This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Nexus monitors your AI agents in production to catch and handle silent failures before your users complain. It leverages your engineering and trace context to automatically catch, surface, and root-cause only issues that will have major user impact.
👋 Hi everyone! I'm Nikhil, the maker of Nexus (trynexus.io).
Today, I'm excited to officially launch Nexus — a monitoring platform that helps you catch and handle AI agent failures in production before they ever reach your users.
In my previous startup, debugging AI agents in production was a nightmare. By the time we realized something was wrong, users had already been impacted. The failures were silent, hard to reproduce, and nearly impossible to root-cause without digging through logs for hours.
Nexus is the tool I wish I had.
🚀 What Nexus does:
🔍 Catch silent failures in real-time — detect beyond basic failure modes with customizable modes aligned to your agent's actual goals
🤖 Root-cause on autopilot — every issue gets analyzed with context pulled from code, logs, traces, and prompts automatically
📊 Track agent performance over time — see which failure modes fire most and how your agent trajectories are trending
⚡ Fix fast — get Slack alerts, auto-created Linear tickets, and even automated PRs so your team can ship fixes before more users get impacted
🔌 Context-aware — It knows your github, slack, linear, traces, and more to better vet issues caught.
If you're shipping AI agents to production, Nexus keeps them from silently failing your users. Check it out at trynexus.io.
This is still early and I'd love your feedback — I'll be around all day!
Thanks for checking it out 🙏
– Nikhil
When a traditional API breaks, you get a 500 error and it's obvious. When an AI agent fails, it often just returns a confident-sounding wrong answer and nobody notices until a user complains. Our existing observability tools have no idea what "good" looks like for an LLM response. How do you define "major user impact"? Is that something we configure based on our own product logic, or does Nexus infer it from the trace context automatically?
About Nexus on Product Hunt
“Context-aware agent monitoring”
Nexus was submitted on Product Hunt and earned 16 upvotes and 3 comments, placing #25 on the daily leaderboard. Nexus monitors your AI agents in production to catch and handle silent failures before your users complain. It leverages your engineering and trace context to automatically catch, surface, and root-cause only issues that will have major user impact.
Nexus was featured in Developer Tools (512.4k followers) and Artificial Intelligence (468.5k followers) on Product Hunt. Together, these topics include over 161.5k products, making this a competitive space to launch in.
Who hunted Nexus?
Nexus was hunted by Nikhil Pillai. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Nexus stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Nikhil Pillai