Building Agent Memory and Self-Prompting Systems
iamrobotbear · x · 2026-07-18
The post discusses a new paradigm for AI Agent memory management. The core idea is that developers shouldn't manually write massive amounts of rules, but rather cultivate and optimize prompts like "tending a garden."
Furthermore, citing an in-depth share from an Anthropic engineer, it points out that managing Agent memory as files is the next major focus. The key to building an efficient Agent lies in creating a self-prompting system, which involves: evolving from basic config files to Agents autonomously writing their own memories; solving memory limits and attention divergence at scale; introducing a "dreaming" mechanism for Agents to review errors and self-evolve during downtime; and ensuring system stability through engineering practices like version control and concurrency management.
Related event: From Prompts to a Knowledge Layer: Running AI Agents as Work Systems(5 posts)→
More from coding & agent
- Coding agents are heading toward an AI-writes, AI-reviews, human-approves workflow — aftahi_ai · 2026-07-22
- oMLX 0.5.2 adds Mac menu-bar stats, low-bit decode kernels, and faster downloads — awnihannun · 2026-07-22
- GitHub review bot hits its PR limit and forces a 39-minute cooldown — DanielLockyer · 2026-07-22
- Max reasoning effort appears to be mobile-only in Codex Remote, not desktop — GabGarrett · 2026-07-22
- A Reddit demo argues online stores should expose carts and pricing through MCP — gelembjuk · 2026-07-22
- Open-source AI SDK provider routes Vercel apps through a local Codex subscription — lgrammel · 2026-07-22