{"id":93508,"date":"2026-05-27T15:16:29","date_gmt":"2026-05-27T22:16:29","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=93508"},"modified":"2026-05-27T15:16:30","modified_gmt":"2026-05-27T22:16:30","slug":"how-engineering-became-agentic","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/","title":{"rendered":"Pioneering the Agentic Shift Within Salesforce Engineering"},"content":{"rendered":"\n<div class=\"wp-block-group is-style-shadow-box is-style-takeaways\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading has-black-color has-text-color has-link-color wp-elements-471ecb11ca02c2e7ea813f9dd3857484\" id=\"h-key-takeaways\">Key Takeaways<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Autonomous tools are now writing code, reviewing pull requests (\u201cPRs\u201d), and driving deployments across the software development lifecycle.\u00a0<\/li>\n\n\n\n<li>Standardizing on Claude Code and removing token limits improved output and quality simultaneously \u2014 more shipped, fewer incidents, and fewer bugs.<\/li>\n\n\n\n<li>\u00a0An agentic workflow allowed a product team to complete a 231-person-day migration in 13 days \u2014 18 times faster.<\/li>\n\n\n\n<li>We\u2019re still in the early stages of redefining how the roles across engineering, product, and design will change.\u00a0<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<p>A few months ago, I wrote about the hard work of getting thousands of engineers <a href=\"https:\/\/www.salesforce.com\/news\/stories\/getting-engineers-to-use-ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">to actually use AI<\/a> \u2014 not just adopt it in name, but embed it meaningfully into how they work. We built the governance scaffolding, the measurement infrastructure, and the workflows to make it real. We crossed 90% adoption. That felt like a milestone.<\/p>\n\n\n\n<p>It turns out, that was just the beginning.<\/p>\n\n\n\n<p>Today, <a href=\"https:\/\/engineering.salesforce.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Salesforce Engineering<\/a> is running on AI. We&#8217;ve moved from a world where AI was a helpful copilot to one where agentic tools are driving the software development lifecycle (SDLC) itself \u2014 writing code, reviewing pull requests (PRs), generating tests, updating documentation, managing deployments, and increasingly coordinating work that used to require significant human handoff. The change has been sharper and faster than anything I&#8217;ve seen in my career.<\/p>\n\n\n\n<p>Here&#8217;s what that shift actually looks like, what drove it, and what it&#8217;s teaching us.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-ramping-with-claude-code\"><strong><strong>Ramping with Claude Code<\/strong><\/strong><\/h4>\n\n\n\n<p>The biggest inflection point was a deliberate, organization-wide pivot to Claude Code as our primary <a href=\"https:\/\/www.salesforce.com\/agentforce\/ai-agents\/\">AI agent<\/a> tool. We rolled it out to all of our engineers. Then we did something that sent an even clearer signal: <strong>we removed all token limits<\/strong>. Our primary directive was to remove every last piece of friction between our engineers and the tools that make them faster and more effective.<\/p>\n\n\n\n<p>The results are showing up in the data. In April 2026, work items completed per developer are up 50.8% compared with April 2025. PRs merged per developer are up 79%. And most importantly, when we measure the true value of code delivered \u2014 not just the volume \u2014 using a machine learning-based <strong>Effective Output score<\/strong>, we&#8217;re seeing that output has grown 151.3% year over year.&nbsp;<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-what-agentic-transformation-actually-looks-like\"><strong>What Agentic Transformation actually looks like<\/strong><\/h4>\n\n\n\n<p>Numbers tell part of the story. But one example from our product teams tells it better.<\/p>\n\n\n\n<p>The team faced a migration of 33 API endpoints to a new cloud-native architecture \u2014 the kind of task that, done the traditional way, would drain roughly 231 person-days, or seven per API. Manual schema mapping, manual testing, and manual documentation create massive friction, stalling momentum and trapping entire engineering teams in months of low-leverage toil.<\/p>\n\n\n\n<p>They did it in 13 days. <strong><em>Eighteen times<\/em><\/strong><strong> faster<\/strong>.<\/p>\n\n\n\n<p>Here&#8217;s how: The team built a rule-based framework using Claude \u2014 markdown files combined with reference implementations \u2014 to standardize the AI-automated migration. Every round of PR feedback got incorporated back into the rule set, so accuracy improved continuously and outputs arrived near production-ready. They let autonomous large language model loops run (build, fix, validate) without manual intervention and parallelized migrations across isolated environments to generate multiple PRs simultaneously. Thirty-three endpoints. Five PRs. The largest single PR delivered 21 endpoints with 100% test coverage.<\/p>\n\n\n\n<p>That&#8217;s a different way of building software.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-more-output-better-quality-at-the-same-time\"><strong>More output, better quality \u2014 at the same time<\/strong><\/h4>\n\n\n\n<p>The skeptic&#8217;s question when you push AI this hard is, what breaks?<\/p>\n\n\n\n<p><a href=\"https:\/\/engineering.salesforce.com\/engineering-360-dashboard-transforming-complex-data-into-powerful-engineering-insights\/\" target=\"_blank\" rel=\"noreferrer noopener\">Engineering 360<\/a>, a platform that centralizes engineering data from hundreds of systems to track security, availability, quality, and developer productivity, has a clear answer: Quality went up: Even with the increase in PRs, total incidents dropped by 5%.<\/p>\n\n\n\n<p>This matters because productivity and quality are often framed as a tradeoff. We&#8217;re not seeing that tradeoff. Trust is our #1 value, and our engineers are investing their AI superpowers to meet our highest quality standards and nonfunctional requirements. For example, we have embedded security guardrails and quality standards structurally into the agentic workflow. When agentic tools get applied properly, quality doesn&#8217;t suffer from speed. It benefits from it.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-rethinking-the-sdlc\"><strong>Rethinking the SDLC<\/strong><\/h4>\n\n\n\n<p>Four months ago, we learned that AI had to fit into existing workflows for engineers to adopt it. Now that they&#8217;ve adopted it, they&#8217;re using AI to completely tear down and rebuild those same workflows.<br><br>Our engineers are fundamentally rethinking how they practice the SDLC. What processes can be removed entirely? What handoffs are unnecessary? Where are humans still doing work that an agent can own? Those are the questions that unlock real productivity \u2014 not marginal improvement. And it definitely doesn&#8217;t look like what we had before, with AI bolted on.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-skills-subagents-and-the-new-engineering-craft\"><strong>Skills, subagents, and the new engineering craft<\/strong><\/h4>\n\n\n\n<p>One of the most interesting developments has been watching engineers become builders of their own agentic workflows, not just users of tools. Claude Code skills \u2014 packaged, reusable capabilities that encode team context, naming conventions, and workflow patterns \u2014 have become a new form of engineering artifact. Teams are building them, sharing them, and compounding on each other&#8217;s work. <\/p>\n\n\n\n<p>We have also built an AI Expert Suite and the Salesforce Foundation Plugins, a curated, institutionalized library of AI skills built specifically for Salesforce engineering workflows, giving every developer a shared foundation of proven capabilities to build from rather than starting from scratch. Our internal benchmark shows clear evidence that the curated skills improve accuracy and reliability on Salesforce-specific coding tasks while reducing unnecessary cost.<\/p>\n\n\n\n<p>Subagents and agent teams \u2014 scoped AI agents that handle parallel workstreams within a larger task, often as a team \u2014 are changing how complex work gets decomposed. An engineer no longer has to context-switch across five systems to move a single task forward. They describe the outcome, and a set of coordinated agents figures out the steps.<\/p>\n\n\n\n<p>This is a different kind of engineering craft. The most important skill today is knowing how to structure problems for an agentic system, when to delegate versus stay in the loop, and how to build reusable patterns your team can compound on.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-what-we-re-still-figuring-out\"><strong>What we&#8217;re still figuring out<\/strong><\/h4>\n\n\n\n<p>A few things are still genuinely hard.<\/p>\n\n\n\n<p>Context management in long, complex agentic sessions remains a craft that engineers are actively learning. The quality of CLAUDE.md files \u2014 the persistent context configurations that orient Claude to your codebase, conventions, and constraints \u2014 varies widely across teams, and that variance matters a lot for output quality.<\/p>\n\n\n\n<p>And security in an agentic world requires a fundamentally different model. When agents can act on your systems \u2014 not just suggest \u2014 the blast radius of a misconfigured tool is bigger. We&#8217;re investing hard in securing the agentic SDLC end to end.<\/p>\n\n\n\n<p>Role evolution is real and worth taking seriously. When agents handle more of the execution layer, how do junior engineers grow into senior engineers if AI is absorbing much of the entry-level work? What is the role of a designer or product manager in this new world?&nbsp; Our unit of execution used to be a scrum team. How will this change? Is it the same for infrastructure layers like our security layer versus our products? We\u2019re experimenting with one-person units or three-person units, but these are still early experiments. We don&#8217;t have clear answers yet, but the teams thinking about these issues proactively will come out ahead.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-the-direction-is-clear\"><strong>The direction is clear<\/strong><\/h4>\n\n\n\n<p>When I wrote my original piece, I said the goal was to build a foundation. We built it. Now we&#8217;re building on it \u2014 fast.<\/p>\n\n\n\n<p>The engineering organization of the future doesn&#8217;t look like the organization of today with AI bolted on. It looks fundamentally different. The product team mentioned earlier didn&#8217;t just go faster \u2014 it changed what was economically possible. That&#8217;s what we&#8217;re scaling across our entire engineering organization.<\/p>\n\n\n\n<p>Our aim is to build the most automated, agentic SDLC in the industry, and these posts are a progress report on our way. Please feel free to share your journey as practitioners and operators.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"h-dive-deeper\"><strong>Dive deeper<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>How we got our <a href=\"https:\/\/www.salesforce.com\/news\/stories\/getting-engineers-to-use-ai\/\" target=\"_blank\" rel=\"noreferrer noopener\">engineers to use AI <\/a>\u2014 without breaking everything<\/li>\n\n\n\n<li>Eight design principles for the <a href=\"https:\/\/www.salesforce.com\/news\/stories\/agentic-enterprise-design-principles\/\" target=\"_blank\" rel=\"noreferrer noopener\">Agentic Enterprise<\/a><\/li>\n\n\n\n<li>If you want to work on an AI-native engineering team, <a href=\"https:\/\/www.salesforce.com\/company\/careers\/\" target=\"_blank\" rel=\"noreferrer noopener\">connect with us<\/a>.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key Takeaways A few months ago, I wrote about the hard work of getting thousands of engineers to actually use AI \u2014 not just adopt it in name, but embed it meaningfully into how they work. We built the governance scaffolding, the measurement infrastructure, and the workflows to make it real. We crossed 90% adoption. [&hellip;]<\/p>\n","protected":false},"author":763,"featured_media":93511,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"sf_subhead":"","sf_i18n_disclaimer":false,"_jetpack_memberships_contains_paid_content":false,"alternateThumbnailId":0,"sf_product_cta_id":0,"footnotes":""},"categories":[1],"tags":[],"sf_content_type":[21243],"sf_theme":[21184],"sf_topic":[21212,20528,21187],"sf_product":[],"sf_industry":[],"sf_role":[],"sf_multimedia_asset":[],"sf_location":[1798],"sf_collection":[],"sf_visibility":[],"coauthors":[21154],"class_list":["post-93508","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","sf_content_type-snapshot-opinion","sf_theme-agentic-ai","sf_topic-agentic-enterprise","sf_topic-agents","sf_topic-ai","sf_location-global"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How Salesforce Engineering Became Truly Agentic - Salesforce<\/title>\n<meta name=\"description\" content=\"Key Takeaways Autonomous tools are now writing code, reviewing pull requests (\u201cPRs\u201d), and driving deployments across the software development lifecycle.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pioneering the Agentic Shift Within Salesforce Engineering\" \/>\n<meta property=\"og:description\" content=\"Key Takeaways Autonomous tools are now writing code, reviewing pull requests (\u201cPRs\u201d), and driving deployments across the software development lifecycle.\u00a0\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/\" \/>\n<meta property=\"og:site_name\" content=\"Salesforce\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/salesforce\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-05-27T22:16:29+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-27T22:16:30+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.salesforce.com\/news\/wp-content\/uploads\/sites\/3\/2026\/05\/From-AI-Assisted-to-AI-Native_-How-Salesforce-Engineering-Became-an-Agentic-Org.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"675\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Srinivas Tallapragada\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@salesforcenews\" \/>\n<meta name=\"twitter:site\" content=\"@salesforcenews\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Srinivas Tallapragada\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/\"},\"author\":[{\"@id\":\"https:\/\/www.salesforce.com\/news\/#\/schema\/person\/image\/0a0513431aead76f2e384d08d44f4e60\"}],\"headline\":\"Pioneering the Agentic Shift Within Salesforce Engineering\",\"datePublished\":\"2026-05-27T22:16:29+00:00\",\"dateModified\":\"2026-05-27T22:16:30+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/\"},\"wordCount\":1344,\"publisher\":{\"@id\":\"https:\/\/www.salesforce.com\/news\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.salesforce.com\/news\/wp-content\/uploads\/sites\/3\/2026\/05\/From-AI-Assisted-to-AI-Native_-How-Salesforce-Engineering-Became-an-Agentic-Org.png\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/\",\"url\":\"https:\/\/www.salesforce.com\/news\/stories\/how-engineering-became-agentic\/\",\"name\":\"How Salesforce Engineering Became Truly Agentic - 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