What if your backlog could shrink while your team focuses on high-impact features? On the HyperShift team at Red Hat, we built an AI-powered workflow using Claude Code that autonomously picks up Jira tickets, implements solutions, opens PRs, and even responds to peer review feedback — all without a developer lifting a finger.

In this talk, we'll walk through the key automations we built as Claude Code skills, orchestrated through OpenShift CI periodic jobs:

jira:solve — A four-phase pipeline triggered weekly by a periodic CI job. Given a Jira issue, it fetches the ticket details, analyzes the codebase, generates an implementation plan, writes the code and tests, and runs verification (linting, builds, unit tests). Then an AI peer review evaluates the changes for code quality issues. A third phase automatically addresses the review findings and pushes fixes. Only after this internal review cycle completes does the final phase open a PR.

address-reviews — A separate periodic job that runs every three hours. It queries GitHub for agent-created PRs with pending review comments from humans or AI reviewers, categorizes them by priority (blocking, change requests, questions, suggestions), makes the requested code changes, amends the relevant commits, and posts concise replies — closing the feedback loop without context-switching a developer. It can also be triggered on-demand for any PR via a Prow comment.
Together, these create a pipeline where low-priority backlog items flow from Jira to review-ready PRs with minimal human intervention. Developers review the AI's work rather than doing it themselves, acting as supervisors instead of implementers for routine fixes.

We'll cover:

Architecture: How we built a system of Claude Code skills that encode our team's standards — code formatting conventions, Gherkin-style test patterns, effective Go idioms, conventional commit formatting, PR templates, and a project-specific AI review profile — so the AI produces output that meets the same bar as a human contributor.

The solve pipeline: Jira ticket parsing → codebase analysis → plan generation → implementation → verification → AI peer review → review fixes → PR creation

The review loop: How the address-reviews job triages human and AI feedback, makes surgical code changes, amends commits, and replies to reviewers

Guardrails: Comment deduplication, authorization checks, token refresh across long-running phases, and why human review remains essential

Results and lessons learned: What works, what doesn't, and where human judgment is still irreplaceable

Whether you're a platform engineer, SRE, or developer tired of your backlog growing faster than you can triage it, this talk will give you a practical blueprint for putting AI to work on the tasks your team doesn't want to do and the lower-priority ones it doesn't have time to do.