Turn raw traces into actionable reliability insights: auto-cluster recurring failures and hallucinations, link them to root causes with guided fixes, and track agent-level performance over time across cohorts and user journeys.
Hey PH community!
Nikhil here, Founder & CEO at Future AGI.
Today, I’m really excited to share Agent Compass, something no other Agent monitoring or evaluation tool offers and we are the first one.
Why did we build this?
Over the past few months, I kept seeing the same problem across AI teams: debugging agents is chaotic. Teams would spend hours digging through logs and dashboards, trying to piece together why an agent failed. One small change in a prompt, a tool, or a data source could cascade into errors that nobody could fully trace. I’ve literally watched engineers spend days chasing failures, only to realize the root cause was something completely unexpected. And to make things worse, the current evaluation tools don’t really help. They just flag that something broke, without giving any clue about why or how to fix it.
How does it actually work?
Agent Compass is a zero-config evaluation tool for AI agents. It automatically identifies issues like hallucinations, traces their causes across prompts, tools, retrievals, and guardrails, and suggests fixes that teams can apply right away. Instead of looking at errors one by one, it shows patterns across your entire agent fleet, making debugging faster and more reliable.
It builds a truth graph for your agents by linking errors across prompts, tools, and execution steps. It automatically clusters failures into a small set of root causes and generates an error tree that shows how one issue cascades across the workflow. Instead of drowning in fragmented traces and logs, you get a clear narrative of what broke, why it happened, and how to fix it. With zero-config evals, setup takes just a few lines of code. Debugging stops being a full-time job and starts becoming a fast, reliable process.
Where we’re headed
This is revolutionary. The vision is to make AI agents as reliable and predictable as traditional software, no matter how complex their workflows become. This will bring us closer to true autonomous reliability.
Thanks for checking this out. I’d love to hear your thoughts, and how your team handles debugging multi-tool AI agents today!
▶️ Debug your AI agents in 5mins.
- Try Agent Compass for free-> https://shorturl.at/IDK32
- Tech Docs -> https://shorturl.at/Y6sCD
- Research Paper -> https://arxiv.org/abs/2509.14647
About Agent Compass on Product Hunt
“Your AI Agent's Truth Graph to diagnose symptoms”
Agent Compass launched on Product Hunt on September 26th, 2025 and earned 148 upvotes and 33 comments, placing #11 on the daily leaderboard. Turn raw traces into actionable reliability insights: auto-cluster recurring failures and hallucinations, link them to root causes with guided fixes, and track agent-level performance over time across cohorts and user journeys.
On the analytics side, Agent Compass competes within Software Engineering, Developer Tools and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Agent Compass performed against the three products that launched closest to it on the same day.
Who hunted Agent Compass?
Agent Compass was hunted by Nikhil Pareek. 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.
Nikhil Pareek