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Keep shoppingAgentic Harness Engineering: Harness Design for AI Engineers
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Agentic Harness Engineering: Harness Design for AI Engineers
(Currently on Drip Schedule) Harness Engineering for Long-Running, Code Executing, and Persistent AI Agents
Created byFikayo Adepoju
Last updated 8/2026
English
English [Auto],
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.
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Course content
10 sections • 38 lectures • 7h 21m total length
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.
Description
Welcome to Agentic Harness Engineering: Harness Design for AI Engineers, the definitive, production-grade masterclass for developers ready to build the infrastructure that makes artificial intelligence truly useful. As Vivek Trivedy of LangChain noted, "The model contains the intelligence. The harness is the system that makes that intelligence useful." While most developers are stuck building fragile, prompt-dependent wrappers, this course focuses on the system design discipline of agent engineering—teaching you how to design, build, and optimize a custom agent harness from scratch.
Through a rigorous, step-by-step curriculum, you will incrementally build a complete, production-ready infrastructure layer from scratch using Python and Docker. You'll start by constructing a robust conversation skeleton and a secure filesystem layer using a versioned Git workspace and custom memory patterns. From there, you will escalate to creating a secure code execution engine inside isolated Docker sandboxes, incorporating advanced self-verification test loops and network isolation.
As your agents take on long-horizon tasks, you will engineer cutting-edge context management systems—including compaction hooks, tool call offloading, and progressive tool disclosure—to actively defeat context rot. Finally, you will implement parallel subagent spawning and the advanced "Ralph Loop" to force autonomous continuation. To wrap up your architectural mastery, you will connect LangSmith to build an evaluation harness, running optimizations against live benchmarks. Stop fighting raw model limitations and start engineering high-autonomy agent systems built for the real world.
Who this course is for:
- AI Engineers and Software Developers wanting to build production-grade, highly autonomous agent infrastructure instead of fragile prompt wrappers.
- Backend and Infrastructure Engineers who need to implement secure, sandboxed code execution environments and scalable multi-agent systems.
- Technical Architects looking to master context management, memory state persistence, and enterprise-level AI observability.