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Salesforce Data Cloud
11Jan

Getting Started with Data Cloud

Before diving into Data Cloud, ensure your team is prepared by reviewing key considerations, navigation, and licensing details. Prepare for Data Cloud Key Readiness Steps: ✅ Understand guidelines and limitations that may impact billing.✅ Review brand management and organizational structure within Data Cloud.✅ Define a data strategy by exploring data model concepts.✅ Analyze existing data and sources to determine ingestion needs.✅ Plan for unified customer profiles to drive insights.✅ Identify users and their permissions for effective access control.✅ Establish goals and outline how your team will leverage data. 📄 Download the Interactive Data Cloud Checklist & Considerations PDF Navigating Data Cloud Once Data Cloud is enabled, access it through the App Launcher. Key Navigation Features: 📌 Data Ingestion & Modeling – Manage data sources through: 📌 Data Exploration & Analysis – View and interact with data using: 📌 Identity Resolution – Define match and reconciliation rules via the: 📌 Insights & Segmentation – Analyze and act on data with: 📌 Setup & Administration – Configure settings through the: Understanding Data Cloud Licensing & Usage Standard Editions & Add-On Licenses 🔹 Data Cloud is included in various Salesforce editions, with additional features available through add-on licenses.🔹 Your Data Cloud license determines available features—some require separate purchases. Data Cloud Guidelines & Limits 💡 Best Practices: Follow recommended guidelines to optimize performance and adoption.🚧 Feature Limits: Some features have usage thresholds affecting performance or billing.⚙ Scalability: Many limits are adjustable—work with your account executive to customize solutions. Understanding Data Cloud Billing 💳 Billable Usage Types – Certain features impact Data Services Credit consumption.📊 Monitoring Usage – Track usage in your org’s Digital Wallet.📑 Billing Documentation – Review feature & usage documentation before activation.💬 Consult Your AE – Understand cost implications by discussing with your Salesforce Account Executive. Data Cloud & Einstein AI 🤖 Einstein AI is built on data – Review which Einstein features use Data Cloud to optimize performance. Stay Updated on Data Cloud 📢 Feature Releases: Data Cloud updates twice monthly. View the latest updates.📜 Licensing & Access Changes: Stay informed about changes in feature availability, billing, and permissions. Ready to unlock the power of Data Cloud? Start your journey today! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 11, 2025in Data, Salesforce, Salesforce Data Cloud

Autonomy, Architecture, and Action

Redefining AI Agents: Autonomy, Architecture, and Action AI agents are reshaping how technology interacts with us and executes tasks. Their mission? To reason, plan, and act independently—following instructions, making autonomous decisions, and completing actions, often without user involvement. These agents adapt to new information, adjust in real time, and pursue their objectives autonomously. This evolution in agentic AI is revolutionizing how goals are accomplished, ushering in a future of semi-autonomous technology. At their foundation, AI agents rely on one or more large language models (LLMs). However, designing agents is far more intricate than building chatbots or generative assistants. While traditional AI applications often depend on user-driven inputs—such as prompt engineering or active supervision—agents operate autonomously. Core Principles of Agentic AI Architectures To enable autonomous functionality, agentic AI systems must incorporate: Essential Infrastructure for AI Agents Building and deploying agentic AI systems requires robust software infrastructure that supports: Agent Development Made Easier with Langflow and Astra DB Langflow simplifies the development of agentic applications with its visual IDE. It integrates with Astra DB, which combines vector and graph capabilities for ultra-low latency data access. This synergy accelerates development by enabling: Transforming Autonomy into Action Agentic AI is fundamentally changing how tasks are executed by empowering systems to act autonomously. By leveraging platforms like Astra DB and Langflow, organizations can simplify agent design and deploy scalable, effective AI applications. Start building the next generation of AI-powered autonomy today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 11, 2025in Agentic AI, Data, Enterprise, Technology
What is Heroku
04Jan

What is Heroku

What is Heroku? Heroku is a modern, container-based Platform as a Service (PaaS) that enables developers to deploy, manage, and scale applications with ease. Designed for simplicity, flexibility, and elegance, it provides the fastest path for developers to take their apps to market. Key Features of Heroku: The Evolution of Heroku Heroku has recently undergone a transformation, becoming fully cloud-native with advanced integrations like Kubernetes, OpenTelemetry, and Agentforce, an AI-powered enhancement to its platform. These upgrades retain the platform’s hallmark simplicity while delivering more performance and tools, such as Graviton processors, EKS, ECR, and AWS Global Accelerator. AI-Powered Innovation: Agentforce Agentforce, Heroku’s latest feature, brings AI-powered automation to app development. It empowers both technical and non-technical users by offering natural language workflows for building applications, making it accessible to a wider range of business users. According to Betty Junod, Heroku’s Chief Marketing Officer at Salesforce, the platform now seamlessly combines user-friendly experiences with cutting-edge AI capabilities: “We’ve replatformed while keeping the experience as simple as ever, but now with added horsepower, Graviton performance, and managed AI tools like Bedrock.” Agentforce is particularly impactful for non-developers, guiding them through building apps and automating processes with no-code or low-code tools. This innovation aligns with Heroku’s mission to make app creation easier and more interactive: “It’s not just apps serving information anymore; users are engaging with them in entirely new ways.” Deliver Apps, Your Way Heroku is designed to serve a variety of needs, from quick prototypes to mission-critical enterprise applications. Its fully managed ecosystem allows you to build and scale apps efficiently, leveraging the tools and languages you already know and love. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 4, 2025in AI Tools, Data, Enterprise, Salesforce
Rise of Agentforce
03Jan

How Agentforce Augments Marketing Cloud

Key Features of Salesforce Agentforce for Marketing and Marketers AI-Powered Assistance: Agentforce leverages AI to automate and optimize marketing tasks, allowing your team to focus on strategic initiatives. From generating campaign plans to analyzing metrics, Agentforce acts as your trusted assistant working around the clock with nary a coffee break. Omnichannel Support: Engage with customers across multiple platforms, including email, social media, and messaging apps like WhatsApp. Agentforce ensures a consistent and personalized experience at every touchpoint. Recognizing your prospect and customer throughout every interaction. Real-Time Analytics and Reporting: Gain in-depth insights into your marketing performance with real-time analytics. Agentforce helps you track key metrics and make data-driven decisions to improve your campaigns. Integration with Salesforce CRM: Seamlessly connect Agentforce with your existing Salesforce CRM to leverage customer data and create more targeted and effective marketing strategies. Proactive Customer Engagement: Agentforce enables personalized, two-way conversations with customers, helping you build deeper relationships and reduce churn through proactive retention strategies. Added Value of Integrating Agentforce with Salesforce Marketing Cloud The integration of Agentforce with the Salesforce Marketing Cloud platform, whether you choose the Growth or Advanced edition, brings several benefits: Enhanced Personalization: With the power of AI and unified customer profiles, you can deliver highly personalized content and offers across every customer interaction. Scalability and Efficiency: The Growth edition supports companies looking to scale their marketing efforts with powerful data tools and content management features. The Advanced edition offers additional tools for deeper insights and more sophisticated customer engagement strategies. Seamless Campaign Management: Both editions provide tools to create, deploy, and adapt campaigns faster, with Agentforce helping to surface insights, define strategy, and generate content. Improved Customer Journeys: Use intelligent reprioritization and real-time interaction management to enhance customer journeys and drive better engagement. Elevating Customer Experience with AI-Powered Marketing Offering a great product or service is important—but have you considered how your customers feel when they engage with your brand? Beyond features and pricing, the real differentiator is the experience you create. How the customer experiences your company can be more impactful than how they experience your product or service. Salesforce research reveals that 80% of customers value their experience with a company as much as the products or services themselves. It’s not just about what you have to offer; it’s about how you make people feel at every interaction. Because in today’s competitive marketplace, customers don’t just buy products—they invest in experiences. In the go-to-market space, Salesforce Agentforce is redefining the Marketing Cloud experience by making customer interactions more intelligent, personalized, and human-like than ever before—all powered by AI. Imagine a marketing strategy where every interaction—every click, every purchase—delivers actionable insights instead of guesswork. This is the future of AI-driven marketing. In fact, recent research found that nearly half (46%) of financial services firms leveraging AI report enhanced customer experiences. With AI-driven automation, conversational intelligence, and predictive analytics, Agentforce helps businesses engage customers at the right time, in the right way, and on the right platform—building deeper, more meaningful relationships. Salesforce’s Ongoing Relationship with Data Since pioneering cloud-based CRM in 1999, Salesforce has been relentless in its pursuit of data-driven experiences. Over time, its capabilities have expanded far beyond traditional CRM, culminating in the Salesforce Data Cloud—a unified data platform that consolidates insights across Sales, Service, Commerce, and Marketing. Data, especially the data we use for ongoing marketing, lives in various platforms like email, advertising tools, social media, analytics, CRMs, and perhaps even spreadsheets. And it is completely unstructured. What began as a Data Management Platform has evolved into a real-time decision-making engine. By integrating Agentforce AI, Data Cloud moves beyond just storing information—it enables businesses to act on real-time insights with automation and intelligence that move deals closer to the goalpost. Meet Agentforce: Humanizing AI in Marketing Cloud At the heart of AI-driven marketing transformation is quality data—which is why Einstein AI is built on Salesforce Data Cloud. “AI is only as good as the data that powers it, and Salesforce is where thousands of companies across industries manage their sales, service, marketing, commerce, and IT data,” says Jayesh Govindarajan, Salesforce SVP of AI & Machine Learning. “That’s an advantage for Salesforce customers because they can use their existing data to create and deliver AI-generated content at scale, seamlessly within their current workflows.” With Agentforce, businesses go beyond access to data—they gain an AI partner that understands how to foster authentic customer connections. How AI is Humanizing the Marketing Experience Instead of generic messaging, Agentforce crafts and delivers hyper-personalized content, offers, and recommendations—at scale. The Future of AI-Driven Marketing is Here With Agentforce, Salesforce Data Cloud, and Einstein GPT, businesses can move beyond static campaigns and embrace dynamic, AI-driven experiences that feel authentic, intuitive, and deeply personal. The next era of marketing isn’t just about automation—it’s about delivering humanized AI experiences that drive lasting customer relationships. The evolution of Einstein, GPT, AI, and Data on top of a powerful marketing platform is the future of AI-driven marketing. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 3, 2025in Agentforce Platform, Data, Salesforce, Salesforce Data Cloud, Salesforce Marketing Cloud
AI Captivates the World
01Jan

AI Captivates the World

In the late 1990s, a transformative moment unfolded that expanded the world to enquiring minds—the screeching of a dial-up modem, followed by a pixelated “Welcome” screen that connected users to a vast, invisible network spanning the globe. The internet revolution redefined how people interacted with information and one another, permanently reshaping digital communication. Fast forward to 2024, and a similar wave of innovation is underway. Artificial intelligence is captivating the world with its ability to understand, create, and process information. Massive datasets can now be uploaded to AI tools, which instantly distill complex insights—tasks that once took teams of analysts weeks to complete are now executed in seconds. Just as the internet linked people and information, AI is deepening connectivity across all aspects of life, from healthcare and finance to workplaces and homes. In this evolving digital divide, designers hold a critical role—not only in making AI usable but in ensuring it remains understandable, trustworthy, and human-centered. As Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute, states, “If we want machines to think, we need to teach them to see.” The traditional linear process of problem ideation, design, prototyping, and delivery is no longer sufficient for AI design. Instead, designers find themselves on an “AI design rollercoaster”—a dynamic cycle of constant iteration. One day, a seemingly impossible feature is prototyped, and the next, the entire approach pivots due to breakthroughs in large language model (LLM) capabilities. Many teams develop working prototypes before even defining their target audience. It is akin to painting a landscape from a moving train—compelling, challenging, and occasionally bewildering. However, this state of flux is where innovation thrives. Strategies for Designers: Understanding AI’s Capabilities and Limitations Designing for AI requires an understanding of its strengths and weaknesses. While designers do not need to become machine learning engineers, they must grasp AI fundamentals to communicate effectively with technical teams. For example, neural networks excel at recognizing patterns in unstructured data but often struggle with logical reasoning. Recognizing these limitations prevents the development of features that sound promising in theory but fail in practice. Strategies for Designers: Designing for Data Scalability Data is the lifeblood of AI systems, yet its quality and availability fluctuate over time. Designers must create interfaces that can adapt to changing data landscapes. For instance, an AI-powered personal finance app may initially rely on basic transaction data but later incorporate richer datasets for advanced investment recommendations. Interfaces should be modular and scalable, capable of accommodating evolving AI functionalities. Strategies for Designers: The Role of Prototyping in AI Design Static wireframes and basic mockups are insufficient for AI-driven products. AI prototypes must capture the responsive, dynamic nature of intelligent systems. Interactive prototypes offer stakeholders a tangible preview of AI’s potential, highlighting both opportunities and challenges early in the design process. Strategies for Designers: Developing AI Design Intuition To navigate AI design effectively, professionals must cultivate an “AI design sixth sense”—an intuitive understanding of what works well in AI-driven interactions. Immersing in AI experiences, exploring different tools, and analyzing emerging design patterns help build this expertise. Strategies for Designers: Pushing Boundaries in AI Design There are no established rulebooks for AI design—only a vast frontier waiting to be explored. The absence of rigid norms offers designers the freedom to experiment and push boundaries. Some of the most groundbreaking innovations stem from unconventional ideas once deemed impractical. Strategies for Designers: Strengthening Collaboration Between Design and Engineering In AI product design, the traditional “design then handoff” model is giving way to a more integrated approach. Designers and engineers increasingly work in tandem, refining AI experiences through continuous iteration. Some of the most effective design solutions emerge from close collaboration with technical teams. Strategies for Designers: The Next Frontier of Design As AI design continues to evolve, the parallels to the early days of the internet are striking. The excitement, potential, and magnitude of change are reminiscent of Web 1.0, yet amplified in scope. Looking ahead, the field must address profound questions: Will AI become indistinguishable from human intelligence? Will designers craft interfaces for AI-human hybrids yet to be imagined? Designers play an essential role in shaping this future—not as passive observers, but as architects of the next digital revolution. The experiences they create will define humanity’s interactions with artificial intelligence. This responsibility should inspire innovation, challenge conventions, and push the boundaries of what is possible. Call to Action Begin the AI design journey today. Choose an AI tool, explore its interface, and analyze its capabilities. Identify strengths, weaknesses, and opportunities for improvement. Share insights with fellow designers and contribute to the evolving conversation on AI design. The next breakthrough may arise from a single moment of curiosity. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 1, 2025in AI Tools, Artificial Intelligence, Data

1 Billion Enterprise AI Agents

Inside Salesforce’s Ambition to Deploy 1 Billion Enterprise AI Agents Salesforce is making a bold play in the enterprise AI space with its recently launched Agentforce platform. Introduced at the annual Dreamforce conference, Agentforce is positioned to revolutionize sales, marketing, commerce, and operations with autonomous AI agents, marking a significant evolution from Salesforce’s previous Einstein AI platform. What Makes Agentforce Different? Agentforce operates as more than just a chatbot platform. It uses real-time data and user-defined business rules to proactively manage tasks, aiming to boost efficiency and enhance customer satisfaction. Built on Salesforce’s Data Cloud, the platform simplifies deployment while maintaining powerful customization capabilities: “Salesforce takes care of 80% of the foundational work, leaving customers to focus on the 20% that truly differentiates their business,” explains Adam Forrest, SVP of Marketing at Salesforce. Forrest highlights how Agentforce enables businesses to build custom agents tailored to specific needs by incorporating their own rules and data sources. This user-centric approach empowers admins, developers, and technology teams to deploy AI without extensive technical resources. Early Adoption Across Industries Major brands have already adopted Agentforce for diverse use cases: These real-world applications illustrate Agentforce’s potential to transform workflows in industries ranging from retail to hospitality and education. AI Agents in Marketing: The New Frontier Salesforce emphasizes that Agentforce isn’t just for operations; it’s poised to redefine marketing. AI agents can automate lead qualification, optimize outreach strategies, and enhance personalization. For example, in account-based marketing, agents can analyze customer data to identify high-value opportunities, craft tailored strategies, and recommend optimal engagement times based on user behavior. “AI agents streamline lead qualification by evaluating intent signals and scoring leads, allowing sales teams to focus on high-priority prospects,” says Jonathan Franchell, CEO of B2B marketing agency Ironpaper. Once campaigns are launched, Agentforce monitors performance in real time, offering suggestions to improve ROI and resource allocation. By integrating seamlessly with CRM platforms, the tool also facilitates better collaboration between marketing and sales teams. Beyond B2C applications, AI agents in B2B contexts can evaluate customer-specific needs and provide tailored product or service recommendations, further enhancing client relationships. Enabling Creativity Through Automation By automating repetitive tasks, Agentforce aims to free marketers to focus on strategy and creativity. Dan Gardner, co-founder of Code and Theory, describes this vision: “Agentic AI eliminates friction and dissolves silos in data, organizational structures, and customer touchpoints. The result? Smarter insights, efficient distribution, and more time for creatives to do what they do best: creating.” Competitive Landscape and Challenges Despite its promise, Salesforce faces stiff competition. Microsoft—backed by its integration with OpenAI’s ChatGPT—has unveiled AI tools like Copilot, and other players such as Google, ServiceNow, and HubSpot are advancing their own AI platforms. Salesforce CEO Marc Benioff has not shied away from the rivalry. On the Masters of Scale podcast, he criticized Microsoft for overpromising on products like Copilot, asserting that Salesforce delivers tangible value: “Our tools show users exactly what is possible, what is real, and how easy it is to derive huge value from AI.” Salesforce must also demonstrate Agentforce’s scalability across diverse industries to capture a significant share of the enterprise AI market. A Transformative Vision for the Future Agentforce represents Salesforce’s commitment to bringing AI-powered automation to the forefront of enterprise operations. With its focus on seamless deployment, powerful customization, and real-time capabilities, the platform aims to reshape how businesses interact with customers and optimize internal processes. By targeting diverse use cases and emphasizing accessibility for both technical and non-technical users, Salesforce is betting on Agentforce to drive adoption at scale—and position itself as a leader in the increasingly competitive AI market. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 27, 2024in Agentforce Platform, AI Tools, Data, Enterprise, Google, Salesforce, Technology
26Dec

Why Build a General-Purpose Agent?

A general-purpose LLM agent serves as an excellent starting point for prototyping use cases and establishing the foundation for a custom agentic architecture tailored to your needs. What is an LLM Agent? An LLM (Large Language Model) agent is a program where execution logic is governed by the underlying model. Unlike approaches such as few-shot prompting or fixed workflows, LLM agents adapt dynamically. They can determine which tools to use (e.g., web search or code execution), how to use them, and iterate based on results. This adaptability enables handling diverse tasks with minimal configuration. Agentic Architectures Explained:Agentic systems range from the reliability of fixed workflows to the flexibility of autonomous agents. For instance: Your architecture choice will depend on the desired balance between reliability and flexibility for your use case. Building a General-Purpose LLM Agent Step 1: Select the Right LLM Choosing the right model is critical for performance. Evaluate based on: Model Recommendations (as of now): For simpler use cases, smaller models running locally can also be effective, but with limited functionality. Step 2: Define the Agent’s Control Logic The system prompt differentiates an LLM agent from a standalone model. This prompt contains rules, instructions, and structures that guide the agent’s behavior. Common Agentic Patterns: Starting with ReAct or Plan-then-Execute patterns is recommended for general-purpose agents. Step 3: Define the Agent’s Core Instructions To optimize the agent’s behavior, clearly define its features and constraints in the system prompt: Example Instructions: Step 4: Define and Optimize Core Tools Tools expand an agent’s capabilities. Common tools include: For each tool, define: Example: Implementing an Arxiv API tool for scientific queries. Step 5: Memory Handling Strategy Since LLMs have limited memory (context window), a strategy is necessary to manage past interactions. Common approaches include: For personalization, long-term memory can store user preferences or critical information. Step 6: Parse the Agent’s Output To make raw LLM outputs actionable, implement a parser to convert outputs into a structured format like JSON. Structured outputs simplify execution and ensure consistency. Step 7: Orchestrate the Agent’s Workflow Define orchestration logic to handle the agent’s next steps after receiving an output: Example Orchestration Code: pythonCopy codedef orchestrator(llm_agent, llm_output, tools, user_query): while True: action = llm_output.get(“action”) if action == “tool_call”: tool_name = llm_output.get(“tool_name”) tool_params = llm_output.get(“tool_params”, {}) if tool_name in tools: try: tool_result = tools[tool_name](**tool_params) llm_output = llm_agent({“tool_output”: tool_result}) except Exception as e: return f”Error executing tool ‘{tool_name}’: {str(e)}” else: return f”Error: Tool ‘{tool_name}’ not found.” elif action == “return_answer”: return llm_output.get(“answer”, “No answer provided.”) else: return “Error: Unrecognized action type from LLM output.” This orchestration ensures seamless interaction between tools, memory, and user queries. When to Consider Multi-Agent Systems A single-agent setup works well for prototyping but may hit limits with complex workflows or extensive toolsets. Multi-agent architectures can: Starting with a single agent helps refine workflows, identify bottlenecks, and scale effectively. By following these steps, you’ll have a versatile system capable of handling diverse use cases, from competitive analysis to automating workflows. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 26, 2024in Data
Tools to Liberate Salesforce Data
23Dec

Student Experience Modernization

Transforming the Student Journey: Utah State University Launches ‘OneUSU CRM’ with Salesforce Utah State University (USU) proudly announces the launch of OneUSU CRM, an AI-driven Customer Relationship Management (CRM) and Marketing Automation Platform built on Salesforce Education Cloud for Student Experience Modernization. This forward-thinking initiative is set to transform the student experience by simplifying services, enhancingfor engagement, and creating personalized educational journeys for all students—from prospective applicants to lifelong alumni. “USU is redefining student support, starting from the first interaction as prospective students to ongoing engagement as alumni,” said Margo Martinez, VP & GM of Education at Salesforce. “With their AI-powered engagement platform, USU is enabling deeper collaboration among faculty and staff, delivering holistic, personalized support, and strengthening lifelong relationships with students. These efforts will enhance retention and institutional impact.” Breaking Down Silos for a Unified Experience For years, USU’s student data and communication systems operated independently, resulting in fragmented processes. OneUSU CRM consolidates these disparate systems into a single, unified platform, providing a 360-degree view of students and donors. This comprehensive approach enables USU to offer seamless, student-centric experiences, streamline processes, and improve service delivery across the university. “Implementing Salesforce as an enterprise solution will revolutionize how we operate,” said Bill Plate, VP of Marketing and Communications at USU. “It will foster unprecedented collaboration across the university, breaking down silos, enhancing communication, and allowing us to better engage with students and alumni. The result is an elevated experience that strengthens our institutional impact.” The Lifecycle Connection The CRM will connect every touchpoint in the student lifecycle—from recruitment and admissions to active alumni engagement. This integration provides actionable insights to better serve stakeholders, boost enrollment, improve retention, and cultivate stronger relationships with alumni and donors. The initiative aligns with USU’s mission to leverage data for meaningful improvements in the university experience. “This project lays the groundwork for USU to provide personalized support at every stage of the student lifecycle,” added Niel Nickolaisen, USU’s IT Director. “By making comprehensive data accessible to those best positioned to assist, we are better equipped to meet students’ needs, enhance their learning journey, and create stronger institutional bonds.” Four Pillars of Transformation in Student Experience Modernization The OneUSU CRM initiative will roll out in four key phases over 18 months, each focusing on a core area: Guiding Implementation USU’s Operational Strategy division will oversee the rollout, with cross-divisional teams led by Rene Eborn spearheading the transition to Salesforce Education Cloud. By adopting agile principles, minimizing customizations, and redesigning legacy processes, USU is committed to a smooth and effective implementation. “After an extensive analysis, we’re ready to transform and simplify services for our students and community,” noted Eborn. “This initiative will reimagine how we operate, ensuring a seamless transition and delivering optimal performance across the university.” Driving Institutional Impact The project began in November, with the first phase launching in summer 2025. Each phase will build on the previous one, and progress updates will be shared regularly. “This initiative is an opportunity to streamline processes and create a connected experience for students, faculty, and alumni,” said Eric Hawley, CIO at USU. “By improving data, communication, and marketing with a student-first focus, we aim to boost enrollment and retention, shorten time to graduation, and foster lasting relationships with alumni and donors.” Student Experience Modernization Students, faculty, and staff can expect a more connected, responsive campus environment. Detailed updates and training opportunities will be provided to ensure a smooth transition. Student Experience Modernization. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 23, 2024in Data, Enterprise, Marketing Automation, Salesforce, Salesforce Education Cloud
AI Sales Agents Explained
20Dec

AI Sales Agents Explained

If you were to ask a sales rep why they chose a job in sales, they’d probably tell you something like, “I love helping to people. I’m ambitious and goal-oriented, and no two days are ever the same.” The reality, however, is that a lot of time in sales isn’t spent selling. Recent data suggests that sales reps dedicate only 28% of their time to actual selling, with the rest swallowed up by administrative tasks and non-revenue-generating work. To ease this burden, sales teams are turning to AI sales agents, enabling them to focus more on building relationships and closing deals. Below, we explore the different types of AI sales agents and how businesses are using them to increase productivity, efficiency, and revenue. What is an AI sales agent? AI sales agents are autonomous applications that analyze and learn from sales and customer data to perform tasks with little or no human intervention. These agents can manage a wide range of activities, from top-of-funnel tasks like nurturing leads via email outreach, answering questions, booking meetings, and generating quotes to more integrated sales support like buyer roleplays and coaching. Unlike simple workflow automation, AI agents are capable of learning, enabling them to improve efficiency and act independently based on data and analysis. They often plug directly into existing CRMs, with pre-built capabilities or customizable configurations for specific business needs. Types of AI sales agents There are two primary types of AI sales agents: The ability to autonomously analyze data, create action plans, and execute them sets modern AI sales agents apart from traditional sales tools and bots. Key features of AI sales agents Benefits of AI sales agents Future trends for AI sales agents In the early days, AI in sales served primarily as a co-pilot — summarizing insights and assisting with tasks like forecasting. It often required significant human input and created siloed data challenges. Today, AI agents autonomously augment human teams, empowering them to focus on high-value tasks like building relationships. In the near future, AI sales agents are expected to handle increasingly complex workflows and multi-step processes across diverse channels. Potential advancements include: These developments promise to unlock new possibilities for efficiency, personalization, and customization in sales teams. AI sales agents pushing teams into a new era According to recent data, sales leaders are focusing on improving sales enablement, targeting new markets, and adopting new tools and technologies to drive growth. Challenges like scaling personalized interactions and hitting quotas are top of mind. AI sales agents directly address these needs, transforming sales organizations by enabling teams to offload repetitive work to autonomous systems while maintaining quality and personalization. Who uses AI sales agents? AI sales agents are used by sales teams to manage tasks such as lead qualification, follow-ups, meeting scheduling, and coaching. By handling repetitive activities, these agents free up reps to focus on relationship-building and closing deals, ultimately driving better outcomes for both teams and customers. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 20, 2024in Agentforce Platform, Data
Gen AI Unleased With Vector Database
18Dec

Knowledge Graphs and Vector Databases

The Role of Knowledge Graphs and Vector Databases in Retrieval-Augmented Generation (RAG) In the dynamic AI landscape, Retrieval-Augmented Generation (RAG) systems are revolutionizing data retrieval by combining artificial intelligence with external data sources to deliver contextual, relevant outputs. Two core technologies driving this innovation are Knowledge Graphs and Vector Databases. While fundamentally different in their design and functionality, these tools complement one another, unlocking new potential for solving complex data problems across industries. Understanding Knowledge Graphs: Connecting the Dots Knowledge Graphs organize data into a network of relationships, creating a structured representation of entities and how they interact. These graphs emphasize understanding and reasoning through data, offering explainable and highly contextual results. How They Work Strengths Limitations Applications Vector Databases: The Power of Similarity In contrast, Vector Databases thrive in handling unstructured data such as text, images, and audio. By representing data as high-dimensional vectors, they excel at identifying similarities, enabling semantic understanding. How They Work Strengths Limitations Applications Combining Knowledge Graphs and Vector Databases: A Hybrid Approach While both technologies excel independently, their combination can amplify RAG systems. Knowledge Graphs bring reasoning and structure, while Vector Databases offer rapid, similarity-based retrieval, creating hybrid systems that are more intelligent and versatile. Example Use Cases Knowledge Graphs vs. Vector Databases: Key Differences Feature Knowledge Graphs Vector Databases Data Type Structured Unstructured Core Strength Relational reasoning Similarity-based retrieval Explainability High Low Scalability Limited for large datasets Efficient for massive datasets Flexibility Schema-dependent Schema-free Challenges in Implementation Future Trends: The Path to Convergence As AI evolves, the distinction between Knowledge Graphs and Vector Databases is beginning to blur. Emerging trends include: This convergence is paving the way for smarter, more adaptive systems that can handle both structured and unstructured data seamlessly. Conclusion Knowledge Graphs and Vector Databases represent two foundational technologies in the realm of Retrieval-Augmented Generation. Knowledge Graphs excel at reasoning through structured relationships, while Vector Databases shine in unstructured data retrieval. By combining their strengths, organizations can create hybrid systems that offer unparalleled insights, efficiency, and scalability. In a world where data continues to grow in complexity, leveraging these complementary tools is essential. Whether building intelligent healthcare systems, enhancing recommendation engines, or powering semantic search, the synergy between Knowledge Graphs and Vector Databases is unlocking the next frontier of AI innovation, transforming how industries harness the power of their data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 18, 2024in Artificial Intelligence, Data, Enterprise, Google
Google Prepares AI-Powered Jarvis Agent
18Dec

Google Prepares AI-Powered Jarvis Agent

Google Prepares AI-Powered Jarvis Agent for Automated Browser Tasks in Chrome Google is reportedly gearing up to launch “Project Jarvis,” an AI-powered browser agent designed to automate tasks directly within the Chrome ecosystem. According to The Information, the tool is expected to roll out in December to select users and will leverage Google’s advanced Gemini 2.0 AI model. Jarvis aims to simplify repetitive online tasks, such as organizing information or booking reservations, offering a seamless and efficient digital assistant embedded within Chrome. This initiative reflects Google’s broader vision to enhance user experiences by automating web-based routines, making its browser a central hub for task automation. Anthropic Expands Desktop Automation with Claude 3.5 Sonnet Anthropic, a key player in the AI landscape, has advanced its Claude 3.5 model with a new “Computer Use” feature, enabling direct interaction with a user’s desktop. This update allows Claude to perform tasks such as typing, clicking, and managing multiple applications, making it a powerful tool for automating workflows like data entry, document management, and customer service. Available through APIs and platforms like Amazon Bedrock and Google Cloud’s Vertex AI, Claude’s new capabilities position it as a versatile solution for businesses seeking desktop-level automation, contrasting Google Jarvis’s browser-specific approach. By interpreting screen elements, Claude’s “Computer Use” mode supports broader applications beyond web tasks, offering businesses an edge in efficiency and scalability. How Google Jarvis Stands Out Unlike Anthropic’s desktop-oriented Claude Sonnet, Google Jarvis focuses on automating tasks within Chrome. Jarvis analyzes screenshots of web pages, interprets user commands, and executes actions like clicks or data entry. While still in development, Jarvis’s design suggests a future where mundane web-based tasks are seamlessly handled by AI. Powered by Google’s Gemini 2.0 language model, Jarvis is tailored for users who prioritize web-specific functions, creating a user-friendly assistant that requires no external software. This aligns with Google’s strategy to deepen integration within its ecosystem, making Chrome a more intuitive and productive environment. Microsoft’s Copilot Agents Lead Business Automation Microsoft, meanwhile, continues to enhance its Copilot AI agents, particularly within Dynamics 365. These specialized agents are designed to automate industry-specific workflows, from lead qualification in sales to financial data reconciliation. Unlike Google Jarvis or Anthropic Claude, Microsoft’s Copilot agents target enterprise users, embedding automation within business applications like Teams, Outlook, and SharePoint. With tools like Copilot Studio, organizations can customize workflows to meet specific needs, offering a level of flexibility that resonates with enterprise clients. Early adopters, including Vodafone and Cognizant, have reported significant productivity gains through these integrations. Microsoft’s efforts position Copilot as a robust partner for day-to-day operations, transforming tasks like analysis, project coordination, and document management into automated, efficient processes. Competing Visions for AI Agents As Google, Anthropic, and Microsoft refine their AI strategies, they’re carving out distinct niches in the AI agent landscape: These approaches highlight the diverse applications of AI agents, from enhancing individual user experiences to transforming business operations. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 18, 2024in Data, Enterprise, Google, Salesforce
salesforce agentforce ai powered agentic agents
16Dec

Agentforce 2.0 Unveiled

Salesforce Unveils Agentforce 2.0: Transforming Workflows with Enhanced AI Reasoning and Data Integration Salesforce has launched Agentforce 2.0, the next-generation version of its digital labor platform, offering enterprises new pre-built skills, advanced workflow integrations, and enhanced AI reasoning capabilities. Designed to create a “limitless workforce,” Agentforce 2.0 equips businesses with AI agents capable of executing complex tasks across any department, system, or workflow with improved precision and efficiency. Key Enhancements in Agentforce 2.0 1. Expanded Pre-Built Skills and IntegrationsAgentforce 2.0 introduces a robust library of pre-built agent skills compatible with Salesforce CRM, Slack, Tableau, and partner-developed tools on the AppExchange. Additionally, integrations with MuleSoft allow businesses to extend Agentforce capabilities across any system or workflow. 2. Advanced AI Reasoning and RetrievalPowered by Salesforce’s upgraded Atlas Reasoning Engine and retrieval-augmented generation (RAG) technology, the platform now handles deeply nuanced queries and multi-step tasks, leveraging enriched context from Data Cloud. 3. Enhanced Agent BuilderAgentforce’s updated Agent Builder can interpret natural language instructions—such as “onboard new team members”—to auto-generate agents and workflows. It also pulls from the expanded skill library to streamline agent creation, saving time and improving customizability. 4. Slack IntegrationSlack Actions are now embedded into Agentforce, enabling AI agents to interact directly within Slack. For example, agents can send direct messages summarizing project updates or modify Slack Canvas documents in response to customer feedback. Industry Impact and Adoption Marc Benioff, Chair and CEO of Salesforce, highlighted the transformative potential of Agentforce 2.0:“This launch takes our digital labor platform to the next level, blending AI, data, apps, and automation to reshape how businesses operate. Agentforce 2.0 empowers organizations to build a limitless workforce, delivering unprecedented levels of intelligence, customization, and efficiency.” Leading enterprises like Accenture, The Adecco Group, IBM, Finnair, and Indeed are already leveraging Agentforce to augment operations. A Growing Market for Digital Labor The release of Agentforce 2.0 responds to surging demand for agentic AI, with Salesforce closing 200 platform deals within a week and adding thousands more to its pipeline. According to CEO Marc Benioff, Salesforce plans to expand its salesforce by 2,000 workers to support adoption. “Digital labor is the new horizon for businesses,” Benioff remarked. “The way we architect, run, and staff our organizations is undergoing a fundamental transformation.” Challenges and Opportunities While the platform promises significant productivity gains, analysts warn of potential governance and security concerns. By 2028, Gartner predicts AI agent misuse could account for 25% of enterprise breaches. Salesforce emphasizes the importance of robust security measures to support adoption and mitigate risks. With over 80% of executives planning to deploy AI agents within three years (according to Capgemini), Agentforce 2.0 positions Salesforce as a leader in the evolving digital workforce space. Agentforce 2.0 is now available globally, with early adopters reporting improved scalability, efficiency, and customer satisfaction. For more information, visit the Salesforce Agentforce product page. About SalesforceSalesforce is a global leader in customer relationship management (CRM), enabling companies to connect with customers in new and innovative ways. With cutting-edge AI, data, and automation solutions, Salesforce empowers businesses to drive productivity, efficiency, and growth. For more details, visit www.salesforce.com. About TectonicWe are a niche, high quality, service-oriented US based technology services provider.We specialize in helping companies take advantage of the cross section between CRM, marketing, the use of data and analytics to shape behaviors and drive desired financial performance results. We have industry leading delivery capabilities addressing some of the most complex technology services, integrations and Salesforce implementation. Our delivery teams have over 200 certifications across a wide variety of technology services and products, including products, services and solutions serving sales, services, marketing, communities, customers, clients, operations, call centers, loyalty programs, just to name a few. In addition, we have highly skilled, cost effective off-shore delivery capabilities that allow us to provide our services at competitive, value added pricing levels. Please reach out and let us see how we can help you and your company. Tectonic is your Salesforce implementation partner. For more details, visit www.gettectonic.com. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 16, 2024in Agentforce Platform, Data, Enterprise, Mulesoft, Salesforce, Technology
MOIRAI-MoE
15Dec

MOIRAI-MoE

MOIRAI-MoE represents a groundbreaking advancement in time series forecasting by introducing a flexible, data-driven approach that addresses the limitations of traditional models. Its sparse mixture of experts architecture achieves token-level specialization, offering significant performance improvements and computational efficiency. By dynamically adapting to the unique characteristics of time series data, MOIRAI-MoE sets a new standard for foundation models, paving the way for future innovations and expanding the potential of zero-shot forecasting across diverse industries.

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December 15, 2024in Data, Salesforce, Technology

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