You spend forty minutes “teaching” an AI agent your brand voice, your product specs, and your weird obsession with Oxford commas. It performs brilliantly. Then, you start a new session, and it greets you with the blank, vacant stare of a goldfished chatbot.
This is the Memory Wall. Most AI workflows fail not because the models are stupid, but because they are amnesiacs. We’re building complex business logic on top of a “clear all” button, and it’s the single biggest reason AI feels like a toy rather than a teammate.
🧠 Concept of the Week
In AI land, “memory” is a spectrum of hacks. To build anything that lasts, you need to know which one to pull:
Context Window: This is “short-term memory.” It’s huge now (millions of tokens), but it’s still ephemeral. When the session ends, the memory dies. Relying on this for long-term logic is like trying to run a company using only sticky notes.
Vector DB (RAG): The “library card” approach. The AI searches a massive database for relevant facts. Great for “What is our travel policy?”, terrible for “How do I usually like my coffee?”
In-Prompt Memory: Hard-coding instructions into your system prompt. It’s reliable but brittle. If you change your workflow, you have to manually update the “brain.”
Recursive Memory: This is the holy grail. It’s the agent writing its own notes to a file or database after a task, then reading those notes before the next one starts.
🔧 Tool of the Week: Letta (formerly MemGPT)
If you want to stop the goldfish effect, look at Letta.
While most tools treat memory as an afterthought, Letta treats the LLM like an operating system. It manages “evolving state” — meaning the agent can actually update its own long-term memory based on what it learns from you. It’s opinionated, slightly technical, and currently the best way to build an agent that actually gets smarter the more you talk to it.
⚙️ The 5-Minute Pattern
You don’t need a vector database to start. Use the “State Log” Pattern for your repetitive tasks:
1. Create a simple markdown file titled MEMORY.md in your project folder.
2. At the end of every AI session, ask: “Summarize the key decisions, style preferences, and progress we made today into 5 bullet points for our MEMORY.md file.”
3. At the start of your next session, upload that file and say: “Read our MEMORY.md. This is your continuity. Act accordingly.”
It’s low-tech, but it creates a persistent feedback loop that survives a session reset.
📢 One Thing to Try This Week
What’s the one thing you’re tired of re-explaining to your AI every single morning? Reply to this email and let me know — I’ll pick the best ones to solve in a future issue.
Stay sharp,
— The Node