https://huggingface.co/docs/smolagents/en/conceptual_guides/intro_agents
- AI Agents are programs where LLM outputs control the workflow
✅When to use agents / ⛔ when to avoid them
Agents are useful when you
need an LLM to determine the workflow of an app. But they’re often
overkill. The question is: do I really need flexibility in the workflow
to efficiently solve the task at hand? If the pre-determined workflow
falls short too often, that means you need more flexibility. Let’s take
an example: say you’re making an app that handles customer requests on a
surfing trip website.
You could know in advance that the
requests will belong to either of 2 buckets (based on user choice), and
you have a predefined workflow for each of these 2 cases.
Want some knowledge on the trips? ⇒ give them access to a search bar to search your knowledge base
Wants to talk to sales? ⇒ let them type in a contact form.
If
that deterministic workflow fits all queries, by all means just code
everything! This will give you a 100% reliable system with no risk of
error introduced by letting unpredictable LLMs meddle in your workflow.
For the sake of simplicity and robustness, it’s advised to regularize
towards not using any agentic behaviour.
But what if the workflow can’t be determined that well in advance?
For
instance, a user wants to ask: "I can come on Monday, but I forgot my
passport so risk being delayed to Wednesday, is it possible to take me
and my stuff to surf on Tuesday morning, with a cancellation insurance?"
This question hinges on many factors, and probably none of the
predetermined criteria above will suffice for this request.
If the pre-determined workflow falls short too often, that means you need more flexibility.
That is where an agentic setup helps
nice course
- https://huggingface.co/learn/agents-course/en/unit0/introduction
- https://www.anthropic.com/engineering/building-effective-agents
- https://edwarddonner.com/2025/04/21/the-complete-agentic-ai-engineering-course/
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