Building Effective "Agents"

This is by far the most practical piece of writing I've seen on the subject of "agents" - it includes actionable definitions, then splits most of the value out into "workflows" and describes those in depth with example applications.

There's also a cookbook with useful code examples: https://github.com/anthropics/anthropic-cookbook/tree/main/p...

Blogged about this here: https://simonwillison.net/2024/Dec/20/building-effective-age...

Yes, they have actionable definitions, but they are defining something quite different than the normal definition of an "agent". An agent is a party who acts for another. Often this comes from an employer-employee relationship.

This matters mostly when things go wrong. Who's responsible? The airline whose AI agent gave out wrong info about airline policies found, in court, that their "intelligent agent" was considered an agent in legal terms. Which meant the airline was stuck paying for their mistake.

Anthropic's definition: Some customers define agents as fully autonomous systems that operate independently over extended periods, using various tools to accomplish complex tasks.

That's an autonomous system, not an agent. Autonomy is about how much something can do without outside help. Agency is about who's doing what for whom, and for whose benefit and with what authority. Those are independent concepts.

AI people have been using a much broader definition of 'agent' for ages, though. One from Russel and Norvig's 90s textbook:

"Anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators"

https://en.wikipedia.org/wiki/Intelligent_agent#As_a_definit...

That definition feels like it's playing on the verb, the idea of having "agency" in the world, and not on the noun, of being an "agent" for another party. The former is a philosophical category, while the latter has legal meaning and implication, and it feels somewhat disingenuous to continue to mix them up in this way.