Future AGI
AI Agent Engineering and Optimization Platform
Start new thread
Nikhil Pareek

Agent Compass - Your AI Agent's Truth Graph to diagnose symptoms

by
Future AGI
•
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.

Add a comment

Replies

Best
Nikhil Pareek
Future AGI
Maker
📌
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
Velalagan M
Future AGI

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.

Nikhil Pareek
Future AGI
Maker

@vel_alagan true that!

Parth Jain

🚀 Debugging agents finally feels structured - auto-clustering failures into root causes is such a time-saver!

Nikhil Pareek
Future AGI
Maker
Abhishek Dhama

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.

What stands out:

👉 Zero-configuration setup lowers adoption friction (critical for busy eng/product teams).

👉 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 🚀

Nikhil Pareek
Future AGI
Maker

@abhishek_dhama Thanks a lot for your kind words.

GaganDeep Tomar
Dezan.cc

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!

Nikhil Pareek
Future AGI
Maker

@gagandt Would love to know your feedback, thanks :)

Manthan Patel

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.

Karthik Mudaliar

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.

Nikhil Pareek
Future AGI
Maker

@karthik_mudaliar2 Thanks bro

Candice Aldridge

Just read their research paper alongside the launch, finally some benchmarks + real methodology around agent failures. Big step for this space.

Nikhil Pareek
Future AGI
Maker

@candice_aldridge yess, thank you :)

Anton Loss
DeepTagger

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! 🚀

Nikhil Pareek
Future AGI
Maker

@avloss Thank you so much, do give it a try.

Khushal Sonawat
Future AGI
Maker

We built Agent Compass because debugging agents was eating up hours. Now it’s minutes. Would love feedback from anyone running production agents!

Nikhil Pareek
Future AGI
Maker

@khushalsonawat It was fun building this with all of you :)

12
Next
Last