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> "Many vendors are contributing to the hype by engaging in 'agent washing' – the rebranding of existing products, such as AI assistants, robotic process automa

by baobun 1y ago

> "Many vendors are contributing to the hype by engaging in 'agent washing' – the rebranding of existing products, such as AI assistants, robotic process automation (RPA) and chatbots, without substantial agentic capabilities," the firm says. "Gartner estimates only about 130 of the thousands of agentic AI vendors are real." I'd expect 100% of "agentic AI" to be hype. It's a meaningless term because almost any long-running software with an execution engine can qualify. What really differentiates "real agentic" from "slapped IFTT and an LLM together"?

admjs 1y ago
Think of execution steps as nodes in a graph. IFTT has pre-defined execution paths through the graph, its determinist. Agents design the execution path on the fly for the most contextually appropriate solution, it's non-deterministic. Both are state machines and DAGs.
- cube00 1y ago
  > Agents design the execution path on the fly for the most contextually appropriate solution Sounds great but given the exact same context they don't design the same path each time.
- baobun 1y ago
  Sure, but the point is that AI-driven data pipelines with executive parts have been commonplace for well over a decade. What's a network of trading bots instructed by ML-models fed by market data and various signals if not agentic AI already? Price-settings systems for airline tickets? Spambots? The word "agentic" doesn't bring anything new. We've always been doing this.
digitcatphd 1y ago
All these arguments remind me of early LLMs. In fact, most of the arguments are nearly identical. Heck, even Bitcoin back 10 years ago was demonized and now governments are pushing for national reserves. If you have used GPT o3, you have used Agentic models. If you use Claude Code or Cursor, you have used Agentic models. If you have used Claude + MCP, you have used agentic models. This isn't theoretical - its already being applied. In fact, Langchain just hosted an entire conference to showcase how Fortune 100 companies were using LangGraph and I can assure you if they could have used a more simple architecture they would have. If you wait around until everyone considers it to be acceptable in the mainstream all the good opportunities to create disruptive tech will be gone. So please - keep publishing this luddite trash and throw shade on the sidelines. I will keep doing agent research and building with it. We will see in 10 years who was wrong I guess. https://www.qodo.ai/blog/building-agentic-flows-with-langgraph-model-context-protocol/ https://www.qodo.ai/blog/building-agentic-flows-with-langgra...
- octopoc 1y ago
  I agree with the overall point but I do think there's a better approach than langchain. Langchain is a framework, not a library. I think a library approach is better at this point because libraries can be composed, which supports fundamental shifts in the structure of the code using the library. Frameworks can't be composed--that's the whole point of having a framework. Agentic patterns are so early that frameworks are limiting and we should be choosing libraries instead. For example, there was a paper recently (unfortunately I can't remember which it was) that talked about how if you support pausing generation and allowing the human to edit the generated text, then resuming, you can get much better results than if you force the human to respond to the AI as a separate message. That's something that is more difficult for a framework to adapt to than for a library to adapt to. I'm building an agentic framework that is more on the library side of things, and it necessarily leaves control of the iteration in the hands of the developer. The developer literally writes a while loop to run the agent, offloading things like RAG and tool calling to different pieces of the library. But every part of the iteration is controlled by the developer's agent source code.
  - digitcatphd 1y ago
    Makes sense, yes, I think a spectrum of tools will be useful depending on the build. N8N, LangGraph, and working with primitives all have their use case and then tools built on each will be useful much like the landscape of RAG builds have evolved.

by baobun 1y ago

admjs 1y ago

Think of execution steps as nodes in a graph. IFTT has pre-defined execution paths through the graph, its determinist. Agents design the execution path on the fly for the most contextually appropriate solution, it's non-deterministic. Both are state machines and DAGs.

cube00 1y ago
> Agents design the execution path on the fly for the most contextually appropriate solution Sounds great but given the exact same context they don't design the same path each time.
baobun 1y ago
Sure, but the point is that AI-driven data pipelines with executive parts have been commonplace for well over a decade. What's a network of trading bots instructed by ML-models fed by market data and various signals if not agentic AI already? Price-settings systems for airline tickets? Spambots? The word "agentic" doesn't bring anything new. We've always been doing this.

digitcatphd 1y ago

All these arguments remind me of early LLMs. In fact, most of the arguments are nearly identical. Heck, even Bitcoin back 10 years ago was demonized and now governments are pushing for national reserves. If you have used GPT o3, you have used Agentic models. If you use Claude Code or Cursor, you have used Agentic models. If you have used Claude + MCP, you have used agentic models. This isn't theoretical - its already being applied. In fact, Langchain just hosted an entire conference to showcase how Fortune 100 companies were using LangGraph and I can assure you if they could have used a more simple architecture they would have. If you wait around until everyone considers it to be acceptable in the mainstream all the good opportunities to create disruptive tech will be gone. So please - keep publishing this luddite trash and throw shade on the sidelines. I will keep doing agent research and building with it. We will see in 10 years who was wrong I guess. https://www.qodo.ai/blog/building-agentic-flows-with-langgraph-model-context-protocol/ https://www.qodo.ai/blog/building-agentic-flows-with-langgra...

octopoc 1y ago
I agree with the overall point but I do think there's a better approach than langchain. Langchain is a framework, not a library. I think a library approach is better at this point because libraries can be composed, which supports fundamental shifts in the structure of the code using the library. Frameworks can't be composed--that's the whole point of having a framework. Agentic patterns are so early that frameworks are limiting and we should be choosing libraries instead. For example, there was a paper recently (unfortunately I can't remember which it was) that talked about how if you support pausing generation and allowing the human to edit the generated text, then resuming, you can get much better results than if you force the human to respond to the AI as a separate message. That's something that is more difficult for a framework to adapt to than for a library to adapt to. I'm building an agentic framework that is more on the library side of things, and it necessarily leaves control of the iteration in the hands of the developer. The developer literally writes a while loop to run the agent, offloading things like RAG and tool calling to different pieces of the library. But every part of the iteration is controlled by the developer's agent source code.
- digitcatphd 1y ago
  Makes sense, yes, I think a spectrum of tools will be useful depending on the build. N8N, LangGraph, and working with primitives all have their use case and then tools built on each will be useful much like the landscape of RAG builds have evolved.