Salesforce Sandboxes

-

Sandboxes create copies of your Salesforce org in separate environments. Use them for development, testing, and training, without compromising the data and applications in your production org. 

Sandboxes: Staging Environments for Customizing and Testing

Sandboxes are isolated from your production org, so operations that you perform in your sandboxes don’t affect your production org.

Sandbox Types

Developer Sandbox – A Developer sandbox is intended for development and testing in an isolated environment. A Developer Sandbox includes a copy of your production org’s configuration (metadata).

Developer Pro Sandbox – A Developer Pro sandbox is intended for development and testing in an isolated environment and can host larger data sets than a Developer sandbox. A Developer Pro sandbox includes a copy of your production org’s configuration (metadata). Use a Developer Pro sandbox to handle more development and quality assurance tasks and for integration testing or user training.

Partial Copy Sandbox – A Partial Copy sandbox is intended to be used as a testing environment. This environment includes a copy of your production org’s configuration (metadata) and a sample of your production org’s data as defined by a sandbox template. Use a Partial Copy sandbox for quality assurance tasks such as user acceptance testing, integration testing, and training.

Full Sandbox – A Full sandbox is intended to be used as a testing environment. Only Full sandboxes support performance testing, load testing, and staging. Full sandboxes are a replica of your production org, including all data, such as object records and attachments, and metadata. The length of the refresh interval makes it difficult to use Full sandboxes for development.

We recommend that you apply a sandbox template so that your sandbox contains only the records that you need for testing or other tasks.

When you create a Full sandbox, you also have to decide whether to include field tracking history and Chatter activity. Include it only if you require it for testing use cases.

The default is to omit field tracking.

Chatter activity data can be extensive, which can add a significant amount of time to your Full sandbox copy.

Sandbox Licenses and Storage Limits by Type


















When Salesforce record IDs are the same

During a full or partial sandbox refresh, record IDs are preserved from the production org to the sandbox org. Record IDs are also preserved when refreshing a sandbox for standard objects, or when a sandbox is cloned.

Comments

Post a Comment

Popular posts from this blog

Introduction to Salesforce Agent Script: Build Predictable AI Agents

-
Image
Please visit here for more details - https://ayaninsights.com/guestblogs/introduction-to-agent-script/ In my latest blog, I introduce Salesforce Agent Script and explain how it helps teams build AI agents that are more predictable, controlled, and aligned with business processes. Large language models are flexible, but relying only on natural-language instructions can sometimes produce inconsistent results. Agent Script addresses this challenge by combining the conversational capabilities of an LLM with structured business logic. The blog explains how developers can use conditions, variables, actions, topics, and transitions to control how an Agentforce agent behaves. Agent Script supports familiar logic such as if/else conditions, value comparisons, sequential actions, and checks for missing data. I also cover its core structure, including system settings, configuration, global variables, languages, connections, and topic blocks. The start_agent topic acts as the entry point for each...
Read more

Anti-Patterns in Salesforce Agentforce: What to Avoid When Building AI Agents

-
Image
Salesforce Agentforce is powerful because it allows teams to build AI agents that can understand user intent, retrieve grounded information, execute actions, and support business workflows across Salesforce. But like any powerful platform, the quality of the implementation matters. A good Agentforce implementation does not start with “How many things can the agent do?" It starts with “What problem should the agent solve reliably?” That is where anti-patterns come in. Anti-patterns are common implementation habits that look useful at first but eventually create confusion, poor user experience, inconsistent answers, or hard-to-maintain agents. Below are some of the most common Agentforce anti-patterns and how to avoid them. 1. Trying to Build One Agent That Does Everything One of the biggest mistakes is designing a single agent to handle every business process, every department, and every possible customer question. At first, this feels efficient. One agent, many use cases. Bu...
Read more

MCP vs. Traditional APIs: Choosing the Right Integration Approach

-
Image
Enterprise integration has traditionally been built around APIs. Applications send structured requests, services process them, and predictable responses are returned. This model has supported everything from payment processing and customer portals to mobile applications and large-scale system integrations. The rise of AI agents introduces a different integration challenge. An AI agent does not always follow a fixed sequence designed in advance. It may need to determine which system to use, discover the operations available to it, gather relevant context, and select the appropriate action based on a user’s request. This is where the Model Context Protocol, or MCP, becomes relevant. MCP and traditional APIs are related, but they solve different problems. APIs expose system capabilities. MCP provides a standardized way for AI applications to discover and use those capabilities as tools and contextual resources. Understanding this distinction is important when designing enterprise AI solut...
Read more