AI Revolutionizes Software Development: Salesforce's Agentforce Leads the Way

This AI already writes 20% of Salesforce's code. Here's why developers aren't worried

"About 20% of all APEX code written in the last 30 days came from Agentforce," Jayesh Govindarajan, Senior Vice President of Salesforce AI, told me during a recent interview. His team tracks not just code generated, but code actually deployed into production. The numbers reveal an acceleration that's impossible to ignore: 35,000 active monthly users, 10 million lines of accepted code, and internal tools saving 30,000 developer hours every month. Yet Salesforce's developers aren't disappearing. They're evolving. "The vast majority of development -- at least what I call the first draft of code -- will be written by AI," Govindarajan acknowledged. "But what developers do with that first draft has fundamentally changed." From lines of code to strategic control: How developers are becoming technology pilots Software engineering has always blended creativity with tedium. Now AI handles the latter, pushing developers toward the former. "You move from a purely technical role to a more strategic one," Govindarajan explained. "Not just 'I have something to build, so I'll build it,' but 'What should we build? What does the customer actually want?'" This shift mirrors other technological disruptions. When calculators replaced manual computation, mathematicians didn't vanish -- they tackled more complex problems. When digital cameras killed darkrooms, photography expanded rather than contracted. Salesforce believes code works the same way. As AI slashes the cost of software creation, developers gain what they've always lacked: time. "If creating a working prototype once took weeks, now it takes hours," Govindarajan said. "Instead of showing customers a document describing what you might build, you simply hand them working software. Then you iterate based on their reaction." 'Vibe coding' is here: Why software engineers are now orchestrating AI rather than typing every command Coders have begun adopting what's called "vibe coding" -- a term coined by OpenAI co-founder Andrej Karpathy. The practice involves giving AI high-level directions rather than precise instructions, then refining what it produces. "You just give it a sort of high-level direction and let the AI use its creativity to generate a first draft," Govindarajan said. "It won't work exactly as you want, but it gives you something to play with. You refine parts of it by saying, 'This looks good, do more of this,' or 'Those buttons are janky, I don't need them.'" He compares the process to musical collaboration: "The AI sets the rhythm while the developer fine-tunes the melody." While AI excels at generating straightforward business applications, Govindarajan admits it has limits. "Are you going to build the next-generation database with vibe coding? Unlikely. But could you build a really cool UI that makes database calls and creates a fantastic business application? Absolutely." The new quality imperative: Why testing strategies must evolve as AI generates more production code AI doesn't just write code differently -- it requires different quality control. Salesforce developed its Agentforce Testing Center after discovering that machine-generated code demanded new verification approaches. "These are stochastic systems," Govindarajan explained. "Even with very high accuracy, scenarios exist where they might fail. Maybe it fails at step 3, or step 4, or step 17 out of 17 steps it's performing. Without proper testing tools, you won't know." The non-deterministic nature of AI outputs means developers must become experts at boundary testing and guardrail setting. They need to know not just how to write code, but how to evaluate it. Beyond code generation: How AI is compressing the entire software development lifecycle The transformation extends beyond initial coding to encompass the full software lifecycle. "In the build phase, tools understand existing code and extend it intelligently, which accelerates everything," Govindarajan said. "Then comes testing -- generating regression tests, creating test cases for new code -- all of which AI can handle." This comprehensive automation creates what Govindarajan calls "a significantly tighter loop" between idea and implementation. The faster developers can test and refine, the more ambitious they can become. Algorithmic thinking still matters: Why computer science fundamentals remain essential in the AI era Govindarajan frequently fields anxious questions about software engineering's future. "I get asked constantly whether people should still study computer science," he said. "The answer is absolutely yes, because algorithmic thinking remains essential. Breaking down big problems into manageable pieces, understanding what software can solve which problems, modeling user needs -- these skills become more valuable, not less." What changes is how these skills manifest. Instead of typing out each solution character by character, developers guide AI tools toward optimal outcomes. The human provides judgment; the machine provides speed. "You still need good intuition to give the right instructions and evaluate the output," Govindarajan emphasized. "It takes genuine taste to look at what AI produces and recognize what works and what doesn't." Strategic elevation: How developers are becoming business partners rather than technical implementers As coding itself becomes commoditized, developer roles connect more directly to business strategy. "Developers are taking supervisory roles, guiding agents doing work on their behalf," Govindarajan explained. "But they remain responsible for what gets deployed. The buck still stops with them." This elevation places developers closer to decision-makers and further from implementation details -- a promotion rather than an elimination. Salesforce supports this transition with tools designed for each stage: Agentforce for Developers handles code generation, Agent Builder enables customization, and Agentforce Testing Center ensures reliability. Together, they form a platform for developers to grow into these expanded roles. The company's vision presents a stark contrast to the "developers are doomed" narrative. Rather than coding themselves into obsolescence, software engineers who adapt may find themselves more essential than ever. In a field where reinvention is routine, AI represents the most powerful compiler yet -- transforming not just how code is written, but who writes it and why. For developers willing to upgrade their own mental models, the future looks less like termination and more like transcendence.

How AI Is Changing the Way Developers Write Code

Remember when GitHub's CoPilot first made waves by automatically generating source code in 2021? It all seemed like a great way to streamline the process until CCS researchers found that 40% of the code generated was highly vulnerable as it included design flaws and bugs. But GitHub quickly learned from their mistakes -- and so did numerous other AI coding models. Now, artificial intelligence (AI) plays an increasingly large role in software development. Developers utilize AI throughout every coding cycle phase to streamline the process, improve software functionality, and create more complex and multifaceted programs. Here's how this technology has become a game changer that actually does the job. With generative AI's rapid advancements and increasing reliability, many developers use platforms like Amazon CodeWhisperer, Tabnine, and the updated version of Copilot to automate their coding workflows. Tools such as machine-learning-based security checks are also being used to assess CI/CD pipelines. Overall, GenAI has some unique capabilities for writing code. Its automation abilities can simplify development tasks such as formatting new code, validating code, and fulfilling commit and pull requests. Its rule-based structure can streamline tasks like manually scanning SQL injections and automatically scanning through code and identifying patterns within minutes. GenAI's machine learning algorithms can also assist with tasks like tracking security vulnerabilities. ML models can be trained to identify known SQL injections and spot patterns in unfamiliar injections. This capability assists developers in recognizing existing vulnerabilities and enabling them to predict and mitigate new vulnerabilities. Additionally, machine learning models can predict and generate everything from coding sequences to novel solutions and answers. Overall, these tools reduce the frequency with which developers need to search for boilerplate code and write new code, effectively transforming their roles in the coding process. Instead of spending the majority of their time writing code, they can spend more time making strategic decisions, optimizing existing code, and developing creative software solutions. LMs such as OpenAI's GPT-4 form the foundations for AI coding assistants by powering the underlying technology of tools designed to produce functional computer code. These models have been trained on vast amounts of programming languages, documentation, and real-world code examples, which allows them to understand the intent behind a developer's prompt and the structure of well-formed code. Tools like GitHub Copilot build on this foundation by actively assisting developers in real time. As developers type, Copilot monitors their keystrokes and context within the file. It then uses GPT-4's predictive capabilities to infer what the developer is trying to achieve, whether writing a function, debugging, or completing a class. Based on that understanding, it generates relevant and usable code snippets on the fly. Other AI assistants, like Meta's Code Llama and Stability AI, offer a range of capabilities engineered for developers. Code Llama provides a free code-generating model for developers of all skill levels, Stability AI's StableCode assists developers in automating the coding process, and models like ChatGPT can help write, optimize, and debug code. These code assistant programs give programmers the information they need to code effectively and address common issues. This data appears in real time alongside the code they're writing, saving them from having to scan forums and websites for solutions. AI assistants track developers' code and notes penned in natural language across all of the files linked to a project, ensuring all AI code produced is aligned with the nature of the task. As a field of machine learning, deep learning has become invaluable for developers. It trains artificial neural networks with intricate layers of data, enabling AI tools to extract comprehensive representations from vast, raw datasets. Large language models (LLMs) have also prompted notable advancements in AI, natural language processing (NLP), and coding. These models can understand natural language inputs and generate human-like text and code. Their outputs are based on the massive volumes of data they have been trained on. Both these tools are being used to assist in code completion. AI code generation platforms use deep learning and LLM models to offer developers code suggestions and completions in real time. Many tools, including Copilot, can be integrated with widely used code editors. Once integrated, they assist programmers by suggesting code snippets, completions, and entire functions based on each project's intended functionality and context. These platforms use data from private and public code repositories to enhance their suggestions and capabilities over time. As they learn, they generate increasingly more contextually accurate code for developers. Most tools support an extensive range of programming languages, ranging from Python, Java, JavaScript, and C++ to C#, TypeScript, Rust, Kotlin, Perl, R, and others. Generative AI platforms, AI assistants, and deep learning models all offer key benefits for coding workflows. The primary benefit of these tools is their ability to enhance developers' productivity considerably. Each of these tools can work together to accelerate software development. They reduce the new code that developers must write from scratch by suggesting accurate and relevant snippets and code completions as they work. These tools can reduce the burden of time-consuming, repetitive tasks through automation and decrease deployment times concurrently. Intelligent assistants can help coordinate tasks between developers, generate build scripts, and automate testing and deployment pipelines. They also help with debugging, translating code from one programming language to another, and refactoring old code into a newer optimized version. This speeds up release cycles and reduces human error while freeing up developers to focus on innovation, problem-solving, and functionality instead. With 28% of the global workforce working remotely, many development teams operate across different time zones. In this case, AI coding assistants enhance global collaboration, ensuring consistent workflows, shared coding standards, and seamless task management, regardless of physical location. Developers can use these AI functions to learn more about coding and enhance their knowledge and skills. AI platforms provide a wealth of educational resources for beginners and seasoned programmers by granting access to proven coding patterns and practices and simplifying industry terminology. Even skilled professionals can use AI to bridge gaps in their coding knowledge and effectively use new programming languages. As the functionality of AI web dev tools advances, AI is expected to play an even greater role in creating user-friendly, versatile software. In a few short years, we've gone from buggy, unsafe, and untrustworthy code to code that ticks all the right boxes at the click of a button. However, this low-code approach doesn't take developers' work away from them. It's giving them the opportunity to streamline workflows and wrap up projects faster and more efficiently.