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AI Agent Evaluation Platform
supaeval
Pinpoint where your AI agents are failing.
imran@supaeval.com  |  supaeval.com
The Shift
AI agents are going into production. Fast.
But nobody can pinpoint what's actually breaking.
Quality is the #1 barrier to adopting AI agents - not cost.
Trust and accuracy beat everything else.
Yet most teams are still evaluating with ad-hoc, manual methods.
78%
cite quality & trust as the #1 barrier to scaling AI agents
supaeval|eval. ship. repeat.
02
The Problem
Sound familiar?
# ai-agent-quality
CS
Customer Success10:14 AM
The AI agent isn't working - three enterprise customers escalated this morning alone.
EM
Engineering Manager10:22 AM
Checked the logs. Overall score looks fine? The model must be hallucinating on edge cases.
VP
VP of Technology10:31 AM
We can't keep scaling this without knowing what's actually breaking. What's the root cause?
Your Customer Success team escalates. Engineering investigates.
Everyone blames the model.

But nobody can pinpoint which layer is actually failing.
supaeval|eval. ship. repeat.
03
The Insight
Every AI agent has 7 layers.
Each one can fail independently.
Intent
→
Reasoning
→
Retrieval
→
Memory
→
Tool Use
→
Generation
→
Business
Most tools only score the final answer.
A green overall score hides failing layers underneath.
Current tool vs. reality
Final Answer Quality 88% ✓
Intent Routing (hidden) 41% ✗
Retrieval Precision (hidden) 68% ~
supaeval|eval. ship. repeat.
04
The Promise
What if you could...
→ See exactly which layer is failing - in minutes, not days
→ Get fix suggestions, not just scores
→ Run 2,000+ test cases instead of 30 manual prompts
→ Catch regressions before customers complain
→ Replace a $300-500K/year eval team with a subscription
Intent Reasoning Retrieval Memory Tool Use Generation Business !
supaeval|eval. ship. repeat.
05
The Solution
supaeval pinpoints where your agents are failing.
Layer-by-layer evaluation. Not "overall quality is fine."
Each layer scored independently. Root cause + fix suggestions included.
1
Connect
One SDK call, any framework.
Python or TypeScript.
2
Evaluate
Every layer scored independently.
Synthetic data amplifies your test coverage from 30 to 2,000+ cases.
3
Fix
Root cause identified with fix suggestions.
Not just what's broken - how to fix it.
supaeval|eval. ship. repeat.
06
Agent Health Monitor
// ENTERPRISE RAG AGENT - PROD
LIVE
Intent Routing
41%
Doc Hit Rate
68%
Retrieval Prec.
72%
Chunking
91%
Final Answer
88%
Root Cause
Intent routing is broken at layer 1. Queries are being misclassified, sending retrieval down the wrong path.
Suggested Fix
Update system prompt to include explicit intent categories with example queries. Add fallback routing for ambiguous intents.
Zero Guesswork.
One Dashboard.

The Agent Health Monitor shows every layer of your agent - scored independently.

No more guessing. The root cause and fix are right there.

Notice:
Final answer: 88%. Looks healthy.
Intent routing: 41%. Completely broken.
Your current tool misses this. supaeval doesn't.
supaeval|eval. ship. repeat.
07
The Transformation
Before and after supaeval

Before

Time to find root cause Days
Test coverage 10-30 prompts
Diagnosis "It's hallucinating"
Annual cost $300-500K
Detection Reactive

After

Time to find root cause 30 minutes
Test coverage 2,000+ cases
Diagnosis "Intent at L1: 41%"
Annual cost $50-100/run
Detection Proactive
supaeval|eval. ship. repeat.
08
Validation
Real production results
An enterprise B2B SaaS ($3B+ company, 20+ production agents) saw their existing eval tool report overall quality above 90%. But customer complaints kept rising. Layer-by-layer evaluation revealed retrieval and intent layers were driving production failures - completely hidden by the aggregate score.
30 min
to identify root cause
63% → 81%
quality improvement
2
layers identified as root cause
supaeval|eval. ship. repeat.
09
Platform
Everything you need to ship better agents
🔍
Layer-by-Layer Evaluation
Score every layer independently.
Retrieval, intent, chunking, generation, tool use.
📊
Agent Health Monitor
One dashboard, zero guessing.
Real-time layer scores for every agent.
🧪
Synthetic Data Pipeline
200 seed prompts → 2,000+ test cases.
No manual prompt writing.
🔧
Fix Suggestions
Actionable recommendations for each failing layer.
Not just scores.
⚡
SDK (Python & TypeScript)
One function call to connect.
Any framework. Any model provider.
🔄
CI/CD Integration
Eval on every PR. Pre-deployment quality gates.
Catch regressions automatically.
supaeval|eval. ship. repeat.
10
Design Partner Program
We're looking for 5 design partners.
Join us in building the evaluation platform your team actually needs.

What you get

  • ✅ 3 months free access on any tier
  • ✅ Direct Slack channel with founders
  • ✅ Priority feature requests
  • ✅ White-glove onboarding

What we ask

  • 📞 Weekly 30-min feedback call
  • 📝 Share what works and what doesn't
  • 🎯 1+ agent in production to evaluate
  • 🤝 Be a reference if it works out
supaeval|eval. ship. repeat.
11
Ready to pinpoint where
your agents are failing?
→ Book a 20-minute demo
→ See your agent's health dashboard in Week 1
→ Fix the root cause, not the symptoms
Book a Demo
imran@supaeval.com  |  supaeval.com