Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks
https://www.anthropic.com/engineering/building-effective-age...While I know it's a marketing term, I think a good distinction is that agents have a loop in the execution graph, and can choose whether to loop or keep going. Workflows are chained LLM calls where the LLM has no "choice".
However, I now realize that most of these steps don't require AI at all, let alone agents. I wrote the full algorithm (including the binary search!) in natural language for the LLM. And although it sometimes worked, the model often misunderstood and produced random errors out of the blue.
I now realize that this is not what agents are for. This problem didn't require any agentic behavior. It was just a fixed workflow, with one single AI step (generating a markdown report text).
Oh well, nothing wrong with learning the hard way.
My only gripe is that the models are still pretty slow, and that discourages iteration and experimentation. I can’t wait for the day a Claude 3.5 grade model with 1000 tok/s speed releases, this will be a total game changer for me. Gemini 2.5 recently came closer, but it’s still not there.
The product itself is exciting and solves a very real problem, and we have many customers who want to use it and pay for it. But damn, it hurts my soul knowing what goes on under the hood.