143. Harness Engineering: How to Build Better AI Coding Agents

143. Harness Engineering: How to Build Better AI Coding Agents

What is harness engineering, and how can it help you get better results from AI coding agents? Discover how tools, context management, and automated checks help coding agents work more consistently and produce maintainable software.

In this episode of Betatalks, we explore the harness: everything around the AI model that enables an agent to take action, use tools, and check its work. Using GitHub Copilot as a familiar example, we discuss how the developer workflow is changing, and why the environment you give your agent matters.

We cover:

• What an agent harness is and how it relates to the AI model

• How context and project instructions help agents follow your team’s conventions

• Why Markdown instructions alone cannot guarantee correct results

• How builds, tests, and architecture checks create a feedback loop

• The role of tool access, permissions and domain knowledge

• The trade-offs between richer context and higher costs

Whether you’re using an existing coding assistant or building your own agent, this episode explains the principles for guiding its actions and verifying its results.

For more information:

• Harness – Microsoft Agent Framework

• Harness engineering – Martin Fowler