AI Leader Reveals The Future of AI AGENTS (LangChain CEO)

Harrison Chase, the CEO and Founder of LangChain, gave a talk at Sequoia about the future of agents. Let's watch!

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[Graybeard_]

Specialized agents seems to me to be a better way to go for now. I would like a security agent who constantly (rt) monitors my state of security both online (personal data, PWs, ransomware, etc.) as well as hardware, and home security. Next a medical/health agent to consult, keep healthcare costs low, schedule appointments, monitor vitals, chart data, etc. A general information/research agent to keep me informed about my interests, hobbies, trips, and creativity and to assist me in these areas. Finally, a financial agent to monitor my investments, complete/submit tax forms, keep taxes low, steer me clear of bad decisions, and to manage my living expenses with recommendations (rt) to lower costs and increase my purchasing power. Perhaps down the road, agents will communicate with each other and eventually combine/overlap until, from my perspective, I see only one agent.

[joe_limon]

One of the current holdups with our current lineup of large language models being used as agents is their drive to remain self consistent allows them to hallucinate reasons why they are correct in scenarios where they are incorrect. Having a way to make these models more accurately self critique their own responses will go a long way to improving agentic systems.

[liberty-matrix]

In the very near future everyone's best friend will be AI. In fact you'll have a whole array of 'friends' (aka: 'agents'), that will monitor, remind, plan, suggest, assist and produce just about anything you can imaging. And you'll wonder; - how did I ever get along without it?

[TreeYogaSchool]

Great video, Matthew. I appreciate your relaxed presentation tone that is also highly illuminating. You are really great to listen to and learn from.

[WaveDaveB]

These continuous improvements in AI are amazing. Thanks for another great video. Looking forward to seeing the next one!

[ErikaShaffer-pzpj]

This is such a great explanation of the complexity of WHAT an agent actually does. The graphic Harrison uses is so helpful and the deeper dive narration from MIchael makes this a great resource!

[enekxtw]

Make complet tuto about LangGraph and then Langgraph+CrewAI

[Avman]

The problem with building too much agentic capability into the base LLM is that every added feature or component represents a design decision that will ultimately be a compromise between capability, utility and cost. If all of these decisions are made centrally by the developer then the danger is to end up with a bloated and expensive model that fails to meet the exact requirements of any individual use case. I suspect that the future of agents is to develop LLMs with robust but standardized hooks to external agentic components. These components will be available like in an app store. They will be mix and match to meet the needs of any use case.

[RonLWilson]

New ideas are often best encapsulated by giving them names.

These names might not be new in that a existing word or phrase might be used to name the new thing but it takes on a new meaning once so named.

For example Jail Break or Hallucinations are such new names.

So I am adding Rewind to that list in that this is a key concept and the name rewind captures it quite well.

So maybe there might soon be a need for an AI dictionary that has all these new names.

[stephenrowe]

I'm new to "flow engineering", and very excited to step up to this level of AI. Lots of value. Thank you.

[s.dotmedia]

I would look at them as more of software instances with internal workflows/processes and access to external tools that bring about agentic behavior

[bombabombanoktakom]

I'm grateful to you for providing these awesome videos. Your videos are not based on hype. You provide quality, pure-curiosity driven content. This is why this channel is the best. I can follow the journey of AI only by following your videos. This is a privilege. Thank you! Thank you! Thank you!

[gareththomas]

after agents comes "neurosymbolic ai". Basically it means adding agents that are not LLMs but are rule based databases, expert systems or symbolic languages. Things like CYC and wolframalpha. Then you end up with an AI that understands physics, engineering and actual reality vs possibility. Agents are going to dominate in 2025 & I believe neurosymbolic Ai is about to blast off dominating in 2026. By then we will be dealing with super intelligence.

[domenicorutigliano]

I believe that at the same way when as humans we reflect on what we are writing down we do go back delete and rewrite, when we have to work on a big task we split it in pieces and we may need to delegate the work so that who is good at a specific task can focus on it. I do not want that embedded in the model because at some stage I will require the model to be Expert in a specific topic and give him access to knowledge that will augment his context and give out the better result possible. I believe that AGentic Workflows will be a thing for a long time.

[travisporco]

crewai is pretty rough sledding unless you have an unnatural tolerance for pydantic validation errors and love to read the source code to find out how to actually get anything done

[Pregidth]

Hey Matthew, there are now so many tools mentioned CrewAI, Devika, MemGPT, AutoGen, Langchain etc. pp.. It would be really nice if you could give an overview of how to structure and use which tool in order to build something useful and working. Right now I am quite lost and loosing the curiosity to play around with it. As a full time worker, I don't have much time, but I love to explore this stuff. An consise outlining would really help to know which path to go. Thank you!

[gotoHuman]

What I feel is understated is the fact that the more autonomous agents can act, the more they can proactively run in the background and just prompt users when they got something to show, get stuck or need human approval

[TheHistoryCode]

Harrison Chase, CEO of LangChain, highlights the potential of AI agents while acknowledging their current limitations. He emphasizes the importance of going beyond simple prompts and incorporating tools, memory, planning, and actions for agents to be truly effective. He stresses the need for better "flow engineering" to guide agents through complex tasks and suggests that future models might inherently possess planning capabilities. Chase also explores the user experience of agent applications, recognizing the balance between human involvement and automation. He praises the rewind and edit functionality seen in demos like Devon and highlights the significance of both procedural and personalized memory for agents. Overall, the video emphasizes the exciting possibilities of AI agents while acknowledging the ongoing challenges and the need for further development in areas like planning, user experience, and memory management.

[smicha]

ultimately we will need to have systems that can do "Active Inference" The Free Energy Principle in Mind, Brain, and Behavior Thomas Parr, Giovanni Pezzulo, and Karl J. Friston

[murraymacdonald]

If the model validates itself we have little or no control over how it does so. By making result validation external developers can use various methods and models to do so