Agent-First Testing: Build Quality Into Every AI Coding Session
This article introduces 'agent-first testing', a workflow where AI coding agents are responsible for verifying their own code changes in real-time, instead of relying on separate QA processes.
Why it matters
Agent-first testing is a critical innovation for enabling AI-powered development to scale, by tightly integrating verification into the agent's workflow.
Key Points
- 1Traditional QA workflows cannot keep up with the rapid development pace of AI coding agents
- 2Agent-first testing embeds verification directly into the agent's workflow, with the agent launching a browser, interacting with the UI, and saving the verification as a test
- 3MCP (Model Context Protocol) enables agents to open a real browser, interact with the UI, and run verification checks as part of the coding process
Details
Agent-first testing is a response to the structural problem of AI coding agents shipping code faster than traditional QA cycles can absorb. When an agent can implement a feature in minutes, a testing workflow that requires hours or days of separate work is no longer compatible with the development velocity. The article outlines three key issues with traditional QA in agent-first teams: volume mismatch (agents generate 10-20x more code changes), context loss (QA engineers lack the agent's session context), and verification gap (agents don't run the application after making changes). Agent-first testing addresses these gaps by making the agent responsible for the full verification loop - implementing the change, launching a browser, verifying the UI, asserting expected outcomes, and saving the test. This is enabled by the Model Context Protocol (MCP), which allows agents to interact with a real browser and run verification checks as part of the coding process.
No comments yet
Be the first to comment