Agentic Context Management: Memory and Cost as Architecture Problems

Re:Agentic Context Management: Memory and Cost as Architectu... by samyakk on Wednesday August 26, 2026 @12:08AM (#49444129)

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?

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Re:Agentic Context Management: Memory and Cost as Architectu... by gdad on Wednesday August 26, 2026 @02:27AM (#49444818)

Thanks Samyakk!

1. Yes, works on docs, agent conversations, human-conversations from different sources (Slack, JIRA, etc.). We have connectors for some of these as well; so it is plug and play 2. conventional RAG recall accuracy is quite low (50-60%) and latency is pretty high (seconds). But worst is the precision; you end up context stuffing to get acceptable recall 3. We do offer on-prem deployments, but only on sizeable annual contracts

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