AI RNG: Practical Systems That Ship
Most production outages are not caused by one error. They are caused by how the system responds to errors. A slow dependency turns into a retry storm. A transient timeout triggers duplicate writes. A “best effort” background job fills a queue until everything else falls behind. Users do not experience “an exception.” They experience cascading failure.
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Good error handling and retry design is a form of respect for reality. Networks fail. Disks fill. Locks contend. Dependencies return partial answers. Your job is to decide, ahead of time, which failures are acceptable, which must be surfaced, and which can be retried safely without making the system worse.
AI can help you build the matrix faster: classify errors, propose policies, generate test cases, and identify hidden edge cases in flows. The judgment remains yours, because the system is the one that pays the bill.
Start with a simple promise: what does this call mean
Every boundary call in your system has an implied promise.
- If it fails, did anything happen?
- If I retry, could I make it worse?
- If it times out, is the operation still running?
- If the dependency is slow, how long am I willing to wait?
If you cannot answer these questions, retries become gambling.
A practical move is to define a contract for each critical call: idempotency, time budgets, and what “success” actually means.
Build an error taxonomy that supports decisions
Errors become manageable when they map to actions. A useful taxonomy is not “500 vs 400.” It is “retry vs do not retry vs escalate.”
| Error class | Typical examples | Safe default behavior | Notes that prevent incidents |
|---|---|---|---|
| Validation / caller faults | malformed input, missing fields, permission denied | do not retry | treat as a contract violation and return a clear error |
| Not found / precondition | missing record, version conflict, stale write | do not retry automatically | retry might be correct only after state refresh |
| Transient dependency | timeouts, connection resets, 503s | retry with backoff and jitter | cap retries and honor a total time budget |
| Rate limiting | 429s, quota exceeded | retry only if instructed | respect retry-after and avoid synchronized retries |
| Resource exhaustion | disk full, memory pressure, queue full | stop and shed load | retries amplify failure when resources are exhausted |
| Unknown / programmer error | null references, invariant breaks | fail fast and alert | retries usually repeat the same failure |
The goal is to make the correct action obvious in code. If everything becomes a generic exception, the system will treat all failures the same, and that rarely ends well.
Retries only work when operations are safe to repeat
The central question in retry design is idempotency.
An operation is safe to retry when repeating it has the same effect as doing it once.
- A read is usually safe to retry.
- A write is safe only when it is idempotent by design.
- A “create” can be safe if it uses an idempotency key or a natural unique constraint.
- A payment, email, or notification is almost never safe to retry blindly.
If a call is not idempotent, you can still design reliability, but you need explicit mechanisms:
- idempotency keys stored server-side
- unique constraints that turn duplicates into harmless no-ops
- outbox patterns that separate state change from external effects
- deduplication in consumers for at-least-once delivery systems
AI can help by scanning a flow and listing the steps that are non-idempotent, then proposing where to add keys or dedupe. You still confirm the real semantics.
Backoff and jitter: the difference between resilience and a stampede
When many clients retry at the same time, they synchronize. This causes load spikes exactly when the dependency is weakest.
Backoff spreads retries over time. Jitter spreads them across clients.
A practical policy usually includes:
- exponential backoff for transient failures
- random jitter per attempt
- a cap on maximum delay
- a hard cap on total retry time across all attempts
The hard cap matters. Without it, a call can consume your entire request budget and hold resources hostage.
Timeouts are part of the contract, not an implementation detail
A timeout is not a nice-to-have. It is how you choose what to abandon in order to keep the system alive.
Design timeouts as budgets:
- per call timeout: how long you wait for this dependency
- total request budget: how long the user request can run
- queue time budget: how long a job can sit before it becomes meaningless
If you retry, the per call timeout and the total budget must align. A common incident pattern is a system that retries aggressively while also using large timeouts, creating long threads and massive concurrency under failure.
Circuit breakers and bulkheads keep one dependency from taking everything down
When a dependency is failing, your best move is often to stop calling it for a short period.
Circuit breakers do this by:
- tracking failure rates
- opening when failures cross a threshold
- allowing limited test traffic to see if recovery occurs
Bulkheads do this by:
- limiting concurrency per dependency
- isolating pools so one slow call cannot exhaust all workers
These patterns are not fancy. They are the simplest way to prevent collapse when reality becomes unfriendly.
Error messages should be useful without being dangerous
Error messages are part of your interface. They should help legitimate callers fix their requests, and they should not leak sensitive detail.
A healthy division is:
- user-facing error: clear, stable, minimal
- internal log: detailed, correlated, safe from secrets
AI is useful for consistency here. It can scan error handling blocks and suggest places where raw exceptions, stack traces, or tokens might leak into responses.
How AI helps you design the policy and the tests
AI can reduce the “blank page” time:
- propose an error taxonomy for your domain
- suggest retry policies per endpoint or job type
- identify where idempotency is missing
- generate a set of test cases that validate safety
The strongest use is test design. If you can describe the contract, AI can help produce tests that verify:
- no duplicates under retries
- correct behavior under timeouts
- correct mapping of error classes to retry decisions
- correct respect for retry-after headers
- no sensitive leakage in error responses
Then you run the tests against the real system behavior and adjust.
A sanity checklist for retry safety
- Retries are limited by a total time budget.
- Retried operations are idempotent or protected by dedupe.
- Backoff and jitter prevent synchronization.
- Timeouts are explicit and consistent with budgets.
- Circuit breakers prevent self-inflicted overload.
- Error mapping is stable and visible in code.
- Logs and metrics allow you to see retries, not just failures.
A system does not become reliable by hoping that the network behaves. It becomes reliable when it treats failure as normal and reacts in a way that protects users, data, and uptime.
Keep Exploring AI Systems for Engineering Outcomes
AI for Performance Triage: Find the Real Bottleneck
https://ai-rng.com/ai-for-performance-triage-find-the-real-bottleneck/
AI for Fixing Flaky Tests
https://ai-rng.com/ai-for-fixing-flaky-tests/
AI for Logging Improvements That Reduce Debug Time
https://ai-rng.com/ai-for-logging-improvements-that-reduce-debug-time/
Integration Tests with AI: Choosing the Right Boundaries
https://ai-rng.com/integration-tests-with-ai-choosing-the-right-boundaries/
AI Debugging Workflow for Real Bugs
https://ai-rng.com/ai-debugging-workflow-for-real-bugs/
Books by Drew Higgins
