This article explores the concept of an "agent harness," the essential software infrastructure that wraps around a Large Language Model (LLM) to enable autonomous, goal-directed behavior. While foundation models provide the core reasoning capabilities, the harness manages the orchestration loop, tool integration, memory, context management, state persistence, and error handling. The author breaks down the eleven critical components of a production-grade harness, drawing insights from industry leaders such as Anthropic, OpenAI, and LangChain. By comparing the harness to an operating system and the LLM to a CPU, the piece provides a technical framework for understanding how to move from simple demos to robust, production-ready AI agents.
This article provides a hands-on coding guide to explore nanobot, a lightweight personal AI agent framework. It details recreating core subsystems like the agent loop, tool execution, memory persistence, skills loading, session management, subagent spawning, and cron scheduling. The tutorial uses OpenAI’s gpt-4o-mini and demonstrates building a multi-step research pipeline capable of file operations, long-term memory storage, and concurrent background tasks. The goal is to understand not just how to *use* nanobot, but how to *extend* it with custom tools and architectures.
This article details a tutorial on building cybersecurity AI agents using the CAI framework. It guides readers through setting up the environment with Colab, loading API keys, and creating base agents. The tutorial progresses to advanced capabilities, including custom function tools, multi-agent handoffs, agent orchestration, input guardrails, and dynamic tools.
It demonstrates how CAI transforms Python functions and agent definitions into flexible cybersecurity workflows capable of reasoning, delegating, validating, and responding in a structured way. The article also showcases CTF-style pipelines, multi-turn context handling, and streaming responses, offering a comprehensive overview of CAI's potential for security applications.
It demonstrates how CAI transforms Python functions and agent definitions into flexible cybersecurity workflows capable of reasoning, delegating, validating, and responding in a structured way. The article also showcases CTF-style pipelines, multi-turn context handling, and streaming responses, offering a comprehensive overview of CAI's potential for security applications.
OpenAI has expanded its Responses API to facilitate the development of agentic workflows. This includes support for a shell tool, an agent execution loop, a hosted container workspace, context compaction, and reusable agent skills. The new features aim to offload the complexities of building execution environments from developers, providing a managed infrastructure for handling tasks like file management, prompt optimization, secure network access, and handling timeouts.
A core component is the agent execution loop, where the model proposes actions (running commands, querying data) that are executed in a controlled environment, with the results fed back to refine the process. Skills allow for the creation of reusable task patterns.
A core component is the agent execution loop, where the model proposes actions (running commands, querying data) that are executed in a controlled environment, with the results fed back to refine the process. Skills allow for the creation of reusable task patterns.
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.
A new MIT study reveals a significant lack of transparency and safety measures in agentic AI systems, with many offering no disclosure about risks or ways to shut down rogue bots.
ClawRouter is an agent-native LLM router empowering OpenClaw. It enables smart routing with 15-dimension scoring, <1ms local routing, and is optimized for autonomous agents. It supports 30+ models and non-custodial payments with x402.
sudo for AI agents - allow, deny, or ask before any tool runs. AI agents run tools autonomously, but some calls are too risky to run unchecked. agentpriv gives you a permission layer to control what goes through.
This article discusses the impact of Anthropic's Claude Code, an AI agent that is significantly impacting software development and the broader information work economy. It analyzes Claude Code's capabilities, its potential to drive revenue growth for Anthropic, the challenges it poses for Microsoft, and the shift in competition within the AI landscape.
Simon Willison’s annual review of the major trends, breakthroughs, and cultural moments in the large language model ecosystem in 2025, covering reasoning models, coding agents, CLI tools, Chinese open‑weight models, image editing, academic competition wins, and the rise of AI‑enabled browsers.
