⚠️ Interactive Guide

Complete Failure Mode Prevention Guide

Identify, prevent, and recover from 14 common failure patterns in agentic AI implementations

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Integration Failures
Performance Issues
Security Vulnerabilities
Operational Failures
H

API Version Mismatch

Incompatible API versions between agents and legacy systems causing communication breakdowns.

67%
Frequency
4-8h
Avg Downtime
M

Data Format Conflicts

Inconsistent data schemas between systems leading to processing errors and data loss.

54%
Frequency
2-4h
Avg Downtime
H

Authentication Chain Breaks

Token expiration and credential rotation issues disrupting agent communications.

43%
Frequency
1-2h
Avg Downtime
L

Protocol Incompatibility

Communication protocol mismatches between modern agents and legacy systems.

31%
Frequency
2-3h
Avg Downtime
H

Token Limit Exhaustion

Agents consuming API tokens faster than expected, leading to service interruptions.

72%
Frequency
$2-5K
Avg Cost
M

Context Memory Leaks

Unbounded context growth causing performance degradation and system crashes.

38%
Frequency
6-12h
Time to Impact
H

Cascade Timeouts

Chain reaction of timeouts across interconnected agents causing system-wide slowdowns.

51%
Frequency
85%
System Impact
H

Prompt Injection Attacks

Malicious inputs manipulating agent behavior and accessing unauthorized data.

23%
Detected Rate
Critical
Risk Level
H

Sensitive Data Leakage

Agents inadvertently exposing confidential information in logs or responses.

19%
Occurrence
$250K
Avg Fine
M

Agent Privilege Escalation

Agents gaining unauthorized access to resources through role assumption.

12%
Detection Rate
High
Impact
M

Model Drift

Agent performance degradation over time due to changing data patterns.

83%
6-Month Rate
-27%
Accuracy Drop
H

Critical Hallucinations

Agents generating false information in production-critical decisions.

31%
Occurrence
$45K
Avg Loss
L

Multi-Agent Deadlock

Circular dependencies between agents causing system standstill.

22%
Monthly Rate
3-5h
Resolution Time