We built an AI Agent to reproduce bugs
Details
- External ID
- 47767829
- Source
- HN
- Company
- —
- Product
- We built an AI Agent to reproduce bugs
- Website domain
- metabase.com
- Launched
- April 14, 2026
- Cohort
- —
- Upvotes
- 12
- Upvotes percentile
- 0.6446015424164524
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
At Metabase, we built an AI agent called Repro-Bot that reads our GitHub issues and attempts to reproduce reported bugs automatically.It started as a hackathon project and is now part of our daily workflow, so we wrote about it and open-sourced the code as an example for others.How have similar tools been working for you? What has worked well and what has not?
Enrichment
- Theme
- developer tools for AI agents
- Vertical
- —
- Function
- Agent / copilot
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- ai agent for bug reproduction
- Manually corrected
- False
Could you build this?
Partial The orchestration loop calling LLMs is straightforward, but autonomously executing, instrumenting, and deterministically reproducing arbitrary software bugs in sandboxed repositories is technically challenging.
What it would actually take: The system requires a distributed sandboxed execution environment (ephemeral Docker containers or Firecracker microVMs) capable of checking out specific commits, installing diverse dependency trees, and mocking databases. The hard part is designing heuristic and agentic feedback loops that interpret ambiguous user bug reports, synthesize reproducible reproduction scripts (e.g. Cypress or unit tests), and distinguish genuine reproductions from setup failures. This requires deep DevSecOps and automated testing expertise.
Discussion
2 comments analyzed.
Concerns raised: 10% false positive rate in bug reproduction, Bot sometimes reproduces different bug than reported, Difficulty validating agent confidence levels
Feature requests: Lower false positive rate through improved prompts, Better distinction between exact vs. partial reproductions
Competitors
Other products that read as similar to this one — 917 launches clear the similarity bar, closest 8 shown.
Attention rank: #349 of 918 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 167 days after the earliest competitor.
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- Legit, Open source Git-based Version control for AI agents · hn · 2026-01-09 · 9 upvotes · similarity 0.53
- repopilot · github · 2026-09-18 · 153 upvotes · similarity 0.50
- HyperFlow · hn · 2026-04-11 · 8 upvotes · similarity 0.50
- 127 PRs to Prod this wknd with 18 AI agents: metaswarm. MIT licensed · hn · 2026-02-03 · 5 upvotes · similarity 0.49
- Detail, a Bug Finder · hn · 2025-12-09 · 67 upvotes · similarity 0.49
Other launches for this product
- No other launches for this product.