What should I do first when SOQL errors, heap-size hits, or callout limits crash my org?
Treat the incident as a triage + forensic task: reproduce the failure in a safe scope, capture debug logs (with appropriate trace flags), identify the failing transaction(s), and put quick mitigations in place (disable problematic automation, throttle integrations). Then run targeted root-cause analysis rather than broad rollbacks so you can restore service quickly and collect the data needed for a durable fix. For teams managing complex debugging scenarios, comprehensive license optimization strategies can help ensure your debugging efforts align with cost-effective resource allocation.
How do I capture useful debug logs so investigations aren't guesswork?
Use trace flags and increased log levels for the user/integration running the failing transaction, stream logs into VS Code or your SIEM for live analysis, and reproduce with representative data and concurrency. Persist logs (Event Monitoring or log storage) so you can correlate errors, timings, and order-of-execution details across retries and integrations.
Which tools speed up Apex debugging and pattern detection?
Use modern analyzers like Apex Log Analyzer or Salesforce Code Analyzer for pattern detection, Developer Console for quick SOQL probes, and log streaming into VS Code. In 2026, AI-enabled tools (Agentforce for Developers, Salesforce Code Builder AI) can auto-explain errors, suggest fixes, and generate unit tests from real log patterns. Organizations looking to automate complex debugging workflows can leverage Make.com's visual automation platform to streamline error detection and response processes.
How can AI reduce investigation and remediation time?
AI agents can summarize log funnels, surface root causes, propose exception-handling changes, and generate targeted Apex tests from live data. They help prioritize fixes (e.g., bulkification vs. async conversion) and can automate repetitive refactors so engineers focus on design-level improvements.
What are practical ways to avoid heap size limit errors?
Process records in batches (Batchable/Queueable), avoid accumulating large in-memory collections, use SOQL for loops, mark transient variables where appropriate, and add runtime checks with Limits.getHeapSize() to short‑circuit or escalate to async processing before hitting hard limits.
How do I work around callout and transaction limits for large-file or high-volume integrations?
Offload large binaries using ContentDocument/ContentVersion pointers or external object stores, use chunking and resumable uploads, rely on async integration patterns (Platform Events, queueable/continuation), and employ External Services or middleware to batch/aggregate calls so Salesforce avoids per-transaction API or heap pressure.
How do I prevent SOQL 101/201 errors and inefficient queries?
Bulkify code (move queries outside loops), select only required fields, use indexed filters, prefer selective WHERE clauses, leverage QueryLocator for large result sets, and use static analysis tools to detect unbounded queries. Regularly review query plans and add selective indexes where needed.
What long-term practices turn recurring outages into resilient systems?
Adopt CI/CD and DevOps (Salesforce CLI, Copado), enforce automated testing (Apex + Flows), run static analysis and pre-deploy gates, refactor to modular architectures, maintain an automation inventory, and embed monitoring/alerts so issues are caught before customer impact. Combine process with tooling to retire technical debt continuously. For teams managing multiple business applications, comprehensive CRM implementation strategies can provide valuable insights into cross-platform optimization approaches.
How should I monitor and alert on performance and governor-limit risk?
Use Event Monitoring and Log Inspector workflows to collect execution telemetry, stream logs for real-time anomaly detection, set alert thresholds for SOQL/CPU/heap/callout patterns, and create dashboards that show trends so you can remediate before limits cause failures.
How can automated testing reduce regression risk when fixing these issues?
Build targeted unit and integration tests (including AI-generated tests), run them in CI pipelines, include flow and automation coverage, and use tools like Provar to validate end-to-end behavior. Preventing regressions through automated gates reduces emergency hotfixes that add more technical debt.
What organizational changes help keep technical debt from recurring?
Create a governance board for automation, enforce coding and naming standards, maintain an automation/catalog inventory, run regular debt sprints to refactor or retire outdated logic, and cultivate knowledge sharing (Trailhead Log Inspector patterns, internal playbooks) so learnings scale across teams.
When should I involve middleware or external platforms (Make.com, dedicated API layer)?
If integrations require heavy transformation, large-file handling, orchestration across many APIs, or rate-shaping to protect Salesforce, move those responsibilities to middleware. Visual automation platforms (Make.com) or an API layer can reduce callout volume, centralize retries, and keep Salesforce within governor limits.
How can I use runtime checks like Limits.getHeapSize() without cluttering code?
Encapsulate runtime checks in utility classes that standardize pre-flight validations (heap, CPU, query count). Use those utilities where large collections or heavy processing happen and route to async handlers when thresholds approach. This centralizes logic and keeps business code readable.
