Amplitude + GitHub Agents: Closing the Product Learning Loop

Coding agents have transformed how teams build software. It’s never been faster to ship a bug fix, a PR, or an entire new feature. But building faster only matters if you’re building the right things.

That’s the problem that GitHub and Amplitude are helping teams solve. And we’re using AI’s speed to solve it.

With AI agents to connect how you build, how you see real user behavior, and how you prove your impact, GitHub and Amplitude create a continuous product learning loop so you can be sure that what you ship is what your users want. Here’s how it works.

Amplitude Wave: Find issues and propose fixes

The loop starts with Amplitude Wave, our proactive AI product agent. By tracking behavioral signals from your product in Amplitude, Wave continuously surfaces improvement opportunities.

Imagine one of the first steps of your product’s onboarding process is to connect a repository. Wave notices that users are abandoning onboarding before completing that step. Could there be an opportunity to help more users connect and complete onboarding successfully?

Wave uses Amplitude’s Analytics funnels and Session Replay to investigate the drop-off. It spots a key point of friction: users are hesitating at the permissions step. Based on that observation and context from your codebase, Wave hypothesizes that a guided permissions flow could fix the issue.

Wave then creates a PRD of its proposed fix. It includes the reason for the change, the observed friction, the proposed change, implementation requirements, and a clear success metric: completion of the repository connection.

Everything you need for a GitHub issue.

GitHub Copilot: Turn ideas into code-based reality

Wave’s opportunity easily transfers into an issue that GitHub Copilot can pick up and act on. Amplitude’s GitHub Agent app facilitates the handoff, and Copilot gets to work.

When GitHub Copilot receives the issue, it uses the context from your codebase and its own coding skills to create a pull request for a guided permissions flow.

The new permissions flow has everything you need to ship it, ready in just a few minutes. Then, you (or your engineering team) review the implementation and validate the change.

But you aren’t just launching fast; you’re also launching safely. That means it’s testing time.

Statsig: Make the improvement prove itself

With your PR ready to go from Copilot, Statsig feature gates let you control who sees the new flow. But they also automatically turn your release into an A/B test.

In Statsig, you quickly set up a randomized experiment to compare the new flow with the existing experience. This lets you measure whether your fix causes more users to complete the permissions step, so you can prove Wave’s initial hypothesis.

(And with the Statsig–Amplitude event integration configured and identities aligned, you can examine user behavior alongside experiment exposure, just in case you’re curious.)

Completing the loop and onto the next

If activation improves and rollout guardrails hold, you now have the evidence to support wider exposure. If users still leave at the permissions step, your next hypothesis gets sharper: is the explanation unclear, too late, or missing something they need?

Those findings inform the next opportunity in Wave, the next GitHub Issue, and the next Statsig experiment. The loop connects a user problem to reviewed code, a controlled release, and a measured outcome.

AI scales your output. Amplitude and GitHub together scale your impact.