guidelines
How to Build Optimal AI Agents That Actually Work at a glance
- Category
- Education & Learning › AI Learning & Guides
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Android, iOS, Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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How to Build Optimal AI Agents That Actually Work FAQ
Do I need to be an expert developer to build AI agents?
No. The article states that you do not need to be an expert developer and that many no-code tools can help. Getting the most from its code examples does require a general understanding of Python and LLMs.
What do I need to run the code examples?
The article lists a general understanding of Python and LLMs, Ollama installed on your machine, and a Jupyter Notebook setup. Google Colab is recommended if you have limited local hardware or need cloud GPUs.
Which model providers does the research compare?
The article names OpenAI, Google, and Anthropic. It reports that results differed by model family: OpenAI models gained most from centralized or hybrid setups, Google models showed an efficiency plateau, and Anthropic models were more sensitive to coordination overhead.
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