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IronBee is your AI QA engineer. It verifies code changes the way a QA engineer would: it opens the running application, exercises what changed, and reports what works and what doesn’t, with the evidence to prove it. There are two ways in, and you can use both:
  • On your deployments and pull requests. Connect GitHub, Vercel or Netlify, and IronBee verifies each preview deployment or pull request on its own infrastructure. The result lands on the pull request, on the deployment, and in the console.
  • Inside your AI coding agent. The IronBee CLI gives Claude Code, Cursor and Codex the tools to verify their own changes in a real browser, a running backend or a terminal before they call the work done.

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The problem

AI coding agents are fast, but they lack accountability. An agent can write hundreds of lines of code, declare success, and move on without ever checking if the browser renders correctly, if the API responds as expected, or if anything broke. Teams using agentic development today face:
  • Silent regressions that only surface in code review or production
  • No evidence that a change was actually verified
  • No insight into how the agent spent its time, what it struggled with, or where it kept failing

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What IronBee does

Verification on every change

Every preview deployment on Vercel or Netlify, and every push or pull request with the GitHub Action, starts a verification run. An agent reads the change and exercises it against your running app.

Verification inside your agent

With the IronBee CLI, your AI coding agent verifies its own changes with real tools. In assist mode you ask for a verification; in enforce mode the agent can’t finish until the verification passes.

Evidence for every run

Every verification run is captured and available in the console: the recording, screenshots, actions, network requests, traces, and logs. Watch a run live while it’s in progress.

Results where you work

Results land as a check and a comment on the pull request, as a Vercel deployment check or on the Netlify deploy page, and in the console. The GitHub Action can also fix what it finds.

AI-powered analysis

Every day, IronBee runs an LLM analysis over each project’s recent sessions to surface findings and recommendations: what went wrong, what patterns keep appearing, and what to do next.

Automatic recommendations

Findings are turned into directives that are automatically injected into the agent’s context on future sessions, it learns from past mistakes without manual intervention.

Security

Never collects prompt content, file content, tool output, credentials (API keys and authentication tokens), or any PII beyond your org-configured email. Your code and secrets stay on your machine.

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How it works

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On your deployments and pull requests

  1. A preview deployment on Vercel or Netlify, or a push or pull request with the GitHub Action, starts a verification run.
  2. An agent on IronBee reads the change, then verifies it. A web app is opened in a browser and exercised like a user would. An API is called directly and its responses are checked against the contract.
  3. The verdict and its evidence land on the pull request, on the deployment, and on the project’s Verifications tab in the console, where you can also follow the run live.
See How integrations work.

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Inside your AI coding agent

This is enforce mode. In the default assist mode, the same verification runs when you or the agent ask for it with /ironbee-verify, and nothing blocks the agent. Every session and its verification cycles are available in the console. See How verification works.

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Supported AI clients

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Next steps

Quickstart

Connect GitHub, Vercel or Netlify, or set up the CLI.

Key concepts

Projects, verification runs, environments, sessions and verdicts.