Context vs. Memory Engineering: Solving Different Problems in Agentic Systems
blaizedsouza · x · 2026-08-12
The article explores how context engineering and memory engineering address distinct challenges in agentic AI systems:
- Context Engineering: Designs a single inference call. It decides what enters the window, how it is compressed, and where it is placed. Everything inside the window is temporary and clears when the call ends.
- Memory Engineering: Designs what survives across calls and sessions. It covers write policies, storage choices, retrieval strategies, and maintenance, determining what gets stored, trusted, and retrieved.
The two disciplines intersect at retrieval time. Memory systems surface candidate information, and context assembly then decides whether it enters the prompt, its volume, and its position. A strong system treats these as separate responsibilities that must be tightly coordinated.
More from coding & agent
- DHH merges PR to bypass permissions for coding agents in omarchy, says treating AGI like a toddler is wrong — mitsuhiko · 2026-08-12
- The Evolution of AI Engineering: From Prompt to Graph Engineering — mattturck · 2026-08-12
- Use /delegate to Seamlessly Hand Off Work Between AI Agents and Teammates — jacob_posel · 2026-08-12
- AI Agent Autonomously Writes Firmware Update to Extract Hardware Encryption Keys — CtrlAltDwayne · 2026-08-12
- OpenAI Now Blocks Hack Forcing LLMs to Output Tool Calls in User Messages — mitsuhiko · 2026-08-12
- Architecting Persistent Memory for Enterprise AI Assistants — Ok_Mud_2004 · 2026-08-12