Autonomous Dogfooding Skill for agent-browser

A skill that uses your app the way your users do — no test scripts, no manual QA.

What Is It

An autonomous testing skill for agent-browser that explores your application like a real user would — clicking buttons, filling forms, navigating pages, and checking for errors — then outputs a structured report with severity ratings, repro videos, and step-by-step screenshots.

How It Works

Point it at any URL and the skill:

Explores pages — autonomously navigates your app
Clicks buttons — interacts with UI elements
Fills forms — tests input flows
Tests edge cases — tries unexpected interactions
Checks the console — monitors for errors
Captures repro videos — records issues as they happen
Screenshots each step — documents the flow
Outputs a structured report — with severity ratings

No test scripts. No manual QA.

What Is agent-browser?

From Chris's earlier announcement (Jan 2026):

"Browser automation CLI for agents

Zero config

Fast Rust CLI

Headed or Headless

Up to 93% less context than Playwright MCP

Compatible with Codex, Claude Code, Gemini, Cursor, Copilot, opencode, and any agent that supports Bash"

agent-browser is a lightweight browser automation tool designed specifically for AI agents — optimized for minimal context usage and universal agent compatibility.

Why It Matters

Dogfooding as Autonomous QA

"Dogfooding" (using your own product) is a standard practice, but it's manual and time-consuming. This skill automates it by having an AI agent explore your app like a curious user would — not following a script, but discovering issues organically.

The Testing Gap

Traditional QA approaches:

Manual testing — slow, doesn't scale, misses edge cases
Unit/integration tests — catches logic bugs but not UX issues
End-to-end test scripts — brittle, require maintenance, only test known paths

Autonomous dogfooding sits between manual exploration and scripted tests — it explores freely but captures everything.

Agent-First Workflow

This represents a shift in how QA happens:

The agent uses the app, not just tests assertions
It discovers issues rather than checking predefined scenarios
It documents findings with videos and screenshots automatically
Severity ratings help prioritize what to fix first

For Agent-Driven Development

As more code gets written by AI agents (see harness-engineering-openai), automated QA becomes critical. If an agent built it, an agent should be able to test it autonomously.

Who Is Chris Tate?

Dev at Vercel
Creator of SpecUI (component specification UI)
Built agent-browser (browser automation CLI for agents)
Previously worked on coding agent platforms and agent-native tooling

Use Cases

CI/CD pipelines — run autonomous dogfooding on every deploy
Pre-release testing — catch UX issues before launch
Regression detection — discover what broke after a refactor
Onboarding simulation — see your app through fresh eyes
Edge case discovery — find interactions you didn't anticipate

harness-engineering-openai — OpenAI's approach to agent-first engineering (where autonomous testing becomes essential)
dmux — Parallel agent orchestration (could run multiple dogfooding sessions in parallel)
exe.dev — AI coding platform where autonomous testing could be integrated
temporal-io-ai — Workflow orchestration that could drive testing workflows

Added: February 24, 2026