Harness Engineering Explained in 22 Minutes

Shaw Talebi

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Video Summary

This video explains harness engineering, which involves building systems around AI models to enhance their helpfulness. The presenter uses a car analogy, where the AI model is the engine and the harness includes everything else that makes it functional. The video details three main levers for optimizing AI harnesses: context, tools, and automations, with a bonus discussion on evaluations (evals).

Context involves providing instructions, knowledge, and data to the AI, customizable through files like claude.md, skills, and external sources. Tools enable AI agents to interact with external applications and services via connectors or custom scripts. Automations allow tasks to run automatically, triggered by time or events, through scheduled tasks or hooks. Evals are tests that help the AI determine the quality of its own output, moving beyond human-in-the-loop processes to agent-in-the-loop systems for increased efficiency.

Short Highlights

Harness engineering builds systems around AI models to make them more helpful.
Key optimization levers include context, tools, and automations.
Context can be customized via files, skills, and external data sources.
Tools enable AI interaction with external apps through connectors or scripts.
Automations allow for scheduled tasks and event-triggered hooks.

Key Details

What is Harness Engineering? [00:15]

Harness engineering refers to building everything around an AI model to make it more helpful.
Analogy: AI model is the car engine; the harness is the wheels, body, seats, etc.
Users can customize agent harnesses to better suit their needs.

Levels of Harness Engineering [01:03]

Level 1: Using off-the-shelf tools like ChatGPT or Claude Co-work with basic customization (trim, color, settings).
Level 2: Advanced customization with tools like Claude Code, requiring more expertise to tune and optimize.
Level 3: Building a completely custom agent harness from scratch using APIs and custom code.

The Three Levers of Harness Engineering [03:07]

The video focuses on context, tools, and automations as key levers for optimization.
A bonus lever, evals, is also discussed.

Lever 1: Context [03:36]

Context includes instructions, knowledge, and data provided to an agent.
Pre-built context in tools like Claude Co-work includes system instructions and metadata.
Customization options include claude.md files for persistent guidance and skills (folders with files like skill.md) for task-specific information.
Skills utilize progressive disclosure, revealing context only when relevant.
External sources like local files, databases, Notion, Obsidian, or Confluence can also provide context.

"Saving things in external sources is a great practice because not only does this avoid again dumping a bunch of unnecessary context on Claude at the start of every conversation, but perhaps more importantly because all your context lives in these third party tools, it makes it much easier to use that information across multiple AI tools."

Lever 2: Tools [09:04]

Tools are actions an agent can take to interact with external apps and services.
Basic tools include web search and code interpreters.
Connectors (e.g., for Gmail, Notion, Google Drive, Slack) allow agents to access and interact with external applications.
Skills can also grant agents access to tools through scripts, enabling actions like editing presentations, videos, or interacting with hosting services via APIs.

"When you give Claude the right tools, it really unleashes the number of use cases that are possible."

Lever 3: Automations [13:34]

Automations are tasks that run automatically, triggered by time or events.
Built-in automations include content screening, file pre-processing, and content moderation.
Custom automations include scheduled tasks (e.g., daily meeting prep) and hooks (event-triggered automations).
Hooks are available in coding agents like Claude Code and allow for actions like automatically pushing config files to GitHub or deleting temporary screenshots.

Bonus Lever: Evals [17:46]

Evals are tests that help an agent determine if its output is good.
They enable an "agent in the loop" process, reducing reliance on constant human review.
Andrej Karpathy's auto-research project is cited as an example where evals automated the AI research process.

"Instead of being stuck at the speed of a human in the loop, you can speed this process up with an agent in the loop."

Key Takeaways [20:53]

AI models alone are not helpful; they require the right harness.
Harness engineering involves customizing context, tools, and automations.
A mindset shift is required from executing tasks to building harnesses that enable agents to execute tasks.
Agent empathy—imagining if you could accomplish the task with the given harness—is crucial for successful harness design.