Ai-Infrastructure

Long-running agents are usually given a memory system that stores facts and retrieves them by similarity, borrowed straight from RAG. We argue that’s the wrong default: cheaply re-derivable facts are net-negative to cache, while the expensive, non-re-derivable thing an agent accumulates, process, is exactly what the field under-builds. We present the Agentic Context and Protocol Ledger, validate it with two policy simulations and a four-model adversarial review, and report four empirical results on staleness, dependency-checked reuse, and the mechanisms that make imperfect dependency declaration safe.

A defense of in-database AI agents: data gravity, security, transactional isolation, backups, replication, and why pg-synapse lives inside Postgres on purpose.

“Add memory to the agent” sounds like one feature. It is six different jobs that need three different mechanisms. Here is the map, with a concrete example for each.