ADR-026-v4: Error Handling Architecture - Part 1: Narrative

Document: ADR-026-v4-error-handling-architecture-part1-narrative
Version: 2.0.0
Purpose: Comprehensive error handling strategy for CODITECT v4
Audience: Business stakeholders, technical architects, operations teams, developers
Date Created: 2025-09-02
Date Modified: 2025-09-02
Status: DRAFT

Table of Contents

1. Document Information
2. Purpose of this ADR
3. User Story Context
4. Executive Summary
5. Visual Overview
6. Background & Problem Statement
7. Decision
8. Implementation Blueprint
9. Testing Strategy
10. Security Considerations
11. Performance Characteristics
12. Operational Considerations
13. Migration Strategy
14. Consequences
15. References & Standards
16. Review & Approval
17. Appendix
18. QA Review Block

1. Document Information 🔴 REQUIRED

Field	Value
ADR Number	ADR-026
Title	Error Handling Architecture
Status	Draft
Date Created	2025-09-02
Last Modified	2025-09-02
Version	2.0.0
Decision Makers	CTO, Chief Architect, Product Manager
Stakeholders	All CODITECT teams, customers, support teams

2. Purpose of this ADR 🔴 REQUIRED

This ADR serves dual purposes:

• For Humans 👥: Understand how CODITECT transforms frustrating error experiences into helpful guidance that users can act on
• For AI Agents 🤖: Implement a comprehensive error handling system with recovery mechanisms, logging, and monitoring

3. User Story Context 🔴 REQUIRED

As a developer using CODITECT,
I want clear, actionable error messages when something goes wrong,
So that I can quickly resolve issues without contacting support.

As a business user,
I want friendly explanations when errors occur,
So that I understand what happened and what to do next.

As an operations engineer,
I want comprehensive error logging and monitoring,
So that I can proactively prevent issues and quickly diagnose problems.

📋 Acceptance Criteria:

• All errors provide user-friendly messages with recovery options
• Error logs contain full context for debugging
• System automatically recovers from transient failures
• No sensitive information leaked in error messages
• 95% of errors are self-resolvable by users
• Sub-5 minute mean time to error resolution

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4. Executive Summary 🔴 REQUIRED

🏢 For Business Stakeholders

Imagine you're driving a car and the engine suddenly stops. A good car doesn't just quit - it shows warning lights, plays alert sounds, and safely guides you to the side of the road. Software errors are similar - things go wrong, but HOW the system handles them makes all the difference.

Current Industry Problem:

• Cryptic error messages frustrate users
• Support teams overwhelmed with preventable tickets
• Lost productivity from unresolved errors
• Security risks from exposed technical details

CODITECT's Solution: Three-layer error handling that transforms frustration into resolution:

1. User Layer: Friendly messages with clear next steps
2. Developer Layer: Complete context for rapid debugging
3. System Layer: Automatic recovery and self-healing

Business Value:

• 95% reduction in error-related support tickets
• $50,000/month savings in support costs
• 25% improvement in user retention
• 10x faster issue resolution

💻 For Technical Readers

Technical Summary: Comprehensive error handling architecture with structured error codes, contextual recovery mechanisms, multi-layer logging, and automatic failover patterns. The system implements retry logic, fallback providers, state preservation, and intelligent error classification with user-appropriate messaging.

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5. Visual Overview 🔴 REQUIRED

5.1 Error Flow Architecture

5.2 Three-Layer Error Strategy

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6. Background & Problem Statement 🔴 REQUIRED

6.1 Business Context

Current Industry Problems:

1. Cryptic Error Messages

   • "NullPointerException at line 2847"
   • "Connection refused: ECONNREFUSED"
   • Users have no idea what went wrong or how to fix it

2. Lost Work

   • Errors cause data loss
   • No recovery options
   • Hours of work vanished

3. Security Leaks

   • Error messages revealing system internals
   • Stack traces exposing file paths
   • Database errors showing schema

4. Poor Developer Experience

   • Debugging takes forever
   • Can't reproduce user issues
   • Logs are useless or missing

6.2 Technical Context

CODITECT's Unique Challenges:

1. Multi-Tenant Isolation

   • Errors from one tenant must NEVER affect another
   • Error logs must maintain tenant boundaries
   • Recovery must be tenant-specific

2. Ephemeral Containers

   • Containers disappear, but error context must persist
   • State recovery after container crashes
   • Graceful handling of container limits

3. AI Integration

   • AI model failures need fallbacks
   • Token limit errors need smart handling
   • Model unavailability needs alternatives

6.3 Constraints

Type	Constraint	Impact
⏰ Time	2-month implementation	Phased rollout by service
💰 Budget	Use existing infrastructure	Leverage FoundationDB, Prometheus
👥 Resources	Current team only	Reusable error components
🔧 Technical	Zero downtime deployment	Blue-green error handler updates
📜 Compliance	GDPR/SOC2 requirements	No PII in error logs

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7. Decision 🔴 REQUIRED

7.1 Y-Statement Format

In the context of providing exceptional user experience and operational excellence,
facing cryptic errors, overwhelmed support teams, and security risks,
we decided for three-layer error handling with automatic recovery
and neglected traditional stack-trace-only error reporting,
to achieve 95% self-resolved errors and 10x faster debugging,
accepting increased implementation complexity and monitoring overhead,
because user satisfaction and operational efficiency drive platform success.

7.2 What We're Doing

Implementing a comprehensive error handling system:

1. Structured Error Codes

   • Service-Category-Code format (e.g., AUTH-USR-1001)
   • Consistent across all services
   • Machine-readable and human-friendly

2. Three-Layer Strategy

   • User Layer: Friendly messages with actions
   • Developer Layer: Complete debugging context
   • System Layer: Automatic recovery mechanisms

3. Recovery Mechanisms

   • Automatic retry with exponential backoff
   • Fallback service providers
   • State preservation and restoration
   • Graceful degradation

4. Comprehensive Logging

   • Structured JSON logs
   • Correlation IDs across services
   • Privacy-preserving context
   • Real-time monitoring integration

7.3 Error Categories and Handling

User Errors (Their Fault, Our Guidance)

• Invalid Input: Clear validation messages
• Exceeded Limits: Explain limits and upgrade options
• Permission Denied: Explain why and how to get access

System Errors (Our Fault, Our Fix)

• Service Unavailable: Automatic failover
• Resource Exhaustion: Auto-scaling
• Bug Crashes: State preservation and recovery

External Errors (Nobody's Fault, Everyone's Problem)

• Network Issues: Offline mode activation
• Third-party Failures: Fallback providers
• Rate Limits: Intelligent backoff

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8. Implementation Blueprint 🔴 REQUIRED

8.1 Architecture Overview

The error handling system consists of:

• Error Code Registry: Centralized error definitions
• Error Handler Chain: Pluggable recovery mechanisms
• Context Extractor: Automatic context gathering
• Recovery Engine: Retry and fallback logic
• Logging Pipeline: Structured error logging
• Monitoring Integration: Real-time alerting

8.2 Core Components

Error Structure:

CoditechError {
  id: Unique error instance ID
  code: Structured error code
  message: User-friendly message
  details: Technical details (hidden from users)
  context: Request/user/tenant information
  recovery: Suggested actions
  timestamp: When it occurred
}

8.3 Implementation Phases

Phase 1: Foundation (Weeks 1-2)

• Error code system design
• Core error types implementation
• Basic logging infrastructure

Phase 2: Intelligence (Weeks 3-6)

• Context extraction middleware
• Recovery mechanisms
• Handler chain implementation

Phase 3: Excellence (Weeks 7-8)

• AI-powered error suggestions
• Pattern detection
• Self-healing capabilities

8.4 API Integration

All APIs will return errors in consistent format:

{
  "error_id": "550e8400-e29b-41d4-a716-446655440000",
  "code": "API-USR-4001",
  "message": "The file name contains invalid characters",
  "recovery": {
    "actions": [
      {
        "label": "Fix automatically",
        "action": "auto_fix_filename"
      },
      {
        "label": "See naming rules",
        "action": "show_help"
      }
    ]
  },
  "timestamp": "2025-09-02T10:30:00Z"
}

8.5 User Interface

Error displays will be:

• Non-modal when possible (inline validation)
• Contextual (appear where the error occurred)
• Actionable (clear next steps)
• Dismissible (after user acknowledgment)

8.6 Logging Requirements

All errors must be logged with:

• Unique Error ID: For tracking across systems
• Full Context: User, tenant, action, request details
• Stack Trace: For technical debugging
• Performance Impact: Time to detect and recover
• Resolution: How the error was resolved

Example log entry:

{
  "timestamp": "2025-09-02T10:30:00.123Z",
  "level": "ERROR",
  "error_id": "550e8400-e29b-41d4-a716-446655440000",
  "code": "CNTR-SYS-3002",
  "service": "container-manager",
  "tenant_id": "tenant_123",
  "user_id": "user_456",
  "action": "create_workspace",
  "message": "Container memory limit exceeded",
  "details": {
    "requested_memory": "8GB",
    "available_memory": "4GB",
    "container_id": "cntr_789"
  },
  "recovery": {
    "action_taken": "auto_upgrade_memory",
    "success": true,
    "duration_ms": 1250
  },
  "stack_trace": "..."
}

8.7 Error Handling Patterns

Retry Pattern:

1. First attempt fails
2. Wait 1 second, retry
3. Wait 2 seconds, retry
4. Wait 4 seconds, retry
5. Fail permanently, activate fallback

Fallback Pattern:

Primary Service → Fails → Secondary Service → Fails → Degraded Mode

Circuit Breaker Pattern:

10 failures in 1 minute → Open circuit → Wait 30 seconds → Test → Resume or stay open

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9. Testing Strategy 🔴 REQUIRED

9.1 Test Scenarios

1. Error Generation Tests

   • Verify all error codes are unique
   • Ensure messages are user-friendly
   • Validate recovery options work

2. Recovery Tests

   • Retry logic functions correctly
   • Fallback services activate
   • State preservation works

3. Security Tests

   • No sensitive data in user messages
   • Stack traces hidden from users
   • Tenant isolation maintained

4. Performance Tests

   • Error handling adds <10ms overhead
   • Logging doesn't block operations
   • Recovery happens within SLA

9.2 Test Coverage Requirements

Component	Unit	Integration	E2E
Error Codes	≥100%	≥95%	≥90%
Recovery Logic	≥95%	≥90%	≥85%
Logging Pipeline	≥90%	≥85%	≥80%
User Messages	≥100%	≥95%	≥90%

9.3 Chaos Testing

• Randomly inject errors in staging
• Verify recovery mechanisms activate
• Ensure no data loss occurs
• Monitor user experience impact

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10. Security Considerations 🔴 REQUIRED

10.1 Information Disclosure Prevention

Never expose in error messages:

• Internal file paths
• Database schemas
• API keys or secrets
• Other tenant information
• System architecture details

Always sanitize:

• User input in error messages
• File paths to relative only
• Sensitive parameter values
• Internal service names

10.2 Error-Based Attacks

Protect against:

• Enumeration: Don't reveal if resources exist
• Timing Attacks: Consistent error response times
• Log Injection: Sanitize all logged data
• DoS via Errors: Rate limit error endpoints

10.3 Compliance Requirements

• GDPR: No PII in error logs beyond 30 days
• SOC2: Audit trail for all system errors
• HIPAA: Extra sanitization for healthcare tenants
• PCI: No payment data in any errors

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11. Performance Characteristics 🔴 REQUIRED

11.1 Expected Metrics

Operation	Target	Actual	Notes
Error Detection	<1ms	TBD	Time to identify error
Message Generation	<5ms	TBD	User-friendly message
Context Extraction	<10ms	TBD	Full debugging context
Recovery Attempt	<100ms	TBD	First retry attempt
Logging Pipeline	<5ms	TBD	Async, non-blocking

11.2 Resource Impact

• Memory: ~100MB for error message cache
• CPU: <1% overhead for error handling
• Network: ~1KB per error log entry
• Storage: ~10GB/month for error logs

11.3 Scalability

• Handle 10,000 errors/second
• Log retention for 90 days
• Real-time monitoring for 1,000 error types
• Support 100,000 unique error codes

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12. Operational Considerations 🔴 REQUIRED

12.1 Monitoring

Key Metrics:

• Error rate by service/category/code
• Recovery success rate
• Mean time to resolution
• Support ticket correlation

Alerts:

• Error rate spike (>10x baseline)
• New error codes appearing
• Recovery failure rate >5%
• Critical system errors

12.2 Maintenance

Regular Tasks:

• Review new error patterns weekly
• Update user messages based on feedback
• Optimize recovery strategies
• Archive old error logs

12.3 Runbooks

High Error Rate:

1. Check monitoring dashboard
2. Identify error pattern
3. Activate additional resources
4. Enable enhanced logging
5. Implement temporary fix
6. Schedule permanent resolution

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13. Migration Strategy 🔴 REQUIRED

13.1 Phase 1: Core Services (Week 1-2)

• Authentication service
• API gateway
• Database layer
• Container manager

13.2 Phase 2: User-Facing Services (Week 3-4)

• Web application
• CLI tools
• WebSocket services
• File services

13.3 Phase 3: AI Services (Week 5-6)

• Model inference
• Training pipelines
• Prompt management
• Agent orchestration

13.4 Rollback Plan

If issues arise:

1. Feature flag to disable new error handling
2. Revert to previous error messages
3. Maintain new logging for debugging
4. Fix issues and re-enable gradually

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14. Consequences 🔴 REQUIRED

14.1 Positive Outcomes

✅ User Experience:

• Clear, actionable error messages
• Self-service problem resolution
• Reduced frustration and support needs
• Increased platform confidence

✅ Developer Productivity:

• 10x faster debugging
• Complete error context
• Pattern identification
• Proactive issue prevention

✅ Business Benefits:

• 95% reduction in error tickets
• $50k/month support savings
• Higher user retention
• Better security posture

14.2 Negative Impacts

⚠️ Implementation Complexity:

• More code to maintain
• Complex recovery logic
• Additional testing needed
• Performance overhead

⚠️ Operational Overhead:

• More monitoring required
• Log storage costs
• Alert fatigue risk
• Training needs

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15. References & Standards 🔴 REQUIRED

15.1 Related ADRs

• ADR-001-v4: Container Execution - Container error handling
• ADR-005-v4: Authentication - Auth error patterns
• ADR-008-v4: Monitoring - Error monitoring integration
• ADR-011-v4: Audit & Compliance - Error audit requirements

15.2 External Standards

• RFC 7807 - Problem Details for HTTP APIs
• Google Error Model - API error handling
• AWS Error Best Practices
• OWASP Error Handling - Security guidelines

15.3 Foundation Standards

• LOGGING-STANDARD-v4 - Logging patterns
• ERROR-HANDLING-STANDARD-v4 - Error patterns
• TEST-DRIVEN-DESIGN-STANDARD-v4 - Testing requirements

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16. Review & Approval 🔴 REQUIRED

Approval Signatures

Role	Name	Signature	Date
Author	SESSION8-ORCHESTRATOR	✓	2025-09-02
Technical Reviewer	Pending	-	-
Business Reviewer	Pending	-	-
Security Officer	Pending	-	-
Final Approval	Pending	-	-

Review History

Version	Date	Reviewer	Status	Comments
1.0.0	2025-09-02	SESSION8-ORCHESTRATOR	DRAFT	Initial creation
2.0.0	2025-09-02	SESSION8-QA-REVIEWER	REVISION	Added v4.2 compliance

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17. Appendix

17.1 Error Code Registry

Code Pattern	Description	Example
AUTH-USR-1xxx	Authentication user errors	AUTH-USR-1001: Invalid credentials
AUTH-SYS-1xxx	Authentication system errors	AUTH-SYS-1004: Auth service down
DB-USR-2xxx	Database user errors	DB-USR-2002: Record not found
DB-SYS-2xxx	Database system errors	DB-SYS-2001: Connection failed
CNTR-USR-3xxx	Container user errors	CNTR-USR-3002: Resource limit
CNTR-SYS-3xxx	Container system errors	CNTR-SYS-3001: Creation failed
API-USR-4xxx	API user errors	API-USR-4001: Invalid request
API-SYS-4xxx	API system errors	API-SYS-4004: Service unavailable

17.2 Example Error Messages

Before vs After

Old Way:

Error: ENOSPC
at /usr/src/app/node_modules/fs/write.js:247
at FSReqCallback.oncomplete (fs.js:155:23)

CODITECT Way:

Unable to save "ProjectPlan.md" - You're out of storage space

You're using 9.8 GB of your 10 GB limit.

Options:
• Free up space (3 large files using 2 GB)
• Save to cloud (unlimited storage)
• Upgrade plan (+50 GB for $5/month)

Your work is safely cached and will save once you free space.

17.3 Recovery Flow Examples

AI Service Error Recovery:

1. Primary AI model timeout (Claude)
2. Log error with context
3. Switch to secondary model (GPT-4)
4. Notify user of slight delay
5. Complete operation successfully
6. Log recovery success

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18. QA Review Block

Status: AWAITING INDEPENDENT QA REVIEW

This section will be completed by an independent QA reviewer according to ADR-QA-REVIEW-GUIDE-v4.2.

Document ready for review as of: 2025-09-02
Version ready for review: 2.0.0

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Next: See Part 2: Technical Implementation for complete implementation details.