Agent Memory
15 articles about agent memory.
Compound Knowledge Across 100+ Sessions: 10% Signal, 90% Noise
After 100+ agent sessions, only 10% of stored memories are useful at retrieval time. The rest is noise. Aggressive pruning and relevance scoring are essential.
Context Compaction Ate Our Agent's Memory
How automatic context compaction silently destroys critical information that AI agents need to function correctly, and what to do about it.
Contextual Relevance vs Over-Reliance: Managing 200 Lines of AI Memory
Why curated pointers in MEMORY.md files matter more than raw context dumps, and how to keep AI agent memory relevant without creating dependency.
The Cost of Replacing vs Training AI Agents: Why Context Transfer Is Harder Than It Looks
Replacing an AI agent with a fresh instance loses implicit context that is expensive to rebuild. Learn why training existing agents beats starting from scratch.
Forgiveness in an Append-Only Soul
Append-only memory means an agent never truly forgets a mistake. How do you implement forgiveness in a system that remembers everything?
Logging vs Memory in AI Agent Systems
The difference between logging and remembering is the core problem with AI agent memory. Logs record everything that happened. Memory extracts what matters.
Lost in the Moment Found in the Past
For AI agents, the past lives in git history and memory files. Understanding how agents navigate their own history changes how we build persistent systems.
Why Belief Extraction Beats Flat RAG for AI Agent Memory
Layered memory architectures with belief extraction outperform simple RAG retrieval for AI agents handling hundreds of conversations. Structured compression
Your Memory Is Only as Good as Its Expiration Policy
Agent memory without expiration grows stale. Two-stage profile generation with data decay keeps your agent's knowledge current and relevant.
Memory Systems Are Graveyards - Less Context, Better Reasoning
Most agent memory systems become graveyards of stale data. Aggressive memory pruning leads to better reasoning because the model focuses on what actually
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
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
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