What Is Harness Engineering?

Prompt Engineering [00:38]

• Prompt engineering emerged with the advent of tools like ChatGPT.
• It involved defining goals, user personas, and evaluation criteria for LLM responses.
• This discipline focused on crafting user and system prompts to elicit better results.

  "And there became all this checklist on how to sort of prompt an LLM to give you better results than just kind of winging it."

Context Engineering [01:41]

• Context engineering expanded on prompt engineering by incorporating external data.
• The goal was to reduce hallucinations and provide LLMs with information they weren't trained on.
• Retrieval Augmented Generation (RAG) is a key technique developed during this phase.

  "So how to pull like private data into the LLM um how to pull other kind of important uh facts guidance policies"

Context Engineering in Practice [02:43]

• An example is AI summarization of patient charts, where LLMs lack specific patient data.
• By feeding relevant context at inference time, LLMs can answer questions about private data.
• This involves managing context windows and intelligently selecting data for the model.

  "You basically feed the LLM some context that it can use at the point of inference to answer questions."

Harness Engineering Defined [04:02]

• Harness engineering refers to providing an environment for agentic AI experiences.
• It includes components like a tool loop, tools (APIs), and resources.
• The harness surrounds the LLM, enabling it to perform more complex tasks.

  "Harness engineering refers to providing an environment for an agentic AI experience to work."

Components of a Harness [04:34]

• Skill files allow the AI to access predefined functionalities like scheduling or summarizing.
• Memory systems enable LLMs to handle long-running tasks and conversations without forgetting.
• Hooks can be implemented to execute specific code before a tool is called.

  "So if you want the Aentic AI to do all sorts of things instead of trying to explain every scenario, you say, 'I have a directory of skill files.'"

The Role of the Harness [05:37]

• The harness is a framework of code that surrounds the LLM.
• The LLM itself remains a model that receives prompts and provides answers.
• The harness provides the necessary environment for the LLM to perform sophisticated tasks.

  "But the harness is sort of creating an environment of tools and the loop and skills and stuff like that around the LLM so it can perform more sophisticated tasks."

Examples of Harnesses [06:26]

• Coding agents like Claude Code and Codeex are examples of harnesses.
• Personal agents like Open Claw or Hermes also utilize harness structures.
• These harnesses provide loops, tools, skills, and memory to ordinary LLMs.

  "So these coding agents are an example of a harness. They have a loop. They have tools."

Advancing AI Capabilities [07:13]

• Building AI systems today involves improving both the models and the harnesses.
• A well-designed harness significantly enhances the power and performance of an LLM.
• The focus has shifted to creating effective environments around LLMs to maximize their potential.

  "And the latest is building these harnesses these environments that kind of can can work in LLM very well."