I’m building an agentic solution to a problem (monitoring social media and news sources, then building world views of different participants).
A single agent would have insufficient context window size to achieve this in one API call, which means I need parallel agents. Then I have to consolidate the parallel outputs in a way that correctly updates state. I feel like multi-agent is the only way to solve this.
Effectively I’m treating the agents as threads and the roles as functions, with one agent managing writing state to avoid shared state surprises. Thinking of it with the actor model (= mailboxes) makes orchestration fairly straightforward and not really much more complex than the way we already build distributed/multi-threaded applications so I was wondering if I was missing something about why this would be an issue just because the implementation is an LLM prompt instead of a typical programming language.
I’m building an agentic solution to a problem (monitoring social media and news sources, then building world views of different participants).
A single agent would have insufficient context window size to achieve this in one API call, which means I need parallel agents. Then I have to consolidate the parallel outputs in a way that correctly updates state. I feel like multi-agent is the only way to solve this.
Effectively I’m treating the agents as threads and the roles as functions, with one agent managing writing state to avoid shared state surprises. Thinking of it with the actor model (= mailboxes) makes orchestration fairly straightforward and not really much more complex than the way we already build distributed/multi-threaded applications so I was wondering if I was missing something about why this would be an issue just because the implementation is an LLM prompt instead of a typical programming language.