Brief Summary
This video introduces the concept of Harness Engineering, defining it as the practice of building frameworks that allow AI agents to operate autonomously and safely. It distinguishes Harnesses from Frameworks, outlines the nine essential components of a modern Harness, and provides guidance on how to start building Harnesses. The video emphasizes the growing importance of Harness Engineers in the AI-driven future, particularly in specialized domains.
- Harnesses are frameworks that enable AI agents to run autonomously and safely across multiple sessions.
- Nine key components of a Harness: Wide Loop, Contact Management, Skills and Tools, Sub-Agents, Built-in Q, Season Storage, System Prompt Assembly, Lifecycle Hooks, and Permissions & Security.
- Harness Engineers are becoming increasingly important as AI transitions from co-pilots to autonomous agents, especially in specialized fields.
Hook
The video opens by painting a picture of an automated coding environment where AI agents work autonomously, highlighting the role of Harness Engineers in making this possible. It introduces Harness Engineering as a significant profession in the near future, emphasizing the importance of understanding and managing AI agents through frameworks.
Agenda
In the next 20 minutes, the video aims to clarify what a Harness is, differentiate it from related concepts, dissect the nine core components of a modern Harness, and guide viewers through building a complete Harness from scratch. The goal is to equip viewers with enough knowledge to construct Harnesses for their own companies.
Phần 1 (Harness là gì)
The video defines a Harness as a framework that supports AI agents by providing memory, tools, access rights, processing loops, and safety measures. It allows AI to operate continuously without losing context, forgetting previous tasks, or causing system damage. Unlike frameworks, where developers configure and control the code, a Harness is a complete product that autonomously configures and runs tasks, reversing the control dynamic. Frameworks are designed for programmers, while Harnesses are designed for AI. Harnesses ensure agents run longer, remember past actions, avoid system damage, and know when to seek human input.
Phần 2 (9 thành phần)
The video outlines the nine essential components of a Harness:
- Wide Loop: The core of the Harness, where the model thinks, calls tools, reads results, and repeats until completion. This loop is the only part allowed to directly call the model, centralizing logic for telemetry, retries, and debugging.
- Contact Management: Manages the context window by retaining important information, summarizing old messages, and discarding outdated ones to prevent the agent from losing focus.
- Skills and Tools: Tools are basic actions (e.g., zip file, edit file) defined by JSON schemas, while skills are specific instructions for projects, often in markdown files. A registry coordinates between available skills and tools based on the project context.
- Sub-Agent: Used to handle complex tasks by creating isolated agents with specific tasks, relevant files, and minimal permissions to avoid overloading the main agent.
- Built-in Q: Pre-packaged skills that the agent can automatically use without prompting, such as processing PDFs or accessing web documents, differentiating Harnesses based on the breadth of these skills.
- Season Storage: Ensures that no progress is lost during long sessions by immediately recording all messages, tool calls, and results to a JSON-L file, allowing the agent to resume from the exact point of interruption.
- System Prompt Assembly: Constructs the system prompt dynamically at the start of each session from static core content and dynamic context gathered from project-related files, ensuring the agent is always aware of its working environment.
- Lifecycle Hooks: Code snippets that run at various points in the agent's process (pre-tool, post-tool, on error, on compaction) to log actions, check for security breaches, manage telemetry, and handle errors.
- Permissions & Security: A tiered permission system (read-only, project-write, full) that dynamically classifies commands to prevent unauthorized actions, providing a secure environment for the agent to operate autonomously.
Phần 3 (Tự build)
The video transitions to the practical aspect of building a Harness using Python without external frameworks. It details the structure, which includes nine modules for different roles like the main loop, contact management, tools, and permissions. The loop file is the core, assembling the system prompt and managing the message list. The contact manager uses a data class to compress messages when a threshold is reached. Tool definitions include names, descriptions, JSON schemas, and permission levels. Sub-agents have predefined permission sets. Season storage involves writing events to a JSON-L file for easy recovery. The prompt file assembles the system prompt from static and dynamic elements. The permission file defines permission levels and checks commands against them.
Tại sao 2026
The video explains that 2026 is a crucial year because AI is shifting from being a co-pilot to an autonomous agent. Companies using AI for coding will need Harness Engineers to build custom Harnesses. There is a high demand for this role, with salaries in the US being high and positions in Vietnam also offering competitive pay.
3 bước vào nghề
The video concludes by outlining three steps to becoming a Harness Engineer: review the video to understand the nine components, rewrite the Cloud Repo Demo from scratch, and choose a field of expertise to build a Harness for that specific area. Having a production-ready Harness will open up job opportunities.
