Long Horizon

Product Introduction

1. Definition: Long Horizon is an agentic end-to-end testing platform for web applications. Technically, it is a desktop application that integrates with AI coding agents (via the Model Context Protocol - MCP) to automate the planning, authoring, execution, and debugging of real-browser tests.
2. Core Value Proposition: It exists to close the loop in AI-driven development by enabling the same AI agent that writes a feature to also automatically test it in a real browser environment. Its primary value is providing confident feature delivery through automated execution reports with logs, screenshots, and network details.

Main Features

1. Agent-Driven Test Planning: The AI agent analyzes your feature description and repository context to autonomously draft a comprehensive test plan. This includes identifying core user paths, edge cases, and potential failure scenarios (e.g., "Checkout — happy path," "Cart — empty checkout," "Payment — decline and retry").
2. AI Test Authoring & Execution: The agent writes executable test code directly into your project. It then runs these tests in a headless or headed real browser (like Chromium via Playwright). Tests are organized into sessions (e.g., "Checkout flow") and produce re-usable code for regression testing.
3. Shareable Execution Reports: Every test run generates a detailed, shareable report. This report includes a human-readable test plan, a status log for all scenarios, and a step-by-step execution log. The log captures every action (navigation, clicks), assertions, network requests/responses (e.g., POST /api/charge 200), and automatic screenshots for visual verification.
4. Automated Debugging & Fixing: When a test fails, the Long Horizon agent analyzes the execution logs to diagnose the root cause (e.g., "inventory returned checkout blocked"). It can then automatically implement fixes, such as patching test files to stub API responses, and re-run the tests to validate the resolution.
5. UI Feedback Collaboration: Developers can leave contextual feedback directly on UI screenshots within the tool. Comments pinned to specific elements (e.g., "Add border and shadow to discount coupon section") are passed directly to the coding agent, which then implements the changes, reducing back-and-forth communication.
6. Evidence-Based PR Review: Long Horizon generates a dedicated testing report link for each feature session. This report can be attached to pull requests, providing reviewers with concrete evidence of what was tested, how it worked, and execution proof, moving approval from guesswork to data-driven confidence.

Problems Solved

1. Pain Point: The disconnect between AI-generated code and verification. Coding agents can produce features quickly, but the burden of creating comprehensive, reliable front-end tests remains manual, slow, and prone to human oversight.
2. Target Audience: Frontend and Full-Stack Developers using AI coding assistants (Claude Code, Cursor, Codex); Engineering Managers seeking to improve release confidence and PR review velocity; QA Engineers transitioning to an AI-augmented, developer-led testing model.
3. Use Cases: Automated Regression Test Creation: Generating test suites for new features instantly. Flaky Test Debugging: Automatically diagnosing and fixing intermittent test failures. PR Validation: Providing tangible proof of functionality for merge requests. Visual & Interaction Feedback: Streamlining UI iteration between developers and AI agents.

Unique Advantages

1. Differentiation: Unlike traditional testing frameworks (Selenium, Cypress, Playwright) which require manual or scripted test creation, Long Horizon uses the project's AI agent to autonomously create and run tests. Unlike simple AI test generators, it provides a closed-loop system with execution, visual reports, and automated debugging.
2. Key Innovation: Its deep integration with the Model Context Protocol (MCP). This allows it to function seamlessly inside the developer's existing coding agent workspace (Claude Dev, Cursor, etc.), making test generation a native, contextual action rather than a separate tool. The agentic feedback loop—where the agent plans, writes, runs, debugs, and fixes tests—is its core technological approach.

Frequently Asked Questions (FAQ)

1. How does Long Horizon work with AI coding agents? Long Horizon integrates via the Model Context Protocol (MCP), exposing its testing capabilities as tools that your coding agent (like Claude Code or Cursor) can call directly from within your IDE. The agent uses it to plan, run, and debug tests in the context of your current task.
2. What kind of tests does Long Horizon run? It runs real, end-to-end browser tests. It automates interactions like clicking, typing, and navigation in a real Chromium browser, waits for network requests, and validates DOM state and visual outcomes, covering user flows from start to finish.
3. Can I review and edit the tests that the AI creates? Yes. The agent writes test code (shown in JavaScript/Playwright-style syntax in the summary) directly into your project's test directory (e.g., .longhorizon/tests/). Developers can review, modify, and commit this human-readable code for future regression testing.
4. Is Long Horizon a replacement for manual QA? It is designed to augment developer and QA workflows by automating the creation and execution of repetitive test scenarios. It shifts the role towards reviewing AI-generated test plans and execution reports, and handling complex, exploratory testing.
5. What is included in the Long Horizon execution report? The shareable report includes the full AI-generated test plan, a list of all executed tests with pass/fail status, and a detailed, step-by-step log with timestamps, actions, network calls, console logs, and automatic screenshots for every key step.