Most AI agents treat each request as an isolated transaction: they receive input, generate an answer, and then discard everything that just happened. This forces users to repeat context, re‑explain goals, and manually stitch together information across conversations. The result is wasted time, inconsistent answers, and a brittle experience for anyone who relies on AI for ongoing work—developers, analysts, support teams, or knowledge workers who need the model to remember past interactions, documents, and decisions.
Google Cloud’s Always‑On Memory Agent solves this by turning memory into a continuously running process rather than a one‑shot call. Built with the Agent Development Kit (ADK) and powered by Gemini 3.1 Flash‑Lite, the agent never stops. It stores facts in a lightweight SQLite database—no vector store, no embeddings, no extra infrastructure.
The workflow splits into three specialist sub‑agents orchestrated by a simple router:
IngestAgent – Whenever new content arrives (a file, snippet, or message), the agent reads it multimodally, extracts a concise summary, entities, topics, and an importance score, then writes that structured record to the memories table.
ConsolidateAgent – On a timer (default every 30 minutes) it reviews the unconsolidated memories, finds links between them, and writes a synthesized insight back to the database. This background “sleep‑cycle” builds higher‑level understanding without any user prompt.
QueryAgent – When a question is asked, it reads all stored memories and any consolidation insights, then generates an answer that cites the exact memory IDs used as sources. The response is grounded solely in what the agent has retained.
Because the agent runs 24/7, latency stays low and cost remains predictable—ideal for continuous background tasks like monitoring logs, tracking project notes, or maintaining a personal knowledge base. Users no longer need to paste the same context repeatedly; the agent remembers, connects, and recalls on demand.
Try the sample in the generative‑ai repository to see how a persistent memory layer transforms fleeting chats into a reliable, evolving assistant.