“Agent” is likely going to be the word that will cause existential dread to true LLM enthusiasts.
Everyone’s got a different idea of what it means. In our modern age of innovation theater, lots of organizations gleefully slap the “agentic” label on anything that vaguely resembles a regular program (and pocket tons of money). Even a simple HTTP call to an LLM-as-a-Service can be called an agent, if you try desperately hard enough.
The internet, as always, is flooded with “groundbreaking” tutorials on building these so-called agents. Often authored by the latest hypefluencers, they typically involve a few lines (probably generated by whatever coding assistant is currently trending on Hacker News) that compose LangChain and an Ollama instance, often being presented as the pinnacle of AI autonomy. Because why bother with actual innovation when you can just repeat the quasi-boilerplate code ad nauseam?
That’s why I liked it a lot when the Anthropic article, Building effective agents, came out, as it dares to suggest that simply bolting on retrieval or memory to an LLM does not, in fact, make an agent. And chaining or routing? That’s just glorified control flow, folks. Only when an LLM is tasked with truly complex, real-world tasks such as coding or using a computer, does it begin to resemble the autonomous agent we’ve been promised
So how do you identify a real agent? Don’t be fooled by the grand pronouncements of those rearranging deck chairs on the Titanic. Ask for the receipts of successful evaluations! Anecdotal evidence of a few successful LLM calls isn’t that useful. Remember, in the world of LLMs, as in life, the loudest claims are often the emptiest!