| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1. Introduction.mp4 | 52.2 MB | ||
| 2 - Agent Harness - All the Parts | |||
| 10. Harness Component 5 - The Memory.mp4 | 32.6 MB | ||
| 11. Harness Component 6 - The Observability.mp4 | 24.3 MB | ||
| 12. How Every Harness Component Connects Together.mp4 | 25.3 MB | ||
| 13. Decomposing a Real-World Harness - Claude Code.mp4 | 95 MB | ||
| 3 - Designing the Harness Conversation Loop | |||
| 14. Harness Project Setup.mp4 | 86.3 MB | ||
| 15. Writing the Bare Conversation Loop.mp4 | 107.6 MB | ||
| 16. Using Kimi Models - A Powerful Cheaper Alternative.mp4 | 76.8 MB | ||
| 17. System Prompt as a First Harness Primitive.mp4 | 195.8 MB | ||
| 18. What's Missing - Mapping the Gaps.mp4 | 104.7 MB | ||
| 4 - The File System Layer | |||
| 19. Why we need a File System first.mp4 | 16.4 MB | ||
| 20. Building the File System Abstraction.mp4 | 322.5 MB | ||
| 21. Adding Git Support for Versioning.mp4 | 199.8 MB | ||
| 22. Injecting Durable Memory at Session Start with AGENTS.md.mp4 | 168.9 MB | ||
| 23. Hands-On Task Multi-step Research and Report.mp4 | 167.7 MB | ||
| 3. The Raw Model Problem.mp4 | 116.3 MB | ||
| 4. Demo - Exposing the Gaps.mp4 | 123.6 MB | ||
| 5. Defining the Harness - The 6 Core Components.mp4 | 90.2 MB | ||
| 6. Harness Component 1 - The Loop Architecture.mp4 | 84.9 MB | ||
| 7. Harness Component 2 - The Tools.mp4 | 76.5 MB | ||
| 8. Harness Component 3 - The Context.mp4 | 183 MB | ||
| 9. Harness Component 4 - The Environment.mp4 | 98.1 MB | ||
| 2. Course Materials.html | 5.4 KB |
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.
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