Description
Agentic Harness Engineering: Harness Design for AI Engineers is a course on how to design, build, and optimize harnesses for autonomous AI agents and multi-agent systems published by Udemy Online Academy. Designed for AI engineers, machine learning specialists, software developers, and platform engineers, this course focuses on creating robust execution environments that enable AI agents to securely and efficiently interact with tools, APIs, external data sources, and complex workflows. Through a rigorous, step-by-step curriculum, you will gradually build a complete, production-ready infrastructure layer from scratch using Python and Docker. You will start by building a robust conversational skeleton and a secure file system layer using a versioned Git workspace and custom memory patterns. From there, you will progress to creating a secure code execution engine inside isolated Docker sandboxes, incorporating advanced self-asserting test loops and network isolation.
Students will learn harness architecture, agent tuning, rapid management, tool integration, memory management, evaluation pipelines, observability, security, and performance optimization. Finally, you will implement parallel sub-agent generation and advanced “Ralph Loop” to create automated persistence. To complete your architectural mastery, you will connect LangSmith to build an evaluation harness and run optimizations against live benchmarks. Stop struggling with raw model constraints and start engineering highly autonomous agent systems built for the real world.
What you will learn in Agentic Harness Engineering: Harness Design for AI Engineers:
- Implementing Multi-Session Memory: Use the AGENTS[dot]md memory file standard alongside vector-indexed retrieval for persistent and cross-session invocation.
- Context Rot Failure: Implement advanced compression hooks and tool call dump middleware to maintain model performance over the long term.
- Safe Code Execution: Engineering isolated Docker sandboxes with limited execution times, whitelists, and output networks.
- Optimization with LangSmith Tracing: Building a rigorous evaluation harness to profile agent traces, detect failures, and measure benchmark pass rates.
- Long-Horizon Task Coordination: Deploying a “Ralph Loop” to catch premature agent exits and perform targeted, independent persistence.
- And more…
Course Details
Publisher: Udemy
Instructor: Fikayo Adepoju
Language: English
Level: Beginner
Number of Lessons: 29
Time: 4 hours and 41 minutes
Course Topics
Agentic Harness Engineering: Harness Design for AI Engineers Prerequisites
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.
Course Images
Agentic Harness Engineering Course : Harness Design for AI Engineers Introduction Video
Installation Guide
After Extract, watch with your favorite Player.
Subtitles: None
Quality: 1080p
Downloadly Link
Rapidgator Link
File(s) Password: www.downloadly.ir
File Size
2.3 GB
