context

A developer describes building a multi-agent voice social-deduction game, solving turn-taking with a central conductor but struggling with shared memory and preserving social subtext when compressing conversation history into structured state.

Explores whether analytics agents should incorporate contextual data from tools like Linear, Sentry, and Notion, or remain purely metrics-driven.

An opinion piece arguing that AI agents in enterprises need a structured 'Company Brain' memory layer to reliably access context, policies, and permissions, rather than relying solely on RAG and tool access.

Zaro allows users to build agents and apps on top of their context with a single prompt.

The author shares lessons from designing a testing framework in Guile, focusing on how adding context to test definitions makes tests more reusable and improves developer experience.

A discussion about the focus of AI evaluations, questioning whether practitioners are optimizing prompts, context, or the entire harness, and noting a shift toward holistic optimization.

This is the sixth article in the series, explaining in detail the concept of subagent, its working principles, and its role in coding agents, including tool call and runtime mechanisms, as well as the applicable scenarios of different subagent types (fresh child, forked child, partial fork).

The article argues that coding agents need continuity—preserving execution history and project state in the repository—rather than simply larger memory or context windows, to avoid losing the operational thread between sessions.

Google announced the Open Knowledge Format, an open standard based on Karpathy's LLM wiki concept, designed to provide context for AI agents using simple markdown files.

Google's Open Knowledge Format (OKF) proposes a portable standard for organizational knowledge to help AI agents retrieve correct context, addressing fragmentation across data catalogs, wikis, and code.

The author argues that AI will not replace engineers because experienced engineers hold crucial context from real-world production failures and edge cases, and knowing what to build remains a human skill. Factory AI is building tools to augment engineers.

Google shares a free, comprehensive example of a long-running AI agent that pauses, resumes, and never loses context, simulating new employee onboarding, teaching three architectural patterns.

Building multi-agent systems reveals that managing shared memory and context consistency is more challenging than orchestration. The author's experiment using Statewave treats memory as an evolving lifecycle rather than a retrieval problem.

The article presents 'Lifting E-Graphs', a refined approach to e-graphs that explicitly encodes the context (dimension) of functions to resolve issues with variable naming, missed sharing, and accidental over-sharing, based on a semantic model of functions from R^n to R.

Discusses the challenge of persistent memory for personal AI agents across sessions, comparing setups like Custom GPTs, Mem, and Open Campus's shared memory approach, and asks for community recommendations on handling memory conflicts.

A reflective inquiry into the practical gaps and motivations behind personalized AI agents, exploring where current systems fail to 'know' users and the boundary between helpful personalization and a surrogate self.

Nessie is a tool that allows users to transfer their context, memory, and history from ChatGPT, Perplexity, and Gemini to other platforms, including OpenClaw and the Hermes Agent, using OpenClaw and MCP servers.

This paper investigates the ability of LLMs-as-judges for safety to adapt to contextual information and varying safety definitions, finding that they are largely rigid and fail to adjust when the context contradicts their internal priors.

Graphify is a tool that helps trace hidden couplings, navigate important files, and provide better context for AI agents.

Walrus Memory allows AI agents to retain context and operate seamlessly across different applications and sessions.