Agnost AI
Monitors AI agents for silent failures and user issues.
What is Agnost AI?
Agnost AI is an AI Agents tool that monitors AI agents for silent failures and user issues. It is available on Web App. It is aimed at developers and data analysts.
Aids in detecting silent failures, understanding user frustration, and approving suggested fixes for AI agents in production. It analyzes conversations between users and agents to identify issues like behavior drift, hallucinations, and hidden feature requests.
Built for developers and data analysts.
Key capabilities
Features
Events / month
Data retention
Incorrect downgrade guidance
From production signal to shipped fix
Find recurring problems
Follow the failure to its source
Approve the suggested fix
Run this prompt
Catch churn before it happens
Use cases
- Detect silent failures in AI agents
- Identify user frustration through conversation analysis
- Approve suggested fixes for agent issues
Who it's for
Pros & cons
Pros
- Public API available
- Secure by design
Cons
No documented limitations yet.
Developer & API
Public SDK API available
- SDKs
- Python, TypeScript
Security & compliance
- SOC 2
As stated on Agnost AI's website — not independently verified by AiByts.
FAQ
What is Agnost AI?
Agnost AI is an AI Agents tool that monitors AI agents for silent failures and user issues. Aids in detecting silent failures, understanding user frustration, and approving suggested fixes for AI agents in production. It analyzes conversations between users and agents to identify issues like behavior drift, hallucinations, and hidden feature requests.
Does Agnost AI have an API?
Yes, Agnost AI offers a public SDK API for building custom integrations.
What platforms does Agnost AI support?
Agnost AI is available on Web App.
Who should use Agnost AI?
Agnost AI is built for developers and data analysts. Common uses: detect silent failures in AI agents, identify user frustration through conversation analysis and approve suggested fixes for agent issues.
What are the best alternatives to Agnost AI?
Similar tools on AiByts include ModelCap, ChatGLM, Plurai, Yasmine Works and Groas.
Agnost AI news & updates
The July Agent Analytics Playbook
A practical playbook for AI agent teams: measure production conversations, catch silent failures, prioritize fixes, and close the loop from user chat to shipped improvement.
Agnost AI Blog3 min readStop Prompting From Opinions. Prompt From Production Conversations.
The best prompt improvements come from real conversations where users corrected, rephrased, abandoned, or revealed a new intent your agent missed.
Agnost AI Blog3 min readThe Agent Analytics Dashboard You Actually Need
Most AI agent dashboards show system health. Product teams also need intent, trust, friction, handoff, recovery, and improvement loop metrics.
Agnost AI Blog3 min readVoice Agent Silence Is Usually Confusion, Not Patience
Voice agents fail when silence, interruptions, and hesitation are treated like clean turn-taking instead of signals of confusion, distrust, or task risk.
Agnost AI Blog3 min readAI Sales Agents Fail Quietly During Qualification
AI sales agents do not only fail by losing leads. They fail by asking shallow questions, missing buying intent, and producing fake qualification confidence.
Agnost AI Blog3 min readThe First 5 Minutes of AI Agent Onboarding Matter More Than Your Demo
AI agent onboarding succeeds when users learn what to trust, what to delegate, and what the agent needs from them in the first few minutes.
Agnost AI Blog3 min readAgent Memory Failures Show Up First in Production Conversations
AI agent memory failures rarely announce themselves as bugs. They show up as repeated questions, stale assumptions, weird personalization, and lost trust.
Agnost AI Blog3 min readFailed Tool Calls Are a User Experience Problem
AI agent tool failures are not just backend errors. They shape user trust, conversation length, escalation, and whether the agent feels competent.
Agnost AI Blog3 min readSession Replay for AI Agent Conversations
AI agent teams need the conversation version of session replay: not just what the user clicked, but where the agent lost intent, trust, or momentum.
Agnost AI Blog3 min readWhy Thumbs Down Feedback Is Not Enough for AI Agents
Thumbs down feedback catches only the users willing to complain. AI agent teams need conversation signals that reveal silent friction and quiet abandonment.
Agnost AI Blog3 min readHow AI Agents Can Repair Trust After a Bad Answer
AI agents will make mistakes in production. The question is whether the conversation repairs trust or makes the user supervise every future answer.
Agnost AI Blog3 min readThe Human Handoff Quality Score for AI Agents
Human handoff is not just whether an AI agent escalated. It is whether the handoff preserved context, trust, urgency, and user momentum.
Agnost AI Blog3 min readConversation Analytics for Coding Agents: What to Measure After Launch
Coding agents need more than task completion metrics. Here is how to measure confusion, trust, retry loops, and production quality from real developer conversations.
Agnost AI Blog3 min readThe Weekly Agent Review Meeting That Actually Improves the Product
A weekly agent review should not be a random transcript reading party. Here is a practical production conversation review cadence for AI agent teams.
Agnost AI Blog4 min readFrom Weird User Chat to Merged Prompt Fix: The Agent Improvement Loop
A blunt walkthrough of the agent improvement loop: find weird production conversations, classify the failure, turn it into a prompt fix, test it, merge it, and monitor the result.
Agnost AI Blog6 min readA Real Agent Conversation Autopsy: Where the User Gave Up
A practical autopsy of an AI agent conversation where the user gave up, including the exact signals most dashboards miss and how to turn them into fixes.
Agnost AI Blog6 min readWhy Language Tutor Evals Miss Learner Confidence Drops
Language tutor evals often pass while learners quietly lose confidence. Here is how to measure the confidence drop in real tutoring conversations before it becomes churn.
Agnost AI Blog7 min readThe Hidden Failure Modes in AI Recruiting Interviews
AI recruiting interviews fail in ways dashboards rarely show: shallow follow-ups, inconsistent probing, false confidence, candidate fatigue, and trust loss after the call.
Agnost AI Blog7 min read
Details are compiled from Agnost AI's website and public sources, last verified Oct 5, 2026. Features and pricing change — confirm on the official site before you buy.
Try Agnost AI
Visit the official website
Comments
Sign in to join the discussion.