FLY-ISOLATE: Implement failure isolation to prevent cascade failures
Labels: enhancement, fly-methodology, reliability
Description
The mock data incident showed how one bad pattern (fallback to mocks) can cascade through multiple layers. We need isolation mechanisms to contain failures.
Problem
- Mock fallbacks hid real failures
- Agents validated mock data as success
- Failure cascaded through validation layers
- No circuit breakers to stop propagation
Solution
- Clear service boundaries
- Explicit failure modes
- Circuit breakers for integrations
- Fail-fast with clear errors
Implementation
Success Metrics
- Zero cascade failures
- 100% of failures isolated to originating service
- Clear error messages at each boundary
- No mock data or theater validation
Estimated: 6 hours
Priority: High (prevents future incidents)
Technical Implementation
Service Boundaries
- API Layer: FastAPI endpoints with clear error responses
- Service Layer: Business logic with explicit failure modes
- Integration Layer: External APIs with circuit breakers
- Data Layer: Database operations with transaction boundaries
Circuit Breaker Pattern
class IntegrationCircuitBreaker:
def __init__(self, failure_threshold=5, timeout=60):
self.failure_count = 0
self.failure_threshold = failure_threshold
self.timeout = timeout
self.last_failure_time = None
self.state = "CLOSED" # CLOSED, OPEN, HALF_OPEN
Health Check Endpoints
/health/github - GitHub API connectivity
/health/calendar - Google Calendar API status
/health/database - Database connection status
/health/overall - System-wide health status
Failure Isolation Rules
- No Silent Failures: All failures must be logged and reported
- No Mock Fallbacks: Replace with honest error messages
- Clear Boundaries: Each service has defined responsibilities
- Fail Fast: Stop processing when critical dependencies fail
- User Transparency: Show real status, not fake success
Integration Points
- Gameplan template updates
- Agent prompt templates
- Service architecture documentation
- Testing methodology updates