A newer version of the Gradio SDK is available:
6.1.0
🔧 Error Recovery & Self-Healing Logic
📋 Task Overview
Task ID: Task-1.1.3
Phase: Phase 1 - Core Enhancement
Priority: Critical
Duration: 2 weeks
Owner: AI Agent Team
Dependencies: Task-1.1.2 (Agent Coordination Framework)
🎯 Objective
Implement advanced error recovery and self-healing capabilities that build upon the existing sophisticated error handling to provide autonomous error resolution across multi-agent workflows.
📊 Current Status
- ✅ Excellent foundation: McpExecutorAgent has advanced error categorization and recovery suggestions
- ✅ Professional error handling: 516+ tests include comprehensive error scenarios
- ⚠️ Missing: Automated recovery execution and multi-agent error coordination
- ⚠️ Gap: Self-healing workflows that learn from failures
📋 Requirements
- Automated error diagnosis with confidence scoring
- Recovery strategy generation and execution
- Fallback tool selection and substitution
- Learning from failed executions
- Success rate improvement tracking
- Multi-agent error coordination via SupervisorAgent
💻 Implementation Details
class ErrorRecoverySystem:
"""Advanced error recovery with self-healing capabilities."""
def __init__(self, orchestrator: AgentOrchestrator):
self.orchestrator = orchestrator
self.failure_history = FailureHistoryTracker()
self.recovery_strategies = RecoveryStrategyEngine()
def diagnose_error(self, error_context: ErrorContext) -> DiagnosisResult:
"""Analyze errors with confidence scoring and categorization."""
def generate_recovery_plan(self, diagnosis: DiagnosisResult) -> RecoveryPlan:
"""Generate automated recovery strategies."""
def execute_recovery(self, plan: RecoveryPlan) -> RecoveryResult:
"""Execute recovery plan with fallback options."""
def learn_from_failure(self, failure: FailureContext) -> None:
"""Update recovery strategies based on failure patterns."""
class FailureHistoryTracker:
"""Track and analyze failure patterns for learning."""
class RecoveryStrategyEngine:
"""Generate context-aware recovery strategies."""
✅ Acceptance Criteria
- ErrorRecoverySystem integrated with existing error handling
- Automated error diagnosis with 90%+ accuracy
- Recovery strategy generation working for common error types
- Fallback tool selection functional
- Learning mechanism improves recovery success over time
- Integration with SupervisorAgent and coordination framework
- Self-healing workflows demonstrate improvement
- Maintains existing 99.8% test pass rate
🔗 Dependencies
- Depends on: Task-1.1.2 (Agent Coordination Framework)
- Builds on: Existing McpExecutorAgent error handling (already excellent)
- Integrates with: SupervisorAgent for multi-agent error coordination
📈 Success Metrics
- Error recovery success rate > 90%
- Time to recovery < 5 seconds for common errors
- Learning improvement: 10% better recovery rate after 100 failures
- Multi-agent error resolution success > 85%
- System uptime improvement measurable
🏷️ Tags
error-recovery, self-healing, machine-learning, fault-tolerance, phase-1
📂 File Structure
agents/
supervisor/
error_recovery.py
failure_tracker.py
recovery_strategies.py
tests/
agents/
supervisor/
test_error_recovery.py
test_failure_learning.py
test_recovery_strategies.py
🔄 Integration Points
- Extends existing McpExecutorAgent error handling
- Uses AgentOrchestrator for multi-agent recovery
- SupervisorAgent coordinates recovery across agents
- Learning system improves over time
💡 Enhancement Areas
- Build on existing error categorization in McpExecutorAgent
- Extend current recovery suggestions to automated execution
- Add pattern recognition to existing error logging
- Integrate with existing retry mechanisms