Overview
Agentforce for Developers is an AI-powered developer tool that's available as an easy-to-install Visual Studio Code extension built using CodeGen and xGen-Code, secure, custom AI models from Salesforce. The extension is available in the VS Code and Open VSX marketplaces as a part of the Salesforce Expanded Pack, in the VS Code desktop application, and in Code Builder.
It is important to note that Salesforce does not use customer data to train our LLMs.
Agentforce for Developers assists you throughout the Salesforce development process with expertise learned from anonymized code patterns. Our suite of AI-powered developer tools increases productivity and provides helpful assistance for complex coding tasks. We enforce development best practices with code generation and our suite of recommended static analysis and security scanning tools. With boilerplate code generation as its foundation, AI-assisted tooling also makes it easier for new developers to onboard to the Salesforce Platform.
Agentforce for Developers generates Apex code from natural language prompts and automatically suggests code completions for you as you type. When enabled along with IntelliSense, this feature makes Salesforce development in Visual Studio Code even richer. Familiarity with Visual Studio Code is assumed.
-
Dev Assistant: Code with ease with the help of your Dev Assistant right beside you. Get started with code generation and Salesforce development by asking for assistance. With the help of convenient slash commands, you can also target specific tasks like understanding unfamiliar code, and improving your code documentation.
-
Command Palette: Use the Agentforce: Generate Code command in the VS Code Command Palette to enter a question about what you want to build. Agentforce will give you Apex code suggestions right in your editor.
-
Inline Code Completions: As you type, Agentforce for Developers can suggest code completions, without any interruptions to your workflow. Easily pick the suggestion that works for you. Use this feature in Apex and LWC (JavaScript, CSS, and HTML) files.
-
Test Case Generation for Apex: Use Agentforce for Developers to easily create unit test cases for your Apex methods. Quickly get to required Apex test coverage and get your code ready for deployment.
Agentforce for Developers uses generative AI, which can produce inaccurate or harmful responses. The output generated by AI is often nondeterministic. Before using the generated output, review it for accuracy and safety. You assume responsibility for how the outcomes are applied to your organization.
Agentforce solutions are designed, developed, and delivered to be compliant with our five principles for trusted generative AI.
Accuracy: We prioritize accuracy, precision, and recall in our models, and we back our model outputs up with explanations and sources whenever possible. We recommend that a human check model output before sharing with end users.
Safety: We work to mitigate bias, toxicity, and harmful outputs in our models using industry-leading techniques. We protect the privacy of personally identifiable information (PII) in our data by adding guardrails around this data.
Honesty: We ensure that the data we use in our models respects data provenance and that we have consent to use the data.
Empowerment: Whenever possible, we design models to include human involvement as part of the workflow.
Sustainability: We strive to build right-sized models that prioritize accuracy and to reduce our carbon footprint.
Learn more at Salesforce AI Research: Trusted AI.
Agentforce for Developers is powered by customized Large Language Models (LLMs) developed by Salesforce. These models are CodeGen2.5 and xGen-Code.
A member of the growing family of Salesforce CodeGen models, CodeGen2.5 shows that a small model, if trained well, can achieve surprisingly good performance. This model powers our Inline Autocomplete feature.
Dev Assistant leverages our newest LLM known as xGen-Code. It has been fine-tuned to specifically handle code-related tasks and support interactive features such as chat. The xGen-Code model is well-equipped to handle tasks that require deeper code understanding and more complex reasoning.