Code for this episode → https://g.dev/ai/fashion-mind
In this episode, you'll learn the critical difference between Structured Facts and Semantic Understanding, why they should never live in the same database, what to do with raw audio/video perception, and how to read and write memory asynchronously without blocking latency-sensitive voice streams.
Watch along and learn:
* Short-Term (RAM) vs. Long-Term Agent Memory
* Facts (Exact Lookup) vs. Understanding (Semantic Recall)
* Why dumping exact facts into a vector store breaks your agent
* Where to store Structured Memory (PostgreSQL, MySQL, Google Cloud SQL) vs. Semantic Memory (pgvector, AlloyDB, Vertex AI Agent Engine Memory Bank)
* How to handle raw multimodal perception (camera frames & audio waveforms)
* he Two-Track Read/Write Loop: Synchronous tool calls for facts vs. asynchronous off-the-hot-path extraction for understanding
Chapters:
00:00 - Intro: What should a multimodal agent remember?
00:25 - Demo: Personal styling agent with long-term memory
00:53 - Short-term (RAM) vs. Long-term memory
01:28 - The 2 types of memory: Facts vs. understanding
02:29 - Where memory lives: SQL databases vs. vector stores
03:40 - Vector store options & managed memory bank
04:30 - Common mistake: Putting everything in a vector store
05:24 - What about raw perception? (Audio & video streams)
06:40 - How agents read memory at session start
07:38 - Saving memory: Fast sync writes vs. async extraction
08:44 - Keeping memory extraction off the live hot path
10:42 - Architecture recap & next episode: Real time vision
💬 Question of the day:
What is the first thing you would want your AI agent to remember about you? Drop your answer in the comments below!
🔔 Subscribe and stay tuned for the next episode, where we give the agent eyes with real-time vision!
Resources:
Gemini Live API docs → https://g.dev/cloud/gemini-live
Google Cloud SQL docs → https://g.dev/cloud/sql-docs
AlloyDB docs → https://g.dev/cloud/alloydb-docs
Vertex AI Agent Engine Memory Bank → https://g.dev/ai/scale-your-agents
Follow Annie online:
LinkedIn → https://goo.gle/annie-linkedin
X → https://goo.gle/annie-x
Watch more The Omni App → https://g.dev/cloud/modern-ai-app
🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
#AIAgents #MultimodalAI #SoftwareArchitecture #VectorDatabase #GoogleCloud #LLM #MachineLearning #AgenticAI #CloudSQL #VertexAI
Speaker: Annie Wang
Products Mentioned: Google Cloud SQL, AlloyDB, Vertex AI Agent Engine Memory Bank, Gemini Live API
In this episode, you'll learn the critical difference between Structured Facts and Semantic Understanding, why they should never live in the same database, what to do with raw audio/video perception, and how to read and write memory asynchronously without blocking latency-sensitive voice streams.
Watch along and learn:
* Short-Term (RAM) vs. Long-Term Agent Memory
* Facts (Exact Lookup) vs. Understanding (Semantic Recall)
* Why dumping exact facts into a vector store breaks your agent
* Where to store Structured Memory (PostgreSQL, MySQL, Google Cloud SQL) vs. Semantic Memory (pgvector, AlloyDB, Vertex AI Agent Engine Memory Bank)
* How to handle raw multimodal perception (camera frames & audio waveforms)
* he Two-Track Read/Write Loop: Synchronous tool calls for facts vs. asynchronous off-the-hot-path extraction for understanding
Chapters:
00:00 - Intro: What should a multimodal agent remember?
00:25 - Demo: Personal styling agent with long-term memory
00:53 - Short-term (RAM) vs. Long-term memory
01:28 - The 2 types of memory: Facts vs. understanding
02:29 - Where memory lives: SQL databases vs. vector stores
03:40 - Vector store options & managed memory bank
04:30 - Common mistake: Putting everything in a vector store
05:24 - What about raw perception? (Audio & video streams)
06:40 - How agents read memory at session start
07:38 - Saving memory: Fast sync writes vs. async extraction
08:44 - Keeping memory extraction off the live hot path
10:42 - Architecture recap & next episode: Real time vision
💬 Question of the day:
What is the first thing you would want your AI agent to remember about you? Drop your answer in the comments below!
🔔 Subscribe and stay tuned for the next episode, where we give the agent eyes with real-time vision!
Resources:
Gemini Live API docs → https://g.dev/cloud/gemini-live
Google Cloud SQL docs → https://g.dev/cloud/sql-docs
AlloyDB docs → https://g.dev/cloud/alloydb-docs
Vertex AI Agent Engine Memory Bank → https://g.dev/ai/scale-your-agents
Follow Annie online:
LinkedIn → https://goo.gle/annie-linkedin
X → https://goo.gle/annie-x
Watch more The Omni App → https://g.dev/cloud/modern-ai-app
🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
#AIAgents #MultimodalAI #SoftwareArchitecture #VectorDatabase #GoogleCloud #LLM #MachineLearning #AgenticAI #CloudSQL #VertexAI
Speaker: Annie Wang
Products Mentioned: Google Cloud SQL, AlloyDB, Vertex AI Agent Engine Memory Bank, Gemini Live API