Inspiration

Software teams ship fast, but manual release verification is slow, fragmented, and lacks clear proof. We built Autopilot-agent—an evidence-first AI agent that automates release checks with human approval at risky points.

What it does

  • Plans & Executes: Converts PR diffs and natural language into structured Playwright browser/API test plans.
  • Collects Audit Proof: Captures screenshots, network logs, and traces linked to immutable commits.
  • Human-in-the-Loop: Enforces policy gates before running risky actions (deployments, PR updates).
  • Release Recommendation: Delivers a clear pass/fail risk report for release managers.

How we built it

  • Core App: Next.js 16 (React 19), Tailwind CSS.
  • AI & Automation: Qwen LLM Gateway, Playwright in isolated workers.
  • Durable State: Custom checkpoint system (FileCheckpointStore / Alibaba OSS layout) for resumable tasks.
  • Cloud Infrastructure: Alibaba Cloud FC3 (Function Compute) with OSS & SLS logging.

Challenges we ran into

  • AI Safety & Policy: Decoupling strict permission rules from prompt context so the AI can't bypass human approval.
  • Long-Running Serverless Runs: Designing durable checkpoints so tasks survive function timeouts and process restarts.

Accomplishments that we're proud of

  • Built a truly resumable agent that pauses for human approval and resumes without losing progress.
  • Created immutable, audit-ready evidence packs tied to commit SHAs.
  • Successfully deployed and verified end-to-end reference shop journeys on Alibaba Cloud FC3.

What we learned

  • Prompts are not policy: Security guardrails must be handled by hard-coded policy engines outside the LLM.
  • Evidence over answers: Teams need verifiable screenshots and logs, not just an AI saying "looks good."

What's next for Autopilot-agent

  • One-click GitHub App integration with automated PR status checks.
  • Multi-agent expansion (Security Scanner, Visual Regression Agent).
  • Cloud adapters for AWS (S3/Lambda) and GCP (Cloud Run/GCS).

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