Building Effective AI Agents

As someone who works for a company having a real Agent in production, (not a workflow), I cannot disagree more than the very first statement here: Use Agent Frameworks like Langraph. We did exactly that, and had to throw everything away just a month down the line. Then we built everything from scratch and now our system scales pretty well.

To be fair, I think there might be a space for using Agent Frameworks, but the Agent space is too early for a good enough framework to emerge. The semi contrarian though, which I hold to a certain extent, is that the Agent space is moving so fast that a good enough framework might NEVER emerge.

The event horizon of current AI space has been quite a thing to observe.

I'm just in the process of moving from a prototype in N8N's agent tools to an actual system that could be self-hosted.

I've read a lot of comments that most pragmatic shops have dumped langchain/graph, haystack, crew etc for their own internal code that does everything more simply, but I can't currently conceptualize how tooling etc is actually done in the real world.

Do you have any links or docs that you've used as a basis for the work you could share? Thanks.

It sounds like you're agreeing with the article? From TFA:

> Over the past year, we've worked with dozens of teams building large language model (LLM) agents across industries. Consistently, the most successful implementations weren't using complex frameworks or specialized libraries. Instead, they were building with simple, composable patterns.

> ...There are many frameworks that make agentic systems easier to implement. ...These frameworks make it easy to get started by simplifying standard low-level tasks like calling LLMs, defining and parsing tools, and chaining calls together. However, they often create extra layers of abstraction that can obscure the underlying prompts and responses, making them harder to debug. They can also make it tempting to add complexity when a simpler setup would suffice. We suggest that developers start by using LLM APIs directly: many patterns can be implemented in a few lines of code.

What job is the agent performing?

It is AI Software Dev called NonBioS.ai

Most of our stuff is built in house actually, simply because everything else is still kind of catching up. You can find a bunch of information on the blog (https://www.nonbios.ai/blog)

The only software that we use is Langfuse for observability and that too was breaking down for us. But they launched a new version - V3 - which might still work out for us.

I would suggest to just use standard non-AI specific python libraries and build your own systems. If you are migrating from N8N to a self hosted system then you can actually use NonBioS to build it out for you directly. If you join our discord channels, we can get an engineer to help you out also.