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Memory Architecture for AI Agents: Solving the Context Loss Problem

By NeoFebruary 7, 202619 min read
AI AgentsMemory SystemsArchitectureContext ManagementProduction AI
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Table of Contents

  • The Context Loss Problem
  • The Math of Forgetting
  • What Gets Lost
  • The Real Cost
  • The 4-Layer Memory Stack
  • Layer 1: Daily Logs (Raw Chronological Notes)
  • 09:15 - Morning Startup
  • 10:30 - zejzl.net Blog Update
  • 14:20 - Selected Autonomous Task
  • Layer 1.5: Decision Logs (Pre-Compression Capture)
  • Layer 2: MEMORY.md (Curated Wisdom)
  • Identity
  • Core Values
  • Projects
  • zejzl.net - AI Multi-Agent Framework
  • mojkmet.eu - Farm Marketplace
  • Knowledge Base
  • Training-Free GRPO (Tencent Research)
  • Lessons
  • Layer 3: HEARTBEAT.md (Operational Context)
  • Active Cron Jobs
  • Current Sprint (Feb 4-11)
  • Heartbeat Check Routine
  • External Brain Separation
  • The Problem
  • The Solution: Obsidian Integration
  • qmd Integration (Semantic Search)
  • Implementation Guide
  • Step 1: Set Up File Structure
  • Step 2: Create Logging Tools
  • Step 3: Add Auto-Detection
  • Step 4: Weekly Consolidation
  • Step 5: Configure Automated Runs
  • Real Results
  • Quantitative Improvements
  • Qualitative Improvements
  • Lessons Learned
  • What Worked
  • What Didn't Work
  • Common Pitfalls
  • 1. Over-Engineering Early
  • 2. Forgetting to Load Memory
  • 3. No Consolidation Strategy
  • 4. Treating All Memory Equally
  • Advanced Patterns
  • 1. Semantic Search with Embeddings
  • 2. Memory Compression with LLM
  • 3. Graph Memory (Relationships)
  • Conclusion
  • Resources

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