Agentforce Technology Trends and Innovations

Salesforce is all-in on AI Agents with Agentforce. Today at Dreamforce, Salesforce demonstrates Agentforce; a significant step toward leveraging AI agents to transform customer success, operational efficiency, and innovation across industries. Their tagline, "Humans with agents drive customer success," captures the idea of collaboration and synergies. Salesforce has a comprehensive approach to making AI Agents accessible and effective for all. ✅ A definition and framework for AI Agents. At its core, an Agentforce agent is defined by five key elements: - Role: The purpose of the agent on your team. - Knowledge: The data the agent needs to be successful. - Actions: The goals an agent can fulfill. - Guardrails: The guidelines an agent operates under. - Channel: The applications where the agent gets work done. ✅ Three agents available at launch: - Customer Service Agent: Enhances customer support with dynamic, conversational AI to handle inquiries 24/7. - Sales Development Agent: Engages prospects around the clock, acting as an intelligent sales agent that drives lead generation and customer acquisition. - Sales Coach Agent: Provides tailored coaching for sales representatives, including personalized feedback, pitch practice, and negotiation strategies, making every rep the best they can be. ✅ Additional agents coming soon: - Agentforce Assistant: Easily create and execute tasks for every employee. - Marketing Agents: Autonomously optimize and personalize marketing campaigns. - Commerce Agents: Instantly set up, manage, and optimize online storefronts. - Employee Service Agents: Automate onboarding and provisioning for new hires, enhancing internal operations. ✅ I see significant potential for Agentforce solutions in sectors like: - Banking: Automating credit risk assessments, fraud detection, and loan processing. - Retail: Managing inventory and order fulfillment, driving personalized marketing campaigns. - Healthcare: Supporting patient scheduling, providing medical assistants, and handling billing queries. - Manufacturing: Predicting equipment failures, optimizing supply chain management, and enhancing safety compliance. - Finance: Automating financial audits, reporting, and compliance monitoring. - IT: Modernizing legacy software systems, handling software requests, and performing security audits. 💡 While these first iterations of agents are powerful, they are still in the early stages, focusing on specific tasks without yet collaborating with other agents. However, this thoughtful and focused start lays the foundation for more sophisticated, interconnected AI systems in future versions—paving the way for a new era in how businesses leverage large language models and AI-driven solutions. I’m excited to see where Salesforce takes Agentforce next. Kudos to Salesforce for leading the charge in AI innovation and making these powerful tools even more accessible!

Recently the engineers released a 20K-word document outlining changes to the Salesforce platform since 2020 … and we talked about the first 2 big things: (1) #Hyperforce and (2) Data Cloud. There’s also (3) #Agentforce – which deserves attention. Hidden in section 6.2 is this: “Centralizing customer data into a single source of truth is crucial but challenging due to data fragmentation and the complexity of system management.” Thus Data Cloud, but: Why is this “crucial”? Because #AI, #GenAI and agentic AI like #Agentforce doesn’t work without data management. The #CDP like Data Cloud is item number 1. Built on #Hyperforce, Data Cloud is basically an integrated infrastructure and no-code platform to consolidate data. Data beautifully aligned has no purpose unless it is put to work. The first – and still the primary – use for CDPs in my opinion is analytics: pulling insights out of data, building predictive models and recommendations. Mere better segmentation can rescue a stalling business; direct marketers have known this for decades. Predictive models often use machine learning, which is a subset of AI. But I would say they are still just a genre of data organization. Really putting data to work requires something more – and this is why #Agentforce matters. Putting data to work requires decisions: What data and what work? Decisions can be made by a rule or a trigger: If a person abandons their cart, wait a day and send them a note. Such a rule can easily be set by a person. #Agentforce happens when the decisions around (a) what data, and (b) what action aren’t so obvious. Salesforce’s existing AI Platform already includes a layer for managing, training, and tuning models, incl. a no-code Model Builder and Prompt Builder. But there have to be ways to integrate AI into business applications like Marketing, Sales and Service Clouds. #Agentforce is like a co-pilot but more helpful. We use #RAG and outside LLMs of course to ground prompts in your own data and a Trust Layer to ensure usability, but there is more to agentic AI. So the #Agentforce Platform incorporates: 💥 Planner Service, which: (a) Interprets the user’s request (their intent & sentiment) using NLP methods and aligns this to a framework of topics (b) Structures a plan to respond to the request, using instructions (guardrails) (c) Initiates actions directly via other services, incl. actions to locate more data #Agentforce itself is the platform for building agents, but we can see how it requires a bigger platform around it to get work done. It needs Data Cloud to access unstructured data (like call center FAQs or contracts) and use it to do (b) above – make a plan, within boundaries. It also needs something like the Salesforce Platform w/ metadata for cross-department cooperation and – most important – built-in automations like Flow Builder and Process Automation to do (c) above, i.e., trigger actions and workflows.

In my column for this week, I make the case that companies should start planning for the next stage of artificial intelligence: the orchestration of multiple agents across their businesses. Most companies are still figuring out how to deploy even one AI-powered agent that can perform a task autonomously or in coordination with humans. But developers are creating protocols to harness these agents into teams that handle everything from customer service and coding to supply chain, logistics, finance, marketing and business strategy. Given the pace of innovation and the time it takes for organizations to adapt, companies will do themselves a favor by getting ready now for multiagent systems increasingly available later this year. Accenture’s chief AI officer, Lan Guan, says only 10% to 15% of her clients currently use multiagent systems, but she expects that percentage to exceed 30% within 18 to 24 months. All told, Accenture has more than 50 multiagent systems today for a range of industries and markets, and expects that number to hit more than 100 by the end of the year. The firm said customers such as carmaker BMW, consumer-brands company Unilever and sports giant ESPN are currently adopting these systems. Accenture last month introduced Trusted Agent Huddle, which it said allows agent-to-agent interoperability with partners such as technology companies Amazon Web Services, Google Cloud, Meta, Microsoft, Nvidia, Oracle, Salesforce, SAP and ServiceNow. Salesforce and Google are working on a protocol called A2A, or Agent-to-Agent. The protocol, which allows agents within Salesforce’s Agentforce ecosystem to interact with each other as well as external agents, focuses on areas such as authentication, identification and message passing, according to Gary Lerhaupt, vice president of product architecture for Agentforce. Keyway, a commercial real-estate tech startup, provides a glimpse into how the concept works in practice, according to co-founder and Chief Executive Matias Recchia. It offers asset managers and property managers a multiagent platform that uses coordinated interactions to address questions such as how to price a rental property or target amenities and incentives. Principal Financial Group has embedded individual AI agents across domains including software engineering co-pilots, claims summarization and post-call analytics, according to Chief Information Officer Kathy Kay. They largely operate within defined scopes, but the investment management and insurance company is actively building the technical foundation to support agent-to-agent collaboration, Kay said.   “These are not isolated functions,” Kay said. “They are systems of tasks that, when connected through intelligent agents, can drive faster insights and better outcomes across the enterprise.”

I believe Salesforce’s move to acquire Momentum.io is more significant than it first appears. Salesforce just signed a definitive agreement to bring Momentum into its ecosystem, strengthening Agentforce 360 and Slack for enterprise sales teams. On the surface, it looks like another conversational AI play. I see it as a deliberate step toward owning the revenue execution layer. Traditional CRM systems rely heavily on manual inputs. After a call, sales teams are expected to log buying intent, objections, pricing discussions, and next steps. From what I’ve observed across enterprise environments, that process is inconsistent, and critical signals often never make it into the system in a structured way. Momentum addresses that gap by converting unstructured data from Zoom, Google Meet, emails, and call recordings into structured intelligence that feeds directly into workflows. I see this as a shift from passive data storage to active workflow orchestration, where insights immediately influence execution instead of sitting in dashboards. This move builds on Salesforce’s earlier acquisition of Qualified, which strengthened inbound qualification at the top of the funnel. With Momentum reinforcing the mid-funnel, I see Salesforce steadily closing the loop between engagement and execution inside Agentforce 360. To me, the larger signal is architectural. The long-standing separation between CRM systems that store data and revenue intelligence tools that interpret it is starting to collapse. Industries with longer, conversation-heavy buying cycles, such as B2B SaaS, enterprise technology, financial services, and regulated sectors like healthcare, are likely to see the strongest impact. In these environments, deal outcomes depend on subtle signals captured during discovery, demos, and negotiations, and I believe grounding AI agents in real conversations materially improves forecasting and pipeline quality. I think this acquisition signals that CRM platforms are evolving from systems of record into systems that actively coordinate revenue activity. The real differentiator will be how effectively enterprises turn conversations into structured, actionable outcomes. DM if you're exploring how Agentforce fits into your revenue stack. #SalesAutomation #EnterpriseAI #B2BSales #Salesforce #Agentforce #Momentum #SalesforceAcquisition #CRM #EnterpriseAI MuleSoft Community Salesforce Developers Salesforce News & Insights

One year after introducing Agentforce and executing a remarkable pivot, Dreamforce offered the perfect opportunity to take stock of Salesforce’s progress on the Agentic AI front. Here are my 9 takeaways. Agentforce 360 – Salesforce unveiled the 4th iteration of its agentic platform, now renamed Agentforce 360. After adding Testing Center to validate agents, Command Center for real-time monitoring and optimization, it is adding Agentforce Script for deterministic agent behavior and complex document handling. These rapid iterations underscore that deploying, monitoring, and optimizing AI agents remains a complex undertaking. Traction and Scale – With 12,000 customers experimenting with Agentforce, interest is undeniable. The key question is how much will convert into paid, production deployments. Salesforce expects paying customers to double to 10,000 by year-end. Agentforce alone contributed $440M of the $1.2B Data 360 and AI ARR in Q2 FY26. Adoption Model – Salesforce is reshaping its customer success model around Forward Deployed Engineers (FDEs), planning to deploy 1,000 by year-end and expand capacity through partners. The move reflects the conviction that hands-on engineering engagement is critical to AI adoption. Pricing Evolution – Salesforce expanded its per-conversation model with flexible credits across multiple consumption metrics. It added a per-seat option offering, a hyperscaler-style framework through pre-committed usage, flex agreements to convert seat-based licenses into consumption, and an "all you can eat" Agentic Enterprise License Agreement. Agent Orchestration – Salesforce introduced MuleSoft Agent Fabric for Agent orchestration. It combines agent and tool cataloging for discovery, a robust trust and security layer, and the MuleSoft Agent Visualizer for observability. Voice AI – Salesforce unveiled Agentforce Voice, enabling the creation of Voice AI agents powered by its Tenyx acquisition, extending AI capabilities into natural voice interactions. IT Service Management (ITSM) – Salesforce introduced Agentforce IT Service to enter the ITSM market, a move that felt in response to ServiceNow’s expansion into CRM. Slack at the Forefront – Salesforce is making a major push to position Slack as the universal client for its applications. Slack gives non-Cloud users access to apps and the ability to search across enterprise data and knowledge assets. It emphasizes conversational experiences, offering an alternative to complex Lightning navigation. For example, Slack channels provide direct access to Salesforce objects. The Next Frontier – Salesforce set a bold long-term revenue target of $60B by FY30, reflecting a 10% organic CAGR. While a reset from its 2022 plan, it remains an ambitious goal. Salesforce is clearly doubling down on its AI-driven transformation. Despite headwinds, the enterprise application juggernaut is demonstrating remarkable agility in seizing its AI opportunity. #cx #salestech #dreamforce

Salesforce’s much-hyped Agentforce promised to bring large language model (LLM) intelligence directly into the CRM. But as early adopters quickly learned, the execution has fallen short. Customers report limited flexibility, integration challenges, and performance gaps that don’t align with the complexity of enterprise use cases. The result? A wave of Salesforce customers are re-evaluating their AI strategy, looking beyond Salesforce-native tools to harness the true potential of LLMs. 🔎 Why Agentforce Fell Short While Agentforce had an ambitious vision, several factors hindered adoption: • Closed ecosystem – Rigid guardrails prevented enterprises from bringing their own fine-tuned models. • Scalability concerns – Struggled to handle large-scale enterprise data. • Vendor lock-in – AI tied tightly to Salesforce’s stack, leaving little room for multi-cloud or hybrid approaches. • Limited connectors – Many organizations rely on MCP servers and diverse AI platforms (OpenAI, Anthropic, Llama, Mistral, etc.) which Agentforce did not support natively. 🌐 The New Path: External AI Platforms + Salesforce Data Enterprises don’t want to be boxed in. They need AI that can: • Plug into Salesforce data easily (Cases, Accounts, Opportunities, Knowledge). • Leverage enterprise-grade AI platforms that already fit into their ecosystem. • Support MCP (Model Context Protocol) for interoperability across AI tools, clouds, and servers. • Enable fine-tuning on proprietary datasets without vendor restrictions. By decoupling Salesforce data from Agentforce and instead exposing it via APIs, Data Cloud, or Data Federation, organizations can run LLMs where it makes the most sense: on external AI platforms that support flexibility, compliance, and scaling. ⚡ Practical Approaches 1. API + Data Cloud Integration Export Salesforce objects (Case, MessagingSession, Knowledge) securely to an external AI pipeline for training and inference. 2. n8n / MuleSoft / Middleware Orchestration Use low-code automation platforms to route data between Salesforce and LLM servers. 3. MCP Servers for Standardization Adopt MCP to allow AI models to interoperate with Salesforce data and other enterprise systems without heavy customization. 4. Fine-Tuned Enterprise Models Train LLMs on your domain-specific Salesforce data (case logs, support chats, sales playbooks) while keeping the model infrastructure in your control. 🏆 The Benefits of Going External • Freedom of Choice: Use the right LLM for the right use case. • Enterprise Security: Keep sensitive data in compliance with company policies. • Future-Proofing: Avoid being tied to a single vendor’s AI limitations. • Faster Innovation: Experiment with open-source and commercial LLMs side by side. 📌 Conclusion The Agentforce experiment showed us what’s possible—but also what enterprises truly need: flexibility, interoperability, and ownership over their AI stack. Wait is over Salesforce customers jump …

🚀 NEW RESEARCH: The Trillion-Dollar Shift to Agentic AI 🔍 My colleague Nick Patience and I just completed a major study of Salesforce's #Agentforce, a bellwether for enterprise agentic AI. Why does this matter? Because agent-based will soon realize a global economic shift. 🌍 The Big Picture: Agent-based AI is set to transform global enterprise operations, unlocking up to $6 trillion in economic impact by 2028, according to our market calculations. AI agents will rapidly become a pervasive source of digital labor, automating complex workflows across industries from finance and healthcare to retail and manufacturing. 🚨 This is the next major evolution in AI and one that will be everywhere. 🚨 🌟 Key Insights from This Research into Actual Agentic AI Adopters: ✅ 5X Faster ROI – Early adopters of Agentforce achieved payback in weeks, not months or years like DIY agent alternatives. ✅ 20%+ Lower TCO – Pre-built workflows & integrations slash implementation costs vs. custom AI builds. ✅ Speed to Market – Deployments in 4-6 weeks, while DIY projects often take 12+ months. ✅ Higher Efficiency & Revenue – AI-powered lead conversion rates up 25%, case resolution times 40% faster. ✅ The Cost of Doing Nothing? Enterprises delaying AI adoption risk losing market share, efficiency, and competitive edge. 💡 DIY vs. Agentforce: The Reality Check I find that a good many CIOs and IT leaders consider building agentic AI solutions from scratch. But our analysis shows that DIY can take up to 10X the effort—delaying AI-driven impact while incurring higher developmental/operational risks, longer dev cycles, and numerous hidden costs. 🌎 What’s at Stake? The agent-based AI market is projected to grow from $5B in 2024 to $50B+ by 2028—an annual growth rate of 78%. Companies acting now will dominate. 🎯 Who Benefits from Agent-Based AI? 🔹 CIOs looking for rapid AI business value without the dev burden 🔹 Sales & Marketing leaders who want personalized customer engagement 🔹 Operations teams automating repetitive processes at scale 🔎 The Verdict? 📌 Agentic AI is not optional. It will be everywhere, reshaping digital work and enterprise automation on a massive scale. 📌 Companies leveraging Agentforce report faster ROI, lower costs, and real business impact. Read the full research for the deep dive. 📊👇 https://lnkd.in/eXYQQWxV cc Dan O'Brien Daniel Newman Tiffani Bova Bill Ruff Deepak Surana Ron Westfall Keith Townsend Mitch Ashley Patrick Moorhead | Marc Benioff Ariel Kelman Vala Afshar Kaylin Voss Joe Ferraro Anne Chen Peter Coffee Antonio Figueiredo Austin Guevara | Negin Boushehri Tom Hebner Evan Kirstel B2B TechFluencer Antonio Vieira Santos Louis C. Marsha Collier Tamara McCleary David Terrar Elitsa Krumova #AI #CIO #AgenticAI #LLMs #Workflow #DigitalLabor #Automation #Salesforce #FutureOfWork #TDX25 #EnterpriseAI

𝐀𝐠𝐞𝐧𝐭𝐟𝐨𝐫𝐜𝐞 3: 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐠𝐞𝐧𝐭𝐬 𝐉𝐮𝐬𝐭 𝐆𝐨𝐭 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐚𝐧𝐝 𝐅𝐚𝐬𝐭𝐞𝐫 Salesforce’s Agentforce 3 update has arrived, bringing major advances that make AI agents more powerful and easier to deploy across any industry. Here’s what’s new—and why it matters: 🔹 𝑪𝒐𝒏𝒕𝒆𝒙𝒕𝒖𝒂𝒍 𝑴𝒆𝒎𝒐𝒓𝒚 Grab AI memory lets Agentforce 3 agents retain context across interactions, enabling more human-like, consistent conversations. Agents can now remember customer history and preferences, improving relevance and continuity in every response. 🔹 𝑶𝒓𝒄𝒉𝒆𝒔𝒕𝒓𝒂𝒕𝒊𝒐𝒏 𝑼𝒑𝒈𝒓𝒂𝒅𝒆𝒔 Improved orchestration capabilities allow multiple AI agents to coordinate tasks and workflows seamlessly. New integrations turn any API into an agent-ready service, making it easier to orchestrate complex, multi-agent processes across business systems. 🔹 𝑴𝒖𝒍𝒕𝒊-𝑪𝒍𝒐𝒖𝒅 𝑺𝒖𝒑𝒑𝒐𝒓𝒕 Agentforce 3 is designed to work across platforms. It offers plug-and-play connectivity to partner services including Amazon Web Services (AWS), Google Cloud, and more so your AI agents can leverage tools and data from multiple clouds. It even supports hosting third-party AI models natively within Salesforce infrastructure. 🔹 𝑷𝒓𝒆𝒃𝒖𝒊𝒍𝒕 𝑨𝒈𝒆𝒏𝒕 𝑻𝒆𝒎𝒑𝒍𝒂𝒕𝒆𝒔 Jumpstart your AI initiatives with over 100+ pre-configured agent actions and templates for common use cases. These ready-made templates come with built-in best practices and setup guidance for fast deployment—allowing businesses to roll out industry-specific bots (for sales, service, etc.) in record time. 𝑽𝒂𝒍𝒖𝒆 𝑨𝒄𝒓𝒐𝒔𝒔 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒊𝒆𝒔: What do these upgrades mean for businesses? In short: greater automation, enhanced customer experiences, and faster issue resolution across the board. Agentforce 3’s contextual intelligence and orchestration enable end-to-end automation of routine tasks, freeing up teams for higher-value work. Customers enjoy more personalized, 24/7 service-boosting satisfaction and loyalty. 📊 𝑬𝒂𝒓𝒍𝒚 𝒂𝒅𝒐𝒑𝒕𝒆𝒓𝒔 𝒉𝒂𝒗𝒆 𝒂𝒍𝒓𝒆𝒂𝒅𝒚 𝒔𝒆𝒆𝒏: 15%+ drop in average case handling time Up to 70% of support chats resolved autonomously during peak periods These benefits translate across #finance, #healthcare, #education, #retail, and beyond. GetOnCRM Solutions Inc : Your Partner in Agentforce 3 Success At GetOnCRM, we are fully aligned—both technically and strategically—to help businesses harness the potential of Agentforce 3. ✅ We integrate Agentforce with your existing Salesforce and cloud systems ✅ Configure and customize prebuilt agent templates ✅ Ensure secure, compliant orchestration for enterprise use ✅ Identify automation opportunities tailored to your business goals This is more than a product upgrade; it’s a shift toward a smarter, AI-led future of CRM. And our team is ready to help you lead that change. #Salesforce #Agentforce3 #AI #Automation #GetOnCRM #CRM #CustomerExperience #DigitalTransformation

The brain of #Agentforce is our #Atlas learning and reasoning engine, developed by Salesforce Research. Atlas reasons over your data and business processes. Atlas generates a plan, evaluates, and refines until it feels confident it knows how to accomplish your goals. It pulls in the structured and unstructured data it needs from Data Cloud using advanced #RAG and custom CRM #embeddings. Then Atlas takes action across the Customer 360, whether that’s automating a campaign in Marketing Cloud, resolving a case in Service Cloud, or, in OpenTable’s case, confirming that perfect dinner reservation for your friend's birthday. The best part is the more you use Atlas, the smarter it gets. We've pioneered reinforcement learning based on customer outcomes (#RLCO)— Agentforce is continuously tuning to align with your business outcomes, such as higher conversion, faster resolutions, and increased CSAT. Your data is never our product, which means your outcome data is proprietary to you. The results we're seeing are incredibly promising. Agentforce powered by Atlas is delivering 33% more accuracy and 2x more relevance than DIY AI. This is making the difference between a DIY science project and a trusted enterprise-grade agent you can confidently deploy into production. Watch this clip from this morning's Dreamforce Main Keynote: #DF24 #agents #enterprise #reasoning

Just wrapped up Salesforce's Agentforce 3 release event, and I’m excited to share the top 3 updates you don’t want to miss. #Sponsored Background: Agentforce was introduced as a platform designed to scale digital labor with complete visibility and plug-and-play integration. What’s New & Why It Matters: 1️⃣Command Center – Unified Observability for Agents You can now monitor both agent and human performance in a single view. This is a big step forward for true human-machine collaboration. 2️⃣ Open & Interoperable – MCP (Model Context Protocol) MCP is quickly becoming the standard. In a fast-moving AI landscape, we need systems that can seamlessly exchange and utilize information, and this does exactly that. 3️⃣200+ Prebuilt Industry Actions Faster time to value is critical in any tech transformation. These prebuilt actions help teams realize ROI quickly without starting from scratch. Which of these updates are you most excited about? Let me know in the comments! #Salesforce #Agentforce3 #AI #DigitalLabor #HumanMachineCollaboration