Issues & Diagnosis

How Issue Detection Works

Sentrial analyzes every session automatically to identify problems. No manual tagging required.

Automatic Scanning

Every session is analyzed in real-time as events stream in.

AI Analysis

LLM evaluates sessions for failures, confusion, errors, and inefficiencies.

Smart Grouping

Similar issues are grouped together so you see patterns, not noise.

What We Detect

Issue	Description	Severity
Task Failures	Agent failed to complete the user’s request	High
Tool Errors	A tool call failed, returned unexpected results, or timed out	High
Hallucinations	Agent made claims not supported by context or tools	High
User Frustration	Signs of user dissatisfaction, repeated questions, or negative sentiment	Medium
Confusion Loops	Agent appears stuck, repeating actions or asking the same questions	Medium
Slow Responses	Session took significantly longer than expected	Low
High Cost	Session used more tokens/cost than typical	Low

Issue

Description

Severity

Task Failures

Agent failed to complete the user’s request

High

Tool Errors

A tool call failed, returned unexpected results, or timed out

High

Hallucinations

Agent made claims not supported by context or tools

High

User Frustration

Signs of user dissatisfaction, repeated questions, or negative sentiment

Medium

Confusion Loops

Agent appears stuck, repeating actions or asking the same questions

Medium

Slow Responses

Session took significantly longer than expected

Low

High Cost

Session used more tokens/cost than typical

Low

AI-Powered Diagnosis

The Signals Page

Issue List

All detected issues sorted by severity and frequency.

Trend Charts

See how issue counts change over time.

Filters

Filter by agent, time range, severity, or issue type.

Quick Actions

Jump to diagnosis, affected sessions, or code fixes.

Fixing Issues in Code

Code Integration — Connect your GitHub repository and fix issues directly from the diagnosis page. Chat with AI about the issue, get code suggestions, and create PRs — all in one flow. Learn about Code Integration →

Best Practices

Track session outcomes

Always call complete_session() with success=True/False so we can identify failures.

Include failure reasons

When success=False, include failure_reason for better diagnosis.

Use consistent agent names

Group sessions by agent_name to see issues per agent type.

Track user IDs

Include user_id to identify user-specific issues and patterns.

# Good: Complete session with full context
client.complete_session(
    session_id=session_id,
    success=False,
    failure_reason="Tool 'search_kb' returned empty results for query",
    estimated_cost=0.023
)

# This gives Sentrial the context to:
# - Identify this as a tool failure issue
# - Group similar empty-result failures
# - Suggest fixes (e.g., improve search, add fallback)

Next Steps

Code Integration

Fix issues directly in your codebase.

Sessions & Tracking

Learn how to track sessions properly.

​How Issue Detection Works

Automatic Scanning

AI Analysis

Smart Grouping

​What We Detect

​AI-Powered Diagnosis

​The Signals Page

Issue List

Trend Charts

Filters

Quick Actions

​Fixing Issues in Code

​Best Practices

​Next Steps

Code Integration

Sessions & Tracking

How Issue Detection Works

What We Detect

AI-Powered Diagnosis

The Signals Page

Fixing Issues in Code

Best Practices

Next Steps