Persistent AI-agent memory — hybrid retrieval + summarized long-term store

Large engineering and product teams building autonomous agents struggle to maintain reliable, cost-effective long-term context: session token limits, high LLM spend when re-fetching verbose histories, drifted or inconsistent summaries, and latency when scanning growing vector indices. This problem is acute at mid and large enterprises—the target TAM we estimate at 250,000 organizations (approximately $30.0B annual opportunity at a $120K ACV)—where agents must preserve multi-week or multi-month state across users, workflows, and integrations. You could build a hybrid memory platform that pairs fast vector retrieval for short-term, high-recall context with an automated, time-aware summarized long-term store that compresses and indexes events by relevance, freshness, and responsibility. Core features would include managed embeddings and hosted vector DB integrations, automated progressive summarization to reduce payloads, selective retrieval policies that trade recall for token cost (estimated token spend reductions of roughly 30–50% depending on workload), developer SDKs for event hooks and policy controls, and enterprise-grade security/compliance. The product would expose APIs and UI for auditability, divergence detection, and per-customer tuning to avoid model drift and ensure deterministic behavior. This market is attractive now because agentification of workflows, rising RAG maturity, and hosted embeddings/vector DBs remove infrastructure friction and make memory a direct ROI lever for autonomous systems; adoption dynamics favor a platform with clear cost and latency benefits. To stand out you need a developer-first product that enforces reproducible summaries, provides strong SLAs for recall/latency, and embeds cost-aware retrieval policies—strengths that are feasible but require tackling hard engineering problems around incremental summarization, evaluation metrics, and multi-tenant correctness.