Stories in Apex and platform

The Winter Twenty Seven release increases the synchronous Apex heap limit from six megabytes to ten megabytes and raises the asynchronous limit from twelve megabytes to twenty-five megabytes. To manage the transition safely, administrators can use Apex Settings to force non-production orgs to retain the previous Summer Twenty Six limits until code is verified against the new thresholds.

Testing with large compressed files shows that the additional memory allows significantly larger payloads to be processed in a single transaction, though compression algorithms and file structure still dictate actual heap consumption. The author notes that higher memory availability does not automatically improve throughput, as CPU limits and governor constraints remain unchanged.

Larger heaps introduce risks such as overwhelming end users with unfiltered data tables, overloading downstream APIs, or masking existing logic flaws that previously failed at smaller record counts. The article concludes that developers should continue writing code that conserves resources and batches workloads, treating the increased limits as capacity for legitimate growth rather than a target to hit routinely.

The author proposes Framework-Driven Development as a response to the risks of AI-assisted coding on the Salesforce platform. Because large language models generate probabilistic output that often ignores existing architecture, developers risk introducing inefficient code that consumes shared governor limits. The approach shifts responsibility for code quality and performance from individual engineers to the underlying framework itself.

Framework-Driven Development relies on three main assumptions. Interfaces must be deliberately restricted so that neither developers nor AI agents can bypass established rules. Internal logic should automatically optimize queries and data operations to respect platform boundaries. Finally, quality must be baked into the design through compile-time contracts and runtime validation that force explicit overrides when defaults are intentionally ignored.

Real-world implementations demonstrate these principles through specialized libraries. A trigger handler restricts available methods based on execution context, preventing invalid database operations before compilation. A query builder enforces caching rules and blocks complex nested conditions that could return misleading results. A data manipulation layer chains operations safely while managing sharing modes and commit strategies. Each component throws descriptive exceptions when misused, requiring deliberate action to bypass safeguards.

The methodology also emphasizes comprehensive documentation and structured prompts to guide artificial intelligence tools. By exposing only safe public methods and providing clear usage examples, teams can maintain consistent standards across multiple projects. This reduces technical debt and ensures that automated code generation aligns with enterprise architecture requirements.

Thresholds, defaults and feature switches tend to get typed straight into whichever formula, Flow or Apex class needs them. The cost shows up later, when the business changes a number and someone has to find every place it was used. Andrew Fawcett argues that Custom Metadata Types are the platform’s answer: a single, deployable home for those values that travels through source control with the rest of the application, unlike records in a Custom Object or Custom Settings.

The post works through a deal-policy example. A Deal_Policy__mdt type holds a large-deal amount, a maximum discount, a default discount and an approval threshold, with one record per region and a Default record for org-wide values. A Formula Field reads the Default record directly using the $CustomMetadata reference, which names the type, the record and the field. The same shape works in Validation Rules and field defaults, though long text area fields cannot be referenced this way.

From there it covers each consumer in turn: formulas, Validation Rules, Flow, Apex, Lightning Record Pages and Agentforce, noting which support the reference natively and which need a workaround. A sample project deploys to a Scratch Org so readers can follow the same Setup paths. The harder question the post keeps returning to is impact: once the value lives in one place, how do you find out what reads it and what changes when you edit it.