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
This looks really cool! That centralized dashboard sounds like a game changer for keeping track of agent performance. How does the data analytics feature work with different platforms?
I've seen discussions about how frustrating it can be to track agent performance across multiple systems. People really struggle with keeping everything clear and organized.
If you're looking to connect with product managers and AI developers, these conversations are happening everywhere. They really need a solution like Agent Compass!
@nikhilpareek Looks like a good problem to solve, will check it out. Congrats to the team!
Hey PH community! Charu here, Co-Founder at Future AGI.
I’ve noticed a common challenge across AI teams: troubleshooting agents is messy and time-consuming. Even minor tweaks in prompts, tools, or data sources can trigger cascading errors, and most evaluation tools only indicate that something went wrong without showing the reason or solution.
This is where Agent Compass comes in. It requires no setup, automatically tracks failures, uncovers their root causes, and suggests actionable solutions. Teams can spot trends across all their agents, integrate insights into tools like Jira or Slack, and soon enable agents to fix issues on their own.
If you're shipping AI agents to production, this is essential. Stop wasting engineering hours on detective work. Agent Compass is exactly what we needed to debug AI agents systematically.
Love the emphasis on guided fixes and tracking performance across user journeys. This approach to reliability engineering is exactly what many teams need.
Debugging has always been the Achilles’ heel for AI agents, and Agent Compass feels like a true breakthrough. Turning scattered traces into a clear root-cause narrative is exactly what the industry has been missing. The “truth graph” and error tree approach is such a smart way to bring order to the chaos.
Now build AI agents without worrying about things breaking in production. We got you!
Check out our research paper https://arxiv.org/pdf/2509.14647. We achieved state-of-the-art results on error detection and categorization!
Thanks for checking out our launch! We’re especially looking for feedback on two things:
What frameworks you’d like Compass to integrate with first
How you’re currently debugging agent failures
Drop your thoughts, we’re here all day answering questions.
Couldn’t be more excited to finally share Agent Compass with the PH community! Our team poured months into making agent debugging actually painless, can’t wait for you all to try it out 🎉
We built Agent Compass because debugging agents was eating up hours. Now it’s minutes. Would love feedback from anyone running production agents!
LLM observability is something that's very hard to get right! Your product looks like something that can truly innovate in this complex space!
Congratulations on your launch and good luck with your product! 🚀
Been using @Future AGI for more than a month, and its super awesome! Especially the support from the builders. We at hiresteve[dot]ai were struggling to establish an eval that actually works, and we were able to get it done, recieveing extensive support from their team. The platform is also very easy to use and the docs are super intuitive. Will check the compass and share more feedback soon!
Just read their research paper alongside the launch, finally some benchmarks + real methodology around agent failures. Big step for this space.
This looks super useful debugging agents has always felt way more painful than it should be. Really like the idea of clustering failures into root causes instead of staring at endless logs. Excited to see where this goes.
Future AGI feels like a much-needed layer in the AI stack. Too many teams still treat hallucination and reliability issues reactively. This flips the model into proactive observability.
👉 The ‘Truth Graph’ approach makes continuous monitoring and optimization more intuitive.
👉 Actionable suggestions + clustering root causes is exactly what accelerates debugging at scale.
From my perspective, the real unlock will be how this drives trust for both enterprise buyers and end-users. As AI observability becomes a baseline expectation, I can see Future AGI becoming the equivalent of ‘New Relic for AI systems.’ Excited to see where you take it 🚀
🚀 Debugging agents finally feels structured - auto-clustering failures into root causes is such a time-saver!
Debugging LLM workflows is a nightmare. Agent Compass really gives a narrative of what broke, why, and how to fix it in minutes — that’s a game changer for AI teams.
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.
Agent Compass was featured in Software Engineering (42.3k followers), Developer Tools (511k followers) and Artificial Intelligence (466.2k followers) on Product Hunt. Together, these topics include over 158.6k products, making this a competitive space to launch in.
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.
Want to see how Agent Compass stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Nikhil Pareek