A curated collection of resources, patterns, and templates for building reliable scaffolding for agents. Harness engineering is the discipline of designing the systems surrounding an agent—such as context delivery, tool interfaces, planning artifacts, verification loops, memory systems, and sandboxes—that determine its success or failure on real tasks. This focus is on the harness rather than the model.
- Design primitives for loops, planning, and memory
- Reference implementations and tutorials
- Security, sandboxing, and permissions
- Evaluation, verification, and observability
- Task runners and orchestration
- Human-in-the-loop and production operations
- Design primitives for loops, planning, and memory
- Reference implementations and tutorials
- Security, sandboxing, and permissions
- Evaluation, verification, and observability
- Task runners and orchestration
- Human-in-the-loop and production operations
The article discusses how integrating Anthropic's Claude Code persistent memory into automation workflows creates more personalized and efficient processes. By using the Claude Code CLI within an automation layer rather than relying solely on standard API calls, users can leverage Auto Memory and CLAUDE.md files to provide deep project context without manual prompt bloating. This approach enables smarter code repository management, automated documentation updates that reflect actual implementation changes, and more intelligent homelab monitoring. The author also distinguishes these memory features from the Model Context Protocol (MCP), which is better suited for fetching frequently changing data from external tools like GitHub or Notion.
Key topics:
- Claude Code's persistent memory via Auto Memory and CLAUDE.md
- Advantages of CLI implementation over standard API calls in workflows
- Practical applications in code repositories, documentation, and homelab environments
- Comparison between project memory and Model Context Protocol (MCP)
Key topics:
- Claude Code's persistent memory via Auto Memory and CLAUDE.md
- Advantages of CLI implementation over standard API calls in workflows
- Practical applications in code repositories, documentation, and homelab environments
- Comparison between project memory and Model Context Protocol (MCP)
GitNexus is an advanced code intelligence engine designed to act as a "nervous system" for AI agents. By indexing entire codebases into a comprehensive knowledge graph, it maps dependencies, call chains, and execution flows, ensuring that tools like Cursor and Claude Code have deep architectural awareness. The platform offers two primary modes: a CLI with Model Context Protocol (MCP) support for seamless integration into developer workflows, and a browser-based Web UI for quick, serverless exploration via WebAssembly. Unlike traditional Graph RAG, GitNexus utilizes precomputed relational intelligence to provide high-confidence impact analysis, multi-file renames, and automated wiki generation, significantly reducing the risk of breaking changes during AI-driven development.
The Model Context Protocol (MCP) is becoming a key component in the agentic AI space, enabling models to interact with external tools and data. The project's 2026 roadmap focuses on addressing challenges for production deployment. Key priorities include improving scalability by evolving the transport and session model, clarifying agent communication and task lifecycle management, maturing governance structures for wider community contribution, and preparing for enterprise requirements like audit trails and authentication. The roadmap also highlights ongoing exploration of areas like event-driven updates and security.
Developers are replacing bloated MCP servers with Markdown skill files — cutting token costs by 100x. This article explores a two-layer architecture emerging in production AI systems, separating knowledge from execution. It details how skills (Markdown files) encode stable knowledge, while MCP servers handle runtime API interactions. The piece advocates for a layered approach to optimize context window usage, reduce costs, and improve agent reasoning by prioritizing knowledge representation in a version-controlled, accessible format.
This article discusses the latest developments in AI agents, including the launch of Perplexity Computer, the shift from 'vibe coding' to 'agentic engineering', the standardization efforts around AI agents, and OpenAI's new deal with the Pentagon after Anthropic was dropped.
* **Multi-Agent Desktops Expand:**
* Perplexity launches "Computer" – easy-use digital worker.
* Notion & Anthropic boost agent capabilities via plugins.
* **Agent Standards Emerge:**
* Anthropic releases "Agent Skills" repository (GitHub).
* OpenAI adopts similar architecture.
* Agentic AI Foundation forming for standardization.
* **Agentic Engineering Takes Hold:**
* Karpathy: "Vibe coding" outdated.
* Focus shifts to code understanding & agent steering.
* **Cloudflare Optimizes for Agents:**
* "Markdown for Agents" reduces token usage on webpages.
* No website owner code changes needed.
* **Pentagon Shifts AI Partners:**
* Pentagon stops using Anthropic products (values concerns).
* OpenAI wins Pentagon deal – stipulations on surveillance/weapons.
* Potentially weaker safeguards than Anthropic.
* **Multi-Agent Desktops Expand:**
* Perplexity launches "Computer" – easy-use digital worker.
* Notion & Anthropic boost agent capabilities via plugins.
* **Agent Standards Emerge:**
* Anthropic releases "Agent Skills" repository (GitHub).
* OpenAI adopts similar architecture.
* Agentic AI Foundation forming for standardization.
* **Agentic Engineering Takes Hold:**
* Karpathy: "Vibe coding" outdated.
* Focus shifts to code understanding & agent steering.
* **Cloudflare Optimizes for Agents:**
* "Markdown for Agents" reduces token usage on webpages.
* No website owner code changes needed.
* **Pentagon Shifts AI Partners:**
* Pentagon stops using Anthropic products (values concerns).
* OpenAI wins Pentagon deal – stipulations on surveillance/weapons.
* Potentially weaker safeguards than Anthropic.
This article explains the differences between Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and AI Agents, highlighting that they solve different problems at different layers of the AI stack. It also covers how ChatGPT routes prompts and handles modes, agent skills, architectural concepts for developers, and service deployment strategies.
Google is introducing the Web Model Context Protocol (WebMCP) to allow AI agents to interact with websites in a more efficient and reliable way, moving away from screen scraping. This protocol enables direct communication between websites and AI models, defining website capabilities for AI access through HTML attributes or JavaScript APIs. The Early Preview Program (EPP) is being used to refine the protocol and gather data. WebMCP offers lower latency, higher accuracy, and reduced costs compared to traditional methods.
Nanobot is an open-source MCP host for building agents, enabling flexible deployment and integration into applications. It supports single file and directory-based configurations with providers like OpenAI and Anthropic.
This article details authentication and authorization mechanisms within the Model Context Protocol (MCP), covering transport layers like stdio and Streamable HTTP, OAuth flows, and security considerations for MCP servers.
