Agent Memory Patterns

The Shape of Memory Benchmarks

Why the familiar memory benchmarks are outdated, how the agent-native work looks today and why design your own.

Memory in Agents: What, Why and How

LLM memory gives language models persistent context across sessions. Learn how it works, how it differs from RAG and context windows, and how to add LLM memory to your agents with Mem0.

How coding agents work - Agentic Engineering Patterns

How coding agents work - Agentic Engineering Patterns

Writing about Agentic Engineering Patterns

I’ve started a new project to collect and document Agentic Engineering Patterns—coding practices and patterns to help get the best results out of this new era of coding agent development …

Agentic Engineering Patterns - Simon Willison's Weblog

Patterns for getting the best results out of coding agents like Claude Code and OpenAI Codex. See my introduction for more on this project.

LukeW | Agent Management Interface Patterns

As an increasing number of AI applications evolve to agents doing work for people, agent management becomes a critical part of these product's design. How can p...

Supermemory

The memory layer for AI agents. Context engineering platform powering enterprise APIs, developer plugins, and a personal app that remembers everything.

Wiki Memory

Memory for agents is still early, with little to no standards. “Memory” means something different to everyone. But one common pattern is emerging: wiki memory.

Ambient Associative Memory

Most agent memory waits to be queried. Ambient memory runs on every tool call — past lessons surface on their own, no rules list required.

Agent Skills Overview - Agent Skills

A standardized way to give AI agents new capabilities and expertise.

Agentic manual testing - Agentic Engineering Patterns

Agentic manual testing - Agentic Engineering Patterns

The Anatomy of an Agent Harness

Learn how agent harnesses transform AI models into autonomous work engines. Explore core components: filesystems, sandboxes, and memory.

Agent Skills

AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...

Building napkin - a memory system for agents

A decade of information retrieval and three years of agent harness engineering, poured into a local-first knowledge system that avoids vector search entirely.

Memory Models: Towards Agents That Learn

Agents that truly learn from experience will be powered by memory models: models that create and curate token-space memory across model generations, trained with memory-native RL.