I built a few multi agent systems and went down a rabbit hole where I reached an important conclusion - From the perspective of the LLM, the prompt/context is the only thing that ever matters. Everything about how your agent will behave ultimately boils down to this.
I had a bunch of fancy stuff like agents collaborating by passing messages and interpreting them with their own prompts and function calls. Then I realized I could collapse all of my "agents" into one dynamic prompt that tracks state in a stupid simple text region. Passing messages around was playing in very expensive traffic at the end of the day.
This is ultimately about information and spinning up an entire matrix of "agents" to process stream of info from A to B seems quite wasteful when many clear alternatives exist.
If we are seeking emergence, then perhaps this mental model still fits better. But, for practical targeted solutions, I think it's a huge distraction.
I had a bunch of fancy stuff like agents collaborating by passing messages and interpreting them with their own prompts and function calls. Then I realized I could collapse all of my "agents" into one dynamic prompt that tracks state in a stupid simple text region. Passing messages around was playing in very expensive traffic at the end of the day.
This is ultimately about information and spinning up an entire matrix of "agents" to process stream of info from A to B seems quite wasteful when many clear alternatives exist.
If we are seeking emergence, then perhaps this mental model still fits better. But, for practical targeted solutions, I think it's a huge distraction.