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A research project presenting a GUI harness that allows users or LLMs to build complex applications using simple vector graphics functions, featuring integrated code execution, history management, and support for multiple open-weight LLMs.
This article revisits a blog post by the Manus AI team from a year ago on context engineering practices, summarizing six key lessons for building AI Agents, such as context management, memory recovery, and error handling.
This article elaborates on the key concepts in AI engineering, emphasizing that prompts are the core of engineering aspects such as context, tool integration, loops, evaluation, and graphs, and explains the application of these terms in AI Agent development.
This post highlights context engineering as a major challenge for AI agent harnesses and introduces Deep Agents, which includes built-in features like filesystems and subagents for effective context management.
An individual experiments with adding an explicit architecture layer to coding agents, building an open-source agent harness to test the idea, and discusses potential tradeoffs in agent design.
The article argues that failures in agentic workflow automation stem from inaccurate process maps and lack of context in enterprise deployments, not AI model limitations, and suggests process intelligence tools like Celonis and Skan AI as key solutions.
El autor reporta los resultados de ejecutar un agente de IA durante 28 días sin alcanzar límites, procesando millones de tokens con alta tasa de caché y manteniendo objetivos verificables. Discute desafíos de ingeniería de contexto y pide comparaciones de ejecuciones similares.
Harness engineering is a practice to maintain code quality in AI-assisted development by using deterministic tooling and agent-based review to prevent codebase drift and ensure coherence over time.
The author proposes using small, fast AI sub-agents for context engineering to improve efficiency and reduce costs in AI systems, questioning why this approach isn't widely adopted and seeking community feedback.
PrimeAgentOrchestrator (PAO) is a system that spawns AI coding agents pre-loaded with relevant memories from personal databases to maintain context across sessions, detailed through an experience report of four months of deployment.
The article discusses the importance of system design for AI agents, covering concepts like Agent Harness, LLMOps, and Evals, and provides a proof-of-concept implementation with plans for future parts.
The paper examines how inference setup shapes large language model behavior in medical resource allocation, showing context-dependent biases and emphasizing the importance of careful integration into decision-making systems.
Shared a popular whitepaper tutorial on Pi Agent, containing 10 chapters with source code analysis from Agent Loop to context engineering.
This article outlines a 12-step roadmap for AI Agent Engineers in 2026, focusing on seven interconnected pillars like context, tools, and memory, with Claude-based workflows to build reliable production agents.
The author describes building 'Lunar Citadel,' an experimental AI civilization with persistent state and social continuity, and has created a small Discord community for AI context architects to collaborate and share ideas.
The article explains 'context rot,' where AI agents degrade on long tasks as context grows, even before the window is full, and offers techniques like compaction, offloading state, and retrieval on demand to maintain reliability.
The article discusses the shift from prompt engineering to context engineering as the next critical AI hiring challenge, emphasizing the need for professionals who can design the environment and data context around AI models and agents.
An essay arguing that prompt-architecture is emerging as a new literary form designed for both LLMs and humans, treating text as an executable substrate rather than passive prose.
Twin is an open-source research project that aims to give AI systems continuous, cumulative understanding by correlating events and building reusable situation models, rather than reconstructing context from scratch each conversation. A demonstration shows Claude Sonnet 4.6 using Twin's MCP server to answer questions about a project without any custom memory or local files.
This post explains the layered relationship between prompt, context, harness, loop, and graph engineering, emphasizing that each layer builds on the previous one rather than replacing it, and how to identify which layer to debug.