ACM, that's the term that I'd been looking for - and your paper explains it clearly. At the end, most of LLM problems are context problems. Getting the correct knowledge into its context window without overpopulating it is the actual engineering effort for most agents. And the solution you present seems promising.
Both compaction with validation and predictive fetching are the way to go.
I do not want to write an implementation for this myself, and if Synap is that implementation, I'd like to ask you a few questions:
1. Does it work with context that's not just agent conversations, but rather documents?
2. Is it better than RAG on large dataset?
3. What does on-prem options look like?
ACM, that's the term that I'd been looking for - and your paper explains it clearly. At the end, most of LLM problems are context problems. Getting the correct knowledge into its context window without overpopulating it is the actual engineering effort for most agents. And the solution you present seems promising.
Both compaction with validation and predictive fetching are the way to go.
I do not want to write an implementation for this myself, and if Synap is that implementation, I'd like to ask you a few questions: 1. Does it work with context that's not just agent conversations, but rather documents? 2. Is it better than RAG on large dataset? 3. What does on-prem options look like?
I like to start with memory engineering then reach full system then reducing costs. This allows unlocking full potential of agents.
I also wrote a shorter preview here: https://www.maximem.ai/blog/agentic-context-management-paper