Also I think current LLMs might not fit well for agent use cases in mid to long term because the RL they go through is based on input-best output methods whereas the intelligence that you need in agents is more around how to build an algorithm to achieve an objective on the fly - this requires perhaps new type of large models ( Large Agent Models ? ) which are trained using RLfD ( Reinforcement Learning from demonstration )

Also I think one of the key missing piece is a highly configurable software middle ware between Intelligence ( LLMs ), Memory ( Vector Dbs ~LTMs, STMs ), Tools and workflows across every iteration. Current agent core loop to find next best action is too simplistic. For example if core self prompting loop or iteration of an agent can be configured for the use case in hand. Eg for BabyAGI, every iteration goes through workflow of Plan, Prioritize and Execute or in AutoGPT it finds the next best action based on LTM/STM, or GPTEngineer it is to write specs > write tests > write code. Now for dev infra monitoring agent this workflow might be totally different - it would look like consume logs from different tools like Grafana, Splunk, APMs > See if it doesnt have an anomaly > if it has an anomaly then take human input for feedback. Every use case in real world has it's own workflow and current construct of agent frameworks have this thing hard coded in base prompt. In SuperAGI( https://superagi.com) ( disclaimer : Im creator of it ), core iteration workflow of agent can be defined as part of agent provisioning.

Another missing piece is notion of Knowledge. Agents currently depend entirely upon knowledge of LLMs or search results to execute on tasks, but if a specialised knowledge set is plugged to an agent, it performs significantly better.

This project came out of building an autonomous marketing app, so faced some of the challenges of using autogpt , babyagi etc in the prod

Comments URL: https://news.ycombinator.com/item?id=36417564

Points: 6

# Comments: 8

Other problem with agent is : most independent agents are capable of doing very thin slice of use case, but for complex knowledge work tasks, more often than not, one agent is not enough. You need a team of agents. We introduced a concept of Agent Clusters - which operate in master slave architecture and coordinating among themselves to complete nuanced tasks and coordinating via shared memory and shared task list.

Another big bottleneck I think is lack of a notional concept of Knowledge for Agents. We have LTM and STM, but knowledge is specialized understanding of particular class of objectives ( ecommerce customer support, Account based marketing, medical diagnostics for particular condition etc ) plugged into the agent. Currently agents leverage on the knowledge available in the LLMs. LLMs are great for intelligence, but not necessarily knowledge required for an objective. So we added concept of knowledge - which is a embedding plugged into agent apart from LTM / STM

There lot of other challenges that need to solved like agent performance monitoring, agent specific models, agent to agent communication etc to truly solve for agents deployed in production. Not sure about point mentioned in the article that they might even take over the entire stack because autonomous agentic behaviour is good for certain use cases and not for all kinds of apps.