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Bigger problem might be using agents in the first place.

We did some testing with agents for content generation (e.g. "authoring" agent, "researcher" agent, "editor" agent) and found that it was easier to just write it as 3 sequential prompts with an explicit control loop.

It's easier to debug, monitor, and control the output flow this way.

But we still use Semantic Kernel[0] because the lowest level abstractions that it provides are still very useful in reducing the code that we have to roll ourselves and also makes some parts of the API very flexible. These are things we'd end up writing ourselves anyways so why not just use the framework primitives instead?

[0] https://github.com/microsoft/semantic-kernel




What's the difference? I thought "agents" was just a fancier word for sequential prompts.


Typically, the term "agents" implies some autonomous collaboration. In an agent workflow, the flow itself is non-deterministic. One agent can work with another agent and keep cycling between themselves until an output is resolved that meets some criteria. An agent itself is also typically evaluating the terminal condition for the workflow.


Some folks try to orchestrate the whole operation by a higher level prompt that essentially uses function calls to more specific prompts.

Versus just using the LLM’s for specific tasks and heuristics / own code for the orchestration.

But I agree there is a lot of anthropomorphizing that over states current model capabilities and just confuses things in general.


It's also used to mean "characters interacting with each other" and sort of message passing between them. Not sure but I get the sense thats what the author is using it as


Some "agents" like the minecraft bot Voyager(https://github.com/MineDojo/Voyager) have a control loop, they are given a high level task and then they use LLM to decide what actions to take, then evaluate the result and iterate. In some LLM frameworks, a chain/pipeline just uses LLM to process input data(classification, named entitiy extraction, summary, etc).


Sequential prompts with an occasional cron job


"Agent" means that it outputs JSON with a function call name and parameters which you execute and usually then feed the results back to the LLM.


What does semantic kernel do for you? It isn't immediately obvious from the Readme.


SK does a lot of the same things that Langhain does at a high level.

The most useful bits for us are prompt templating[0], "inlining" some functions like `recall` into the text of the prompt [1], and service container [2] (useful if you are using multiple LLM services and models for different types of prompts/flows).

It has other useful abstractions and you can see the full list of examples here:

- C#: https://github.com/microsoft/semantic-kernel/tree/main/dotne...

- python: https://github.com/microsoft/semantic-kernel/tree/main/pytho...

---

[0] https://github.com/microsoft/semantic-kernel/blob/main/dotne...

[1] https://github.com/microsoft/semantic-kernel/blob/main/dotne...

[2] https://github.com/microsoft/semantic-kernel/blob/main/dotne...


I'm not OP, but it's just C#/.NET glue and "sample" code for Azure, OpenAI, and a few others (if I were to generously describe it).

It doesn't actually "do" anything or provide useful concepts. I wouldn't use it for anything, personally, even to read.




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