Selfship.ai
Surface and fix isues with your agentic applications 24x7
Get picks like this daily. Today's launch numbers, the top launches, AI and tech news, and a daily opportunity pick, straight to your inbox.
Want to reach these readers? Sponsor an issue.
We've been building an AI chat based trading system for last 3 years. The biggest issue was that when our Agent would mess up, we wouldn't know until a user reported. Agent traces helped us uncover what's wrong. But surfacing issues was almost always a manual trigger. So from those learnings, we built Selfship.ai. It's an autonomous system that observes every trace/turn/multi turn convo to find out issues. If a user got what they wanted, if a tool call is failing repeatedly, if users have to always reframe their questions, if the agent is taking optimal paths and many more. It's a loop - group failures by user intent, evaluate them, and ship fixes as PRs. After a fix is deployed, it evaluates if it worked or not. We recently opened it up as a SaaS. If you have an agentic product in production, we would love for you to try it out.
HN · launched Sep 1, 2026 · 10 upvotes (top 41%) · 0 views
Niche: ai agent observability platforms · 220 launches, 5 in the last 7 days ▼ -64% · Explore this niche →
Category and tags
- Horizontal
- Observability & eval
- Developer
- AI-native
- Commercial product
Visit website ↗ Source listing ↗
More like this: all 220 launches in ai agent observability platforms →
Could you build this?
Partial A web dashboard and GitHub PR generator are straightforward, but building automated root-cause failure clustering on agent execution traces that produces verified code fixes is difficult.
What it would actually take: Requires an ingestion pipeline that parses heterogeneous LLM/agent traces (OpenTelemetry, Langfuse format), performs semantic clustering on trace error topologies, and isolates failure-inducing prompt/code steps. An evaluation sandbox spins up headless Docker containers to reproduce the failed session, runs a coding model against the repo to patch it, and verifies the fix via regression tests before opening a PR. This demands solid DevOps/sandboxing engineering and robust agent evaluation methodologies.
Discussion
10 comments analyzed.
Competitors mentioned: Domino (systematic error/slice discovery), AgentBoard (trajectory/progress evaluation), τ-bench (goal/outcome correctness), MAST (failure taxonomies)
Concerns raised: How to distinguish actual agent failures from normal user drop-offs, Risk of overfitting fixes to specific conversations rather than solving root problems, Uncertainty about optimal conversation turn count for effectiveness, Unclear if approach has domain-specific limitations or breaks down in certain areas
Feature requests: On-premise deployment option, Identify domain-specific sweet spots for best performance
Competitors
Other products that read as similar to this one : 1659 launches clear the similarity bar, closest 8 shown.
Attention rank: #759 of 1660 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 302 days after the earliest competitor.
- ReplyPilot · ph · 2026-09-17 · 1 upvotes · similarity 0.50
- AgentInflux · ph · 2026-09-07 · 1 upvotes · similarity 0.48
- Midwater · ph · 2026-10-19 · 2 upvotes · similarity 0.48
- Logic · ph · 2026-04-27 · 274 upvotes · similarity 0.47
- AiTalk.ch · ph · 2026-10-09 · 2 upvotes · similarity 0.47
- Robinhood Agentic Trading · ph · 2026-05-28 · 165 upvotes · similarity 0.47
- Agent Checker · ph · 2026-09-06 · 2 upvotes · similarity 0.46
- Artis · ph · 2026-09-30 · 2 upvotes · similarity 0.46
The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.