This article explains the concept of an agent harness—a user-owned software layer that provides environment, system prompts, tools, an agentic loop, and a translation layer for AI models—using a climbing harness analogy, and argues that open-source, neutral harnesses like Pi empower user autonomy over AI.
The article explains why swapping the Harness — the runtime environment around an AI model — changes agent behavior even when the model weights stay identical, covering system prompts, tool definitions, agentic loops, translation layers, execution boundaries, feedback fidelity, context compression vs. immutable event logs, and architectural trade-offs illustrated by Pi, Codex, DSH, and the claudex experiment.
This article explains Harness as a software runtime that gives AI models a workspace — system prompt, tools, an agentic loop for self-directed reasoning, and a translation layer to swap models — using Pi Agent as a concrete example of a thin, user-extensible harness with over 5,000 community extensions.
This article traces Claude Code agent interactions to reveal how natural language queries are converted into search commands via agentic tool-driven loops, highlighting the impact of tool configuration and LLM reasoning quality on code location accuracy.
This article walks through Claude Code’s Loop Engineering concepts—turn‑based, goal‑based, time‑based, and proactive loops—using concrete Fable 5 demos, detailed step‑by‑step instructions, and cost‑quality tips for building reliable AI‑driven automation.
This lesson explains how Harness uses Subagents—isolated execution units created via the Task tool—to offload heavy, multi‑step operations into separate context windows, custom prompts, and limited toolsets, and how to configure built‑in and custom Subagents with the Claude Code SDK.
The article explains Claude Code's definition of loops, breaks down four loop types—manual, goal‑based, timed, and proactive—provides concrete command examples, best‑practice tips for code quality and token management, and shows how to choose the right loop for your workflow.
This guide explains how to build reliable, provider‑neutral AI agent harnesses for production by covering the agentic loop, tool and permission management, context compaction, security evaluations, budgeting, and deployment considerations, and provides an open‑source skill with ready‑to‑use artifacts.
The article explains the two main AI Agent Loop patterns—human‑in‑the‑loop and fully autonomous agentic loops—highlights the hidden costs, product‑drift risks, and budget limits of the latter, and provides concrete, low‑risk scenarios and a step‑by‑step code‑review loop that keeps humans in control.
The Orchestrator pattern reduces LLM calls from seven to two, cutting latency from 4.2 s to 1.1 s and cost by about 70%, by separating routing and synthesis from deterministic execution and supporting single, parallel, and sequential agent strategies.
This article explains what an agentic loop is, contrasts it with single‑pass chatbots, outlines its five stages, shows a visual architecture, walks through a concrete multi‑step example, provides Python pseudocode and a LangChain implementation, and discusses when to use or avoid such loops.
The article breaks down Claude Code’s persistent agentic loop into four layers—QueryEngine, Tool System, Permission/Hook, and React + Ink—explaining how each turn gathers context, makes model decisions, executes actions, verifies results, handles errors, and renders a terminal UI.
This article explains the concept of Conditional Edge in LangGraph, shows how to add conditional edges with three parameters, demonstrates rule‑based, multi‑branch, and loop routing patterns, compares rule‑based versus LLM‑based routing, provides a complete customer‑service agent example, and lists common pitfalls and best‑practice checklists.
This guide explains what an AI Agent is, how it differs from a chatbot, the importance of tools and prompt design, common pitfalls, multi‑agent coordination, and practical steps to build, monitor, and deploy production‑grade agents.
Coding Agents have evolved from simple line‑by‑line autocomplete tools into autonomous developers that can read entire repositories, plan tasks, execute commands, run tests, and iteratively refine code, driven by a model‑tool loop, extensible memory layers, skills, sub‑agents, and integration hooks, while requiring careful supervision and context engineering.
