Reliability Patterns includes six types: model fallback chains for backup AI models, graceful degradation for partial functionality during failures, circuit breakers to stop cascading failures, retry strategies for transient errors, timeout handling to prevent indefinite waits, and idempotency for safe retries. The right choice depends on failure type and recovery requirements. Most systems need circuit breakers and retries as baseline protection. Fallback chains handle AI failures. Graceful degradation keeps systems partially working. Timeouts prevent resource exhaustion. Idempotency prevents duplicates.
Your AI assistant stops responding at 2 AM on a Saturday. By Monday, 847 messages sit unanswered.
A payment API times out. Your system retries. And retries. Now there are duplicate charges.
One slow database query backs up your entire queue. Everything freezes while one thing waits.
The question is not if your dependencies will fail. It is what happens when they do.
Part of Layer 5: Quality & Reliability - Making systems that keep running when things break.
Reliability Patterns are design approaches that help systems handle failures gracefully. Instead of crashing when an API times out or a service goes down, these patterns detect problems, route around them, and keep operating until things recover.
Most outages are not caused by one thing breaking. They are caused by one failure cascading into many. These patterns contain failures at the source before they spread through your entire system.
Each pattern handles a different type of failure. Choosing wrong means building protection that does not help when you need it.
Fallbacks | Degradation | Breakers | Retries | Timeouts | Idempotency | |
|---|---|---|---|---|---|---|
| Problem Solved | Transient errors that resolve quickly | Operations that hang indefinitely | Retries create duplicates | |||
| When It Activates | Immediately after each failure | When time limit is exceeded | Before executing any operation | |||
| What Happens | Wait and try again | Cancel and run fallback | Return cached result if duplicate | |||
| Implementation Effort | Low - simple loop with delays | Low - set time limits | Medium - need request tracking |
The right choice depends on what failure you are protecting against. Most systems need multiple patterns working together.
“My AI provider has occasional outages and I need continuous availability”
Fallback chains switch to backup models automatically when the primary fails.
“One broken feature should not stop everything else from working”
Graceful degradation isolates failures so working features stay available.
“Retries to a dead service are piling up and crashing my system”
Circuit breakers stop sending requests to failing services, preventing cascade.
“Occasional API timeouts cause workflows to fail permanently”
Retries handle transient failures by waiting and trying again automatically.
“Slow responses from one service are blocking my entire queue”
Timeouts prevent indefinite waits so resources do not pile up behind slow calls.
“Network failures during payment cause double charges”
Idempotency ensures retries produce the same result, preventing duplicates.
“I need protection against all of the above”
Layer patterns: timeouts on all calls, circuit breakers per service, retries with backoff, fallbacks for critical paths, and idempotency for sensitive operations.
Answer a few questions to get a recommendation.
Reliability patterns are not about preventing failures. Failures are inevitable. These patterns are about controlling what happens when things break.
An external dependency fails or becomes unreliable
Detect the failure, contain it, route around it, and continue operating
One broken service does not become a complete outage
When your AI-powered support goes silent because a provider is down...
That's a fallback chain problem - switch to a backup model and keep responding.
When a payment API times out and the retry charges the card twice...
That's an idempotency problem - retries should find the existing charge, not create new ones.
When one data source is slow and the entire dashboard times out...
That's a graceful degradation problem - show available data with a notice that one section is loading.
When a third-party API goes down and queues back up for hours...
That's a circuit breaker problem - stop queuing requests that will fail and alert operators.
Which of these sounds most like your current situation?
These patterns seem simple until you implement them. The details matter.
Move fast. Structure data “good enough.” Scale up. Data becomes messy. Painful migration later. The fix is simple: think about access patterns upfront. It takes an hour now. It saves weeks later.
Reliability patterns are design approaches that help systems recover from failures without human intervention. They include fallback chains for switching to backup services, circuit breakers for stopping cascading failures, retry strategies for handling transient errors, timeout handling for preventing indefinite waits, graceful degradation for maintaining partial functionality, and idempotency for making operations safe to repeat.
Start with circuit breakers and retry strategies as baseline protection for any system calling external services. Add timeouts to every external call. If you use AI models, add fallback chains. If failures are common, add graceful degradation. If you process payments or other sensitive operations, add idempotency. Most production systems need 3-4 patterns working together.
Retry strategies help with transient failures by trying again after a brief wait. Circuit breakers detect when a service is consistently failing and stop sending requests entirely. Use retries when failures are brief and intermittent. Use circuit breakers to prevent overwhelming a struggling service with more requests. They work together: circuit breakers protect against repeated retry attempts.
Use fallback chains when you have a direct replacement for a failing service, like a backup AI model. Use graceful degradation when no replacement exists but partial functionality is better than none. Fallback chains switch to alternatives. Graceful degradation disables non-essential features or serves cached data while keeping core functions running.
Idempotency ensures that running an operation multiple times produces the same result as running it once. You need it anywhere retries could cause duplicates: payment processing, order creation, data synchronization. Without idempotency, a network timeout followed by a retry could charge a customer twice or create duplicate records.
The biggest mistakes are: retrying errors that will never succeed (like authentication failures), setting timeouts too short for legitimate slow operations, not testing fallback paths, circuit breakers that trip too late after damage is done, and checking for idempotency after executing instead of before. Each pattern needs proper configuration for your specific failure scenarios.
Yes, most production systems layer multiple patterns. A typical setup: timeouts on all external calls, circuit breakers per external service, retries with exponential backoff for transient failures, fallback chains for critical AI models, graceful degradation for non-essential features, and idempotency for sensitive operations. The patterns complement each other.
When one service fails, requests pile up waiting for it, consuming resources until your entire system stops. Timeouts prevent indefinite waits. Circuit breakers stop sending requests to dead services. Graceful degradation routes around failed components. Together, they contain failures to the affected service instead of letting problems spread through your entire system.
Timeouts set a maximum wait time for individual requests. Circuit breakers track failure patterns across many requests and temporarily disable a failing service. Use timeouts on every external call to prevent hanging. Use circuit breakers to detect when a service has become consistently unhealthy and stop calling it entirely until it recovers.
AI systems depend on external model APIs that can fail, time out, or hit rate limits. Model fallback chains switch to backup models during outages. Timeouts prevent slow inference from blocking workflows. Circuit breakers stop calls during provider outages. Retries handle brief API glitches. Idempotency prevents duplicate AI-triggered actions when requests are retried.
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