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Building Effective AI Agents

(www.anthropic.com)
543 points Anon84 | 2 comments | 17 Jun 25 17:50 UTC | HN request time: 0.633s | source
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simonw ◴[17 Jun 25 18:59 UTC] No.44302601[source]▶
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...

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kodablah ◴[18 Jun 25 01:44 UTC] No.44305898[source]▶
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.
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sothatsit ◴[18 Jun 25 02:25 UTC] No.44306120[source]▶
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.

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1. gwd ◴[18 Jun 25 11:18 UTC] No.44308850[source]▶
Just to emphasize your point, below is a workflow I wrote for an LLM recently, to do language tagging (e.g., of vocab, grammar structures, etc). It's very different than what you'd think of as an "agent", where the LLM has tools and can take initiative.

LLMs are amazingly powerful in some ways, but without this kind of "scaffolding", simply not reliable enough to make consistent choices.

---

1. Here are: a) a "language schema" describing what kinds of tags I want and why, with examples, b) The text I want you to tag c) A list of previously-defined tags which could potentially be relevant (simple string match)

List for yourself which pre-existing tags you plan to use when doing tagging.

[LLM generates a list of tags]

2. Here is a,b,c from above, and d) your own tag list

Please write a draft tag.

[LLM writes a draft]

3. Here is a-d from above, plus e) your first draft, and f) Some programmatically-generated "linter" warnings which may or may not be violations of the schema.

Please check over your draft to make sure it follows the schema.

[LLM writes a new draft]

Agent checks for "hard" rules, like making sure there's a 1-1 correlation between the text and the tags. If no rules are violated move to step 5.

4. Here is a-e from above, plus g) your most recent draft, and h) known rule violations. Please fix the errors.

[LLM writes a new draft]

Repeat 4 until no hard rules are broken.

5. [and so on]

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2. ◴[18 Jun 25 16:49 UTC] No.44311446[source]▶