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 read
AI agents
19 stories
The 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 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 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 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 readThe Revenue Leak Between "Task Completed" and "User Satisfied"
Task completion is not the same as user satisfaction. The revenue leak sits in the gap where your AI agent technically finished the job but the user still would not pay, renew, or trust it again.
Agnost AI Blog7 min readThe Agent Failure That Looks Like Normal Churn
Some AI agent churn is not normal product churn. It is unresolved intent, lost trust, and quiet downgrade behavior hiding inside standard retention metrics.
Agnost AI Blog6 min readThe Founder's Guide to Finding the Next 5 Agent Fixes in Production
A blunt founder guide to finding the five AI agent fixes that actually matter, using production conversations instead of opinions, vibes, and the loudest customer thread.
Agnost AI Blog6 min readThe CTO's Guide to Catching Agent Drift Before Users Churn
Agent drift is when your AI agent keeps returning technically valid responses while real user outcomes slowly get worse. Here's how CTOs can catch it before churn shows up.
Agnost AI Blog7 min readWhen Should an AI Agent Escalate to a Human?
AI agents should escalate when confidence, context, authority, emotion, or user value crosses a risk threshold. Here is a practical escalation framework.
Agnost AI Blog6 min readThe Support Conversations Your Agent Resolved But Your Customer Hated
An AI support agent can mark a ticket resolved while leaving the customer annoyed, confused, or less likely to renew. Here is how to spot resolution without satisfaction.
Agnost AI Blog6 min readEvals, Traces, and Conversations: What Each One Catches
Evals, traces, and conversations are three different quality layers for AI agents. Here is what each catches, what each misses, and how to use them together.
Agnost AI Blog6 min read
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