Salesforce Elastic Limit for Async Apex

 Salesforce introduced Elastic Limits for Async Apex (Beta).

Here’s what it does:

→ Your org can now handle up to 500,000 async jobs per day
→ Jobs after 250k will still run, but at a slower speed
→ No sudden failures when the limit is crossed
→ You can now see live usage directly on the Apex Jobs page
→ Developers can also monitor limits using System.OrgLimits.getMap()
→ Available for Enterprise, Performance, Unlimited, and Developer editions

How to enable it?

→ Go to Setup​
→ Search for Apex Settings​
→ Enable “Use elastic limits for asynchronous Apex jobs (Beta)”

That’s it.

Once enabled, the Apex Jobs page will show:

→ Current 24-hour usage
→ Daily limit
→ Elastic limit
→ Whether throttling is active

No more guessing.
No more surprise failures in the middle of the night.

This is a really useful update for orgs handling large-scale automation and integrations.

Cleaner Apex Code with Multiline Strings and Using String.template() -

 The new Multiline Strings and String.template() functionality in Salesforce Summer ’26 is a major quality-of-life improvement for Apex developers.

• Create multiline strings using triple single quotes (''')
• Perform named variable interpolation using String.template()
• Avoid repetitive string concatenation
• Replace index-based placeholders from String.format()

Ex :

String formatted = '''

{

    "Account": "${accountName}",

    "Last Updated": "${date}"

}

'''.template(new Map<String, Object>{

    'accountName' => 'My Account',

    'date' => DateTime.now()

});

System.debug(formatted);

Agentforce reasoning engine

 Most Salesforce developers don't know that Agentforce has a sophisticated reasoning engine that decides which action to call, and understanding how it works can greatly improve your automation workflows.

The reasoning engine consists of three main components: the planner, the action selector, and the response synthesizer. The planner determines the overall strategy for the conversation, the action selector chooses the specific action to take, and the response synthesizer generates the response to the user. 

In a real-world scenario, this can be useful for automating customer support workflows, where the Agentforce reasoning engine can determine the best course of action based on the customer's input. For example, you can configure the action selector to call a specific Apex method, such as "handleCustomerComplaint", by setting the "actionName" parameter to "handleCustomerComplaint".