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Why do Salesforce integrations work initially but become unstable over time?
Most integrations are built and tested with small volumes and ideal conditions. As real usage grows, API limits, retry storms, data quality issues, and unhandled edge cases start surfacing. Salesforce is especially sensitive to inefficient request patterns and excessive synchronous processing. StablRead more
Most integrations are built and tested with small volumes and ideal conditions. As real usage grows, API limits, retry storms, data quality issues, and unhandled edge cases start surfacing. Salesforce is especially sensitive to inefficient request patterns and excessive synchronous processing.
See lessStable integrations usually rely on batching, idempotent design, proper error handling, and asynchronous processing. Monitoring and backoff strategies are just as important as the initial implementation.
Takeaway: Integration stability depends more on architecture than on initial correctness.
Why do sharing rules become harder to reason about over time?
Sharing rules accumulate silently. Each exception adds another layer, and Salesforce evaluates them together at runtime. Manual shares, implicit sharing, and role hierarchy effects make outcomes non-obvious. Mature orgs periodically audit and simplify sharing models instead of layering fixes indefinRead more
Sharing rules accumulate silently. Each exception adds another layer, and Salesforce evaluates them together at runtime. Manual shares, implicit sharing, and role hierarchy effects make outcomes non-obvious.
See lessMature orgs periodically audit and simplify sharing models instead of layering fixes indefinitely.
Takeaway: Sharing models need refactoring just like code.
Why do Lightning Web Components fail silently in production but not sandbox?
Production environments usually have stricter security settings, larger datasets, and more complex sharing rules. LWCs run entirely in user context, so differences in field-level security or record access can cause data retrieval to fail silently if error handling isn’t implemented correctly. AnotheRead more
Production environments usually have stricter security settings, larger datasets, and more complex sharing rules. LWCs run entirely in user context, so differences in field-level security or record access can cause data retrieval to fail silently if error handling isn’t implemented correctly.
See lessAnother common cause is unhandled promise rejections in JavaScript. In sandbox, test users often have broad permissions, masking issues that only appear when real users with limited access load the component.
The most reliable fix is adding robust error handling in both Apex and JavaScript, logging meaningful errors, and testing LWCs using realistic user profiles.
Takeaway: LWCs rarely “break randomly”—they expose hidden permission and error-handling gaps.
Why does Apex logic behave unpredictably when multiple triggers exist?
Salesforce does not guarantee execution order between multiple triggers on different objects. When one trigger updates another object, it can cause that object’s triggers and automation to fire, sometimes recursively. This creates execution paths that are difficult to reason about just by reading coRead more
Salesforce does not guarantee execution order between multiple triggers on different objects. When one trigger updates another object, it can cause that object’s triggers and automation to fire, sometimes recursively. This creates execution paths that are difficult to reason about just by reading code.
See lessThe unpredictability increases when triggers perform updates without guarding against recursion or checking whether changes are actually required.
Most mature orgs solve this by using trigger handler frameworks, enforcing single-trigger-per-object patterns, and minimizing cross-object updates in synchronous transactions.
Takeaway: Trigger behavior becomes unstable when execution order is assumed rather than controlled.
Why do Salesforce Flows become hard to maintain as automation grows?
Flows become hard to maintain because they scale visually, not structurally. Each new requirement adds branches, decisions, and record updates, but there’s no strong modularity like you’d have in Apex. Over time, logic that should be reusable or isolated ends up duplicated across paths, making changRead more
Flows become hard to maintain because they scale visually, not structurally. Each new requirement adds branches, decisions, and record updates, but there’s no strong modularity like you’d have in Apex. Over time, logic that should be reusable or isolated ends up duplicated across paths, making changes risky.
Teams usually handle this by splitting responsibilities: keeping Flows focused on orchestration and moving complex logic into Apex, subflows, or reusable components. Clear naming, documentation, and strict ownership rules also help slow down entropy.
Takeaway: Flows work best when they stay simple and delegate complexity elsewhere.
See less