AI Incident Reporting System
The AI Incident Reporting System is a structured framework for capturing, classifying, triaging, and resolving incidents in AI systems.
It is designed for teams building applications with large language models, retrieval systems, and agent-based workflows, where failures are not isolated bugs but system-level behaviors.
The system provides a consistent model for turning incidents into actionable improvements.
brandonhimpfen
The Constraint
AI systems do not fail in the same way as traditional software.
Failures are often:
- probabilistic rather than deterministic
- context-dependent rather than reproducible
- distributed across prompts, models, data, and tools
Without structure, incidents are treated as isolated issues.
With structure, they become signals that can be aggregated, analyzed, and resolved at the system level.
What This System Provides
This project introduces a complete foundation for AI incident management:
- A standardized incident schema
- A severity model aligned to real-world impact
- A lifecycle for triage, investigation, and resolution
- A taxonomy for consistent classification
- Database schemas for structured storage
- JSON contracts for ingestion and interoperability
- Example incidents for common failure modes
The goal is to establish a system of record for AI behavior.
Incident Model
Incidents are defined across multiple dimensions:
- Type
misuse, hallucination, unsafe output, bias, privacy, system failure - Severity
from informational signals to critical failures with real-world impact - Surface area
input, model behavior, retrieval, tools, or system integration
This allows incidents to be grouped and analyzed beyond individual reports.
Lifecycle
Each incident follows a structured lifecycle:
- New
- Triaged
- Investigating
- Resolved
- Closed
This mirrors incident management practices in reliability engineering, adapted for AI systems.
From Reporting to Learning
The system is designed to convert incidents into improvements.
Each incident can be linked to:
- root cause
- corrective action
- system change
Examples of resulting actions:
- prompt updates
- guardrail implementation
- retrieval improvements
- evaluation additions
- policy refinement
This creates a feedback loop where incidents improve the system over time.
Architecture
The system follows a simple but extensible structure:
- ingestion layer for reports and automated signals
- classification and labeling
- centralized incident store
- triage and workflow management
- analysis and clustering
- feedback loop into models and systems
This allows incremental adoption without requiring a full platform upfront.
Use Cases
This system is useful for:
- AI applications in production environments
- RAG-based systems requiring traceability
- agent-based systems with tool access
- platforms with user-generated prompts
- teams implementing AI safety and governance practices
Positioning
This project is not a dashboard or monitoring tool.
It is a structured foundation for:
- AI reliability
- AI safety workflows
- incident-driven system improvement
- governance and audit readiness
It can be used as a standalone system or integrated into existing infrastructure.
Repository Contents
The repository includes:
- schemas for incidents and related objects
- database migrations for structured storage
- documentation covering models and lifecycle
- example incident reports
- issue templates for consistent reporting
How to Use
Start by adopting the incident schema and taxonomy.
Integrate reporting into your application through user reporting, internal tooling, or automated detection.
Store incidents in a structured database and apply the lifecycle model.
From there, build analysis and feedback workflows that connect incidents to system improvements.
Design Principle
This system is built on a single principle: AI incidents should not be treated as isolated failures. They should be treated as structured inputs into a continuous improvement system.