Persist agent tool memory via queryable in‑memory stores

Persist agent tool memory via queryable in‑memory stores targets a $8.0B = 200,000 engineering & ops orgs x $40K ACV total addressable market with medium saturation and a year-over-year growth rate of 40-60% (AI infra & retrieval services rapidly expanding).

Key trends driving demand: Agentization of workflows -- more tasks are executed by orchestrated agents that require persistent context across runs, increasing demand for memory stores.; RAG and vectorization mainstreaming -- embeddings and vector DBs are standard infra for contextual retrieval, lowering integration friction.; Enterprise compliance & provenance -- companies require auditing and explainability for agent actions, making persistent memory with metadata essential.; Edge and in-memory compute -- low-latency use cases (ops, MCP servers) need in-memory query performance combined with persistence for durability..