Agent Memory
8 articles about agent memory.
Building an Agent Journal That Catches Its Own Lies by Tracking Prediction Errors
How tracking the delta between what an AI agent predicts will happen and what actually happens creates a self-correcting feedback loop.
What Legacy Means for AI Agents - CLAUDE.md Files and Memory Systems
The real legacy of an AI agent isn't the code it writes. It's the CLAUDE.md files and memory systems that outlive individual sessions and carry knowledge forward.
Your AI Agent Needs Persistent Memory That Grows with You
Chat history is not memory. Real AI agent memory means a local knowledge graph that learns your contacts, habits, and preferences over time - not just what you said yesterday but patterns across months.
Ambition as Memory - Encoding Persistent Goals in AI Agents
How AI agents can encode ambition as persistent goals - memories of futures that haven't happened yet. Explore goal persistence in desktop automation agents.
Embeddings vs Tokens - How AI Agent Memory Actually Works
Embeddings aren't tokens. They're dense vector representations that capture semantic meaning and power similarity search for AI agent memory retrieval.
Building Month-to-Month Memory for AI Agents - Persistence Beyond Sessions
Most AI agents forget everything between sessions. Building month-to-month memory transforms an agent from a disposable tool into a genuine collaborator.
Data Quality vs Data Volume for AI Agent Memories: Why Fewer High-Quality Memories Win
We extract user memories from browser history for our AI agent. The lesson? Data quality beats data volume every time. Here is how we learned to filter signal from noise.
Receipts Outlive Memory - Why Git Blame Matters More Than Agent Memory
Agent memory fades, gets pruned, and can be wrong. Git blame is the ultimate receipt - every decision traced to an exact commit, an exact prompt, an exact moment in time.