Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy.\u00a0Learn more<\/em><\/p>\n\n\n\n Salesforce rolled out sweeping enhancements to its AI agent platform Monday, addressing the biggest hurdles enterprises face when deploying digital workers at scale: Knowing what those agents are actually doing and ensuring they can work securely across corporate systems.<\/p>\n\n\n\n The company\u2019s Agentforce 3 release introduces a comprehensive \u201cCommand Center\u201d that gives executives real-time visibility into AI agent performance, plus native support for emerging interoperability standards that allow agents to connect with hundreds of external business tools without the need for custom coding.<\/p>\n\n\n\n The timing reflects surging enterprise demand for AI agents. According to Salesforce data, AI agent usage has jumped 233% in six months, with more than 8,000 customers signing up to deploy the technology. Early adopters are seeing measurable returns: Engine reduced customer case handling time by 15%, while 1-800Accountant achieved 70% autonomous resolution of administrative chat requests during peak tax season.<\/p>\n\n\n\n \u201cWe have hundreds of live implementations, if not thousands, and they\u2019re running at scale,\u201d Jayesh Govindarajan, EVP of Salesforce AI, said in an exclusive interview with VentureBeat. The company has moved decisively beyond experimental deployments, he noted: \u201cAI agents are no longer experimental. They have really moved deeply into the fabric of the enterprise.\u201d<\/p>\n\n\n\n Adam Evans, EVP and GM of Salesforce AI, said in a live event on Monday announcing the platform upgrade: \u201cOver the past several months we\u2019ve listened deeply to our customers and continued our rapid pace of technology innovation. The result is Agentforce 3, a major leap forward for our platform that brings greater intelligence, higher performance and more trust and accountability to every Agentforce deployment.\u201d<\/p>\n\n\n\n Among the companies embracing this technology is PepsiCo, which is deploying Agentforce as part of a broader AI-driven transformation of its global operations. In an exclusive interview with VentureBeat, Athina Kanioura, PepsiCo\u2019s chief strategy and transformation officer, described the deployment as crucial to the company\u2019s evolution in an increasingly complex marketplace.<\/p>\n\n\n\n \u201cAs a longtime partner of Salesforce, we recognized an opportunity to holistically integrate the way we utilize their platforms across our business \u2014 especially as the customer landscape evolves, trade becomes more complex and the need to better integrate our data increases,\u201d Kanioura told VentureBeat.<\/p>\n\n\n\n The food and beverage giant, whose products are consumed over a billion times daily worldwide, sees AI agents as essential for meeting customers \u201cwhere they are \u2014 and in the ways they want to engage with us,\u201d while driving backend efficiency by integrating systems and simplifying processes.<\/p>\n\n\n\n PepsiCo\u2019s seven-year relationship with Salesforce has positioned the company to move quickly on AI agents. \u201cWe were excited about how Agentforce could enhance the day-to-day experience for our field sellers \u2013 streamlining workflows and surfacing smarter insights in real time,\u201d Kanioura explained.<\/p>\n\n\n\n The Command Center represents Salesforce\u2019s response to a critical gap in the enterprise AI market. While companies have rushed to deploy AI agents for customer service, sales and operational tasks, many lack visibility into how those digital workers are performing or impacting business outcomes.<\/p>\n\n\n\n Govindarajan described the challenge facing enterprises that have moved beyond pilot programs: \u201cIt\u2019s one thing to build an AI agent demo, but when you actually build an agentic system and put it in front of your users, there\u2019s a different standard.\u201d Companies need tools to understand when AI agents are struggling and when to bring humans into the workflow, he explained.<\/p>\n\n\n\n \u201cTeams can\u2019t see what agents are doing \u2014 or evolve them fast enough,\u201d the company acknowledged in its announcement. The new observability platform provides detailed analytics on agent interactions, health monitoring with real-time alerts and AI-powered recommendations for optimization.<\/p>\n\n\n\n The system addresses what Govindarajan calls \u201cday two problems\u201d \u2013 the operational challenges that emerge after initial deployment. \u201cYou can have multiple agents for multiple personas, and you need to be able to observe how that\u2019s actually impacting the task that needs to get done at scale,\u201d he said. This includes managing the handoffs between digital agents and human workers when complex decisions or approvals are required.<\/p>\n\n\n\n The system captures all agent activity in Salesforce\u2019s Data Cloud using the OpenTelemetry standard, enabling integration with existing monitoring tools like Datadog and other enterprise systems. This addresses enterprises\u2019 need to incorporate AI agent oversight into their existing operational workflows.<\/p>\n\n\n\n Perhaps more significant is Salesforce\u2019s embrace of the Model Context Protocol (MCP), an emerging open standard for AI agent interoperability. The platform will include native MCP support, allowing Agentforce agents to connect with any MCP-compliant server without custom development work.<\/p>\n\n\n\n \u201cThere\u2019s generic interoperability, and then there\u2019s what we call enterprise-grade interoperability,\u201d Gary Lerhaupt, VP of product architecture at Salesforce, explained in an exclusive interview with VentureBeat. \u201cIf it\u2019s not enterprise grade, it\u2019s like sparkling untrusted interop.\u201d The key difference, he said, lies in governance and control mechanisms that enterprise customers require.<\/p>\n\n\n\n This capability, working alongside an expanded AgentExchange marketplace, gives enterprises access to pre-built integrations with over 30 partners including Amazon Web Services, Box, Google Cloud, IBM, PayPal and Stripe. Lerhaupt said the company is launching with \u201cnorth of 20, maybe 25 plus\u201d vetted MCP servers, with partners like PayPal offering invoicing capabilities and Box providing document access through their MCP implementations.<\/p>\n\n\n\n \u201cIn a world full of AI tools, Agentforce stood out not just for its first-of-a-kind technology but how seamlessly it fit into our technology ecosystem, the way we work and our AI strategy, standards and framework,\u201d Kanioura said.<\/p>\n\n\n\n Underlying the new features is what Salesforce calls an enhanced \u201cAtlas\u201d architecture designed for enterprise-grade performance and security. The platform now offers 50% lower latency compared to January 2025, as well as response streaming for real-time user experiences and automatic failover between AI model providers to ensure continuous operation.<\/p>\n\n\n\n For regulated industries, Salesforce\u2019s approach to hosting AI models directly within its infrastructure addresses critical security concerns. \u201cWith Anthropic, the entire stack will be running within Salesforce infrastructure,\u201d Govindarajan explained. \u201cThe calls are not going out to OpenAI, and traffic will be running within the Salesforce VPC. For regulated industries, that\u2019s what we\u2019ve been working on.\u201d<\/p>\n\n\n\n Critically for regulated industries, Salesforce now hosts Anthropic\u2019s Claude models directly within its infrastructure via Amazon Bedrock, keeping sensitive data within the Salesforce security perimeter. The company plans to add Google\u2019s Gemini models later this year, giving enterprises more options for AI model governance.<\/p>\n\n\n\n The platform also expands global availability to Canada, the UK, India, Japan and Brazil, with support for six additional languages including French, German, Spanish, Italian, Japanese and Portuguese.<\/p>\n\n\n\n Recognizing that enterprises need faster returns on AI investments, Salesforce has built more than 200 pre-configured industry actions \u2014 with more than 100 added this summer alone. These range from patient scheduling in healthcare to advertising proposal generation in media, designed to help companies deploy functional AI agents quickly rather than building from scratch.<\/p>\n\n\n\n The results demonstrate the platform\u2019s maturity. Beyond 1-800Accountant\u2019s 70% deflection rate during tax season, Govindarajan cited other production deployments: \u201cOpenTable sees 73% of all restaurant web queries handled by agents,\u201d and Grupo Falabella, a Colombian customer service operation using WhatsApp, achieved a 71% reduction in phone call traffic in just three weeks.<\/p>\n\n\n\n The company also introduced more flexible pricing, including unlimited usage licenses for employee-facing agents and per-action pricing that scales with actual AI work performed rather than simple conversation volume.<\/p>\n\n\n\n As enterprises increasingly view AI agents as digital employees rather than simple automation tools, the stakes for getting deployment right have never been higher. Companies that successfully scale AI agents stand to gain significant competitive advantages, while those that struggle with governance and oversight risk operational disruptions.<\/p>\n\n\n\n Govindarajan sees fundamental changes in how work gets organized: \u201cNew roles are emerging for people who manage a fleet of agents,\u201d he said. \u201cA CIO might ask, \u2018I have seven agents running in my enterprise, what\u2019s broadly happening?\u2019 But someone running a specific marketing agent has a different lens on the same problem.\u201d<\/p>\n\n\n\n Looking ahead, Lerhaupt positioned the current moment as transformational: \u201cYou had the personal computer, then the Internet and now it\u2019s multi-agent,\u201d he said. He described the evolution from single-agent deployments as \u201cthe multi-agent revolution and the ability to plug agents together to do exceedingly complex new types of work.\u201d<\/p>\n\n\n\n For PepsiCo, the transformation goes beyond efficiency gains. \u201cAI and technology are reshaping enterprise operations in ways that were once unimaginable,\u201d Kanioura said. \u201cThe work we\u2019re doing with Agentforce is one element of PepsiCo\u2019s broader transformation as a connected company, paving the way for a more resilient and adaptive future of work.\u201d<\/p>\n\n\n\n The competitive landscape is intensifying as major technology companies race to establish AI agent platforms. When asked about competition from Microsoft, Google and Amazon, Govindarajan emphasized Salesforce\u2019s integration advantages: \u201cWe are able to track the entire cycle of work within the enterprise ecosystem,\u201d he said. \u201cWe can define flows and interactions in the enterprise, and we\u2019ve been open and extensible in bringing in your data, your actions and orchestrating them effectively.\u201d<\/p>\n\n\n\n The Agentforce 3 platform is generally available now, with several features including hosted Anthropic models and the full Command Center rolling out through August. But perhaps the most telling sign of the technology\u2019s enterprise readiness isn\u2019t in the feature list \u2014 it\u2019s in the confidence of companies like PepsiCo to bet their digital transformation on AI agents they can finally see, measure and control.<\/p>\n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n
\n<\/div>How global food giant PepsiCo is leading the enterprise AI agent revolution<\/h2>\n\n\n\n
The missing piece: Why enterprise AI needs real-time monitoring and control<\/h2>\n\n\n\n
Open standards and secure integration: How AI agents connect across enterprise systems<\/h2>\n\n\n\n
Performance boost: Faster AI models and enhanced security for regulated industries<\/h2>\n\n\n\n
From zero to AI agent: How pre-built industry actions speed enterprise deployment<\/h2>\n\n\n\n
The new digital workforce: What enterprise AI adoption means for business operations<\/h2>\n\n\n\n
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