Agentic Harness Engineering: Harness Design for AI Engineers

### Core Architecture & Primitives

The course is built around the modern engineering reality that system-level performance is heavily driven by orchestration. You will cover:

– **Component Deconstruction:** How to separate tangled agent logic into clean, file-addressable layers (including system prompts, tool implementations, middleware, reusable skills, and multi-agent routing configurations).
– **Context Engineering & Compaction:** Designing data delivery systems that feed the agent exactly what it needs to know without overwhelming its context window or blowing through token budgets.
– **Tool Design & the Model Context Protocol (MCP):** Crafting explicit action surfaces, secure runtime execution layers, and connecting agents to external environments like CI/CD pipelines, deployment logs, and sandboxed terminals.
– **Closed-Loop Observability Systems:** Transitioning from basic logging to multi-layered evaluation setups (Component, Experience, and Decision observability) to automatically track, trace, and even allow an outer-loop agent to programmatically test and evolve its own scaffolding.

### Who This Masterclass Is For

– **AI Engineers & Framework Builders:** Developers building production-ready autonomous coding agents, support agents, or corporate automation tools who need to move past standard “vibes-based” prompting.
– **MLES / DevOps for AI:** Engineers responsible for setting up secure sandboxes (like E2B or dockerized runners), state persistence, and verification loops.
– **Advanced Software Architects:** Those interested in how systematic infrastructure design, deterministic linters, and structured guardrails can swing agent benchmark success by significant margins without altering the underlying LLM.

Author: KYZ

Topics: AI

Source: https://www.udemy.com/course/agentic-harness-engineering/