Udemy - Agentic Harness Engineering - Harness Design for AI Engin...
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Agentic Harness Engineering: Harness Design for AI Engineers https://WebToolTip.com Published 7/2026 Created by Fikayo Adepoju MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 23 Lectures ( 4h 35m ) | Size: 2.4 GB Harness Engineering for Long-Running, Code Executing, and Persistent AI Agents like Claude Code, Codex, and OpenClaw What you'll learn ⚡ Build a Complete Harness: Design and implement a multi-layered agent harn
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Agentic Harness Engineering: Harness Design for AI Engineers
https://WebToolTip.com
Published 7/2026
Created by Fikayo Adepoju
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 23 Lectures ( 4h 35m ) | Size: 2.4 GB
Harness Engineering for Long-Running, Code Executing, and Persistent AI Agents like Claude Code, Codex, and OpenClaw
What you'll learn
⚡ Build a Complete Harness: Design and implement a multi-layered agent harness from scratch using raw Python and custom execution loops.
⚡ Implement Multi-Session Memory: Utilize the AGENTS[dot]md memory file standard alongside vector-indexed retrieval for persistent, cross-session recall.
⚡ Defeat Context Rot: Implement advanced compaction hooks and tool call offloading middleware to sustain model performance over long runs.
⚡ Secure Code Execution: Engineer isolated Docker sandboxes with execution timeouts, command allow-lists, and restricted outbound networks.
⚡ Optimize with LangSmith Tracing: Build a rigorous evaluation harness to profile agent traces, diagnose failures, and measure benchmark pass rates.
⚡ Orchestrate Long-Horizon Tasks: Deploy the "Ralph Loop" to intercept premature agent exits and enforce goal-driven, autonomous continuity.
Requirements
❗ Python Proficiency: Strong comfort with advanced Python syntax, file handling, and structural logic.
❗ LLM Foundations: Basic familiarity with Large Language Models, chat APIs, and the fundamental mechanics of prompting.
❗ Environment Tools: Comfort using the command line (Bash) and a local development machine with Docker installed for sandboxing.