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).
Built With
- alibaba
- langchain
- qwen
- typescript
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