By: Logan Kilpatrick
Re-posted from: https://logankilpatrick.medium.com/the-future-of-ai-agents-with-yohei-nakajima-2602e32a4765?source=rss-2c8aac9051d3------2
Delving into AI agents and where we are going next
The future is going to be full of AI agents, but there are still a lot of open questions on how to get there & what that world will look like. I had the chance to sit down with one of the deepest thinkers in the world of AI agents, Yohei Nakajima. If you want to check out the video of our conversion, you can watch it on YouTube:
Where are we today?
There has been a lot of talk of agents over the last year since the initial viral explosion of HustleGPT, where the creator famously told the chatbot system that it had $100 and asked it to try and help him make money for his startup.
Since then, the conversation and interest around agents has not stopped, despite there being a shockingly low number of successful agent deployments. Even as someone who is really interested in AI and has tried many of the agent tools, I still have a grand total of zero agents actually running in production right now helping me (which is pretty disappointing).
Despite the lack of large scale deployments, companies are still investing heavily in the space as it is widely assumed this is the application of LLMs that will end up providing the most value. I have been looking more and more into Zapier as the potential launching point for large scale agent deployments. Most of the initial challenge with agent platforms is they don’t actually hook up to all the things you need them too. They much support Gmail but not Outlook, etc. But Zapier already does the dirty work of connecting with the worlds tools which gets me excited about the prospect this could work out as a tool.
Why haven’t AI agents taken off yet?
To understand why agents have not taken off, you need to really understand the flow that autonomous agents take when solving tasks. I talked about this in depth when I explored what agents were in another post from earlier last year. The TLDR is that current agents typical use the LLM system itself as the planning mechanism for the agent. In many cases, this is sufficient to solve a simple task, but as anyone who uses LLMs frequently knows, the limitations for these planners are very real.
Simply put, current LLMs lack sufficient reasoning capabilities to really solve problems without human input. I am hopeful this will change in the future with forthcoming new models, but it might also be that we need to move the planning capabilities to more deterministic systems that are not controlled by LLMs. You could imagine a world where we also fine-tune LLMs to specifically perform the planning task, and potentially fine-tune other LLMs to do the debugging task in cases where the models get stuck.
Beyond the model limitations, the other challenge is tooling. Likely the closest thing to a widely used LLM agent framework is the OpenAI Assistants API. However, it lacks many of the true agentic features that you would need to really build and autonomous agent in production. Companies like https://www.agentops.ai/ and https://e2b.dev are taking a stab at trying to provide a different layer of tooling / infra to help developers building agents, but these tools have not gained widespread adoption.
Where are we going from here?
The agent experience that gets me excited is the one that is spun up in the background for me and just automates away some task / workflow I used to do manually. It still feels like we are a very long way away from this, but many companies are trying this using browser automation. In those workflows, you can perform a task once and the agent will learn how to mimic the workflow in the browser and then do it for you on demand. This could be one possible way to decrease the friction in making agents work at scale.
Another innovation will certainly be at the model layer. Increased reasoning / planning capabilities, while coupled with increased safety risks, present the likeliest path to improved adoption of agents. Some models like Cohere’s Command R model are being optimized for tool use which is a common pattern for agents to do the things they need. It is not clear yet if these workflows will require custom made models, my guess is that general purpose reasoning models will perform the best in the long term but the short term will be won by tool use tailored models.