Find user frustration.
See where your agent fails, users get frustrated, and churn begins. Every pattern links to the exact conversations and traces so you can fix it fast.
Backed by Y Combinator
Find Agent Failures to Train a Custom Model
Perform better, respond faster, and cost less for your workload.
Your production traces become the training data.
Agnost analyzes more than one million messages a day to find the repeatable workflows a custom model can own.
Explore your tracesFeatures
Ship what users want.
See what frustrates users, what they ask for, and what to improve across every conversation.
Auto-cluster conversations.
Turn thousands of chats into the recurring problems that disappoint users, ranked by impact and ready to investigate.
Prevent costly failures.
Catch hallucinations, broken promises, and policy violations with the exact conversation and trace behind every failure.
Train your Custom Model:
Uses historical production traces to create eval sets and train models specialized for each agent’s workload.
Get started with Analytics.
Connect Agnost to your agent in two quick steps, then let real conversations reveal what needs attention.
↓Download the skill
npx skills add AgnostAI/skills --skill agnost-ai⌘Run this prompt
Use the agnost-ai skill to add Agnost AI analytics.✦Build from real production work.
Use your historical traces to create evals, identify repeatable workflows, and train a custom model for the job.
Pricing
Start with traces.
Specialize when you're ready.
The sooner you capture production work, the sooner it can become eval and training data.
Free
- Intents and sentiment, discovered automatically
- Violations across quality, policy, and compliance
- Alerts for silent failures and rising friction
- Self-improvement suggestions for your agent
- Up to 1,000 events / mo
- 7-day data retention
The full product for agents in early production.
Start Free$49/mo
- Everything in Free
- Up to 10,000 events / mo
- 30-day data retention
- Onboarding help
More history and help getting set up.
Start Small$499/mo
- Everything in Starter
- Up to 1,000,000 events / mo
- 90-day data retention
- Founders Slack channel
High-volume agents that need faster iteration.
Start ProCustom
- Everything in Pro
- Train custom models for your workflows
- Custom event volume & retention
- Self-hosted VPC deployments
- Audit logs
- Custom SLAs & SLOs
- Custom improvement workflows
Security, controls, and scale on your terms.
Let's ChatFAQ
Questions,
answered.
Everything you need to know before putting your production conversations to work.
Talk to the foundersI already have observability and traces. Why do I need Agnost?+
Because a trace can say “success” when the user still got nothing useful. We read every trace alongside the conversation, map it back to the user, and catch the silent failures: the agent says it sent the PDF but it never arrived, or confidently answers the wrong question. You see how many users hit it, the traces behind it, and what to fix.
Does this replace frontier models?+
No. Frontier models remain the right choice for open-ended work. Custom models make sense when an agent repeatedly routes requests, selects tools, retrieves data, or formats responses inside a predictable workflow.
How does Agnost create a specialist model?+
Agnost analyzes your historical production traces, turns them into workload-specific training and evaluation data, and trains a model for the narrow jobs your agent actually performs.
Is my data secure?+
The honest answer: we process the conversation data you choose to send us. Use pseudonymous IDs and redact secrets or sensitive fields before ingestion; transport uses HTTPS and dashboard access is authenticated. We do not pretend to automatically redact PII for you. If your team needs a security review, DPA, or a specific deployment setup, talk to us directly.
How complicated is the setup?+
You do not need to rebuild your agent or change how it works. Connect the events and conversations you already have, inspect one staging trace to make sure you are happy with what is being sent, and then turn it on. The point is to get insight from real traffic, not create another implementation project.