Building Effective AI Agents

This article remains one of the better pieces on this topic, especially since it clearly defines which definition of "AI agents" they are using at the start! They use: "systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks".

I also like the way they distinguish between "agents" and "workflows", and describe a bunch of useful workflow patterns.

I published some notes on that article when it first came out: https://simonwillison.net/2024/Dec/20/building-effective-age...

A more recent article from Anthropic is https://www.anthropic.com/engineering/built-multi-agent-rese... - "How we built our multi-agent research system". I found this one fascinating, I wrote up a bunch of notes on it here: https://simonwillison.net/2025/Jun/14/multi-agent-research-s...

I believe the definition of workflows in this article is inaccurate. Workflows in modern engines do not take predefined code paths, and agents are effectively the same as workflows in these cases. The redefinition of workflows seems to be an attempt to differentiate, but for the most part an agent is nothing more than a workflow that is a loop that dynamically invokes things based on LLM responses. Modern workflow engines are very dynamic.

I think the distinction is more about the "level of railroading".

Workflows have a lot more structure and rules about information and control flow. Agents, on the other hand, are often given a set of tools and a prompt. They are much more free-form.

For example, a workflow might define a fuzzy rule like "if customer issue is refund, go to refund flow," while an agent gets customer service tools and figures out how to handle each case on its own.

To me, this is a meaningful distinction to make. Workflows can be more predictable and reliable. Agents have more freedom and can tackle a greater breadth of tasks.