AI Agent Memory & Context Management Guide

AI Agent Memory & Context Management Guide

Your AI agent has a finite context window. Conversations grow, context fills up, and the agent starts forgetting important details. This guide covers four layers of memory — from system prompts to external storage — with concrete patterns.

What You Get

• 4-layer memory architecture — system prompt → session context → compressed summaries → external storage
• Context budgeting system — allocate tokens per layer with safety margins
• Compression triggers — exactly when and how to summarize
• Prioritization heuristics — what to keep vs. discard
• Memory hygiene routines — daily, weekly, monthly maintenance
• Decision flow — choose the right storage layer for any fact
• Compression templates — ready-to-use formats

Sections Covered

1. System Prompt (Layer 1) — What belongs in always-on context
2. Session Context (Layer 2) — Active working memory management
3. Compressed Summaries (Layer 3) — Sliding window, semantic gist, decision log
4. External Storage (Layer 4) — Key-value, structured facts, graph relationships
5. Context Budgeting — Per-layer allocation and monitoring
6. Prioritization Heuristics — Score and rank what to remember
7. Memory Hygiene — Keep memory lean and accurate

Perfect For

• Developers whose agents lose track of long conversations
• Anyone hitting context limits
• Teams building agents that persist knowledge across sessions