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Agentic AI is Here
14Jan

On Premise Gen AI

In 2025, enterprises transitioning generative AI (GenAI) into production after years of experimentation are increasingly considering on-premises deployment as a cost-effective alternative to the cloud. Since OpenAI ignited the AI revolution in late 2022, organizations have tested large language models powering GenAI services on platforms like AWS, Microsoft Azure, and Google Cloud. These experiments demonstrated GenAI’s potential to enhance business operations while exposing the substantial costs of cloud usage. To avoid difficult conversations with CFOs about escalating cloud expenses, CIOs are exploring on-premises AI as a financially viable solution. Advances in software from startups and packaged infrastructure from vendors such as HPE and Dell are making private data centers an attractive option for managing costs. A survey conducted by Menlo Ventures in late 2024 found that 47% of U.S. enterprises with at least 50 employees were developing GenAI solutions in-house. Similarly, Informa TechTarget’s Enterprise Strategy Group reported a rise in enterprises considering on-premises and public cloud equally for new applications—from 37% in 2024 to 45% in 2025. This shift is reflected in hardware sales. HPE reported a 16% revenue increase in AI systems, reaching $1.5 billion in Q4 2024. During the same period, Dell recorded a record .6 billion in AI server orders, with its sales pipeline expanding by over 50% across various customer segments. “Customers are seeking diverse AI-capable server solutions,” noted David Schmidt, senior director of Dell’s PowerEdge server line. While heavily regulated industries have traditionally relied on on-premises systems to ensure data privacy and security, broader adoption is now driven by the need for cost control. Fortune 2000 companies are leading this trend, opting for private infrastructure over the cloud due to more predictable expenses. “It’s not unusual to see cloud bills exceeding 0,000 or even million per month,” said John Annand, an analyst at Info-Tech Research Group. Global manufacturing giant Jabil primarily uses AWS for GenAI development but emphasizes ongoing cost management. “Does moving to the cloud provide a cost advantage? Sometimes it doesn’t,” said CIO May Yap. Jabil employs a continuous cloud financial optimization process to maximize efficiency. On-Premises AI: Technology and Trends Enterprises now have alternatives to cloud infrastructure, including as-a-service solutions like Dell APEX and HPE GreenLake, which offer flexible pay-per-use pricing for AI servers, storage, and networking tailored for private data centers or colocation facilities. “The high cost of cloud drives organizations to seek more predictable expenses,” said Tiffany Osias, vice president of global colocation services at Equinix. Walmart exemplifies in-house AI development, creating tools like a document summarization app for its benefits help desk and an AI assistant for corporate employees. Startups are also enabling enterprises to build AI applications with turnkey solutions. “About 80% of GenAI requirements can now be addressed with push-button solutions from startups,” said Tim Tully, partner at Menlo Ventures. Companies like Ragie (RAG-as-a-service) and Lamatic.ai (GenAI platform-as-a-service) are driving this innovation. Others, like Squid AI, integrate custom AI agents with existing enterprise infrastructure. Open-source frameworks like LangChain further empower on-premises development, offering tools for creating chatbots, virtual assistants, and intelligent search systems. Its extension, LangGraph, adds functionality for building multi-agent workflows. As enterprises develop AI applications internally, consulting services will play a pivotal role. “Companies offering guidance on effective AI tool usage and aligning them with business outcomes will thrive,” Annand said. This evolution in AI deployment highlights the growing importance of balancing technological innovation with financial sustainability. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 14, 2025in Data, Enterprise, Generative AI, Google, Technology0 Comments

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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 11, 2025in Agentic AI, Data, Enterprise, Technology0 Comments
From Chatbots to Agentic AI
04Jan

From Chatbots to Agentic AI

The transition from LLM-powered chatbots to agentic systems, or agentic AI, can be summed up by the old saying: “Less talk, more action.” Keeping up with advancements in AI can be overwhelming, especially when managing an existing business. The speed and complexity of innovation can make it feel like the first day of school all over again. This insight offers a comprehensive look at AI agents, their components, and key characteristics. The introductory section breaks down the elements that form the term “AI agent,” providing a clear definition. After establishing this foundation, we explore the evolution of LLM applications, particularly the shift from traditional chatbots to agentic systems. The goal is to understand why AI agents are becoming increasingly vital in AI development and how they differ from LLM-powered chatbots. By the end of this guide, you will have a deeper understanding of AI agents, their potential applications, and their impact on organizational workflows. For those of you with a technical background who prefer to get hands-on, click here for the best repository for AI developers and builders. What is an AI Agent? Components of AI Agents To understand the term “AI agent,” we need to examine its two main components. First, let’s consider artificial intelligence, or AI. Artificial Intelligence (AI) refers to non-biological intelligence that mimics human cognition to perform tasks traditionally requiring human intellect. Through machine learning and deep learning techniques, algorithms—especially neural networks—learn patterns from data. AI systems are used for tasks such as detection, classification, and prediction, with content generation becoming a prominent domain due to transformer-based models. These systems can match or exceed human performance in specific scenarios. The second component is “agent,” a term commonly used in both technology and human contexts. In computer science, an agent refers to a software entity with environmental awareness, able to perceive and act within its surroundings. A computational agent typically has the ability to: In human contexts, an agent is someone who acts on behalf of another person or organization, making decisions, gathering information, and facilitating interactions. They often play intermediary roles in transactions and decision-making. To define an AI agent, we combine these two perspectives: it is a computational entity with environmental awareness, capable of perceiving inputs, acting with tools, and processing information using foundation models backed by both long-term and short-term memory. Key Components and Characteristics of AI Agents From LLMs to AI Agents Now, let’s take a step back and understand how we arrived at the concept of AI agents, particularly by looking at how LLM applications have evolved. The shift from traditional chatbots to LLM-powered applications has been rapid and transformative. Form Factor Evolution of LLM Applications Traditional Chatbots to LLM-Powered Chatbots Traditional chatbots, which existed before generative AI, were simpler and relied on heuristic responses: “If this, then that.” They followed predefined rules and decision trees to generate responses. These systems had limited interactivity, with the fallback option of “Speak to a human” for complex scenarios. LLM-Powered Chatbots The release of OpenAI’s ChatGPT on November 30, 2022, marked the introduction of LLM-powered chatbots, fundamentally changing the game. These chatbots, like ChatGPT, were built on GPT-3.5, a large language model trained on massive datasets. Unlike traditional chatbots, LLM-powered systems can generate human-like responses, offering a much more flexible and intelligent interaction. However, challenges remained. LLM-powered chatbots struggled with personalization and consistency, often generating plausible but incorrect information—a phenomenon known as “hallucination.” This led to efforts in grounding LLM responses through techniques like retrieval-augmented generation (RAG). RAG Chatbots RAG is a method that combines data retrieval with LLM generation, allowing systems to access real-time or proprietary data, improving accuracy and relevance. This hybrid approach addresses the hallucination problem, ensuring more reliable outputs. LLM-Powered Chatbots to AI Agents As LLMs expanded, their abilities grew more sophisticated, incorporating advanced reasoning, multi-step planning, and the use of external tools (function calling). Tool use refers to an LLM’s ability to invoke specific functions, enabling it to perform more complex tasks. Tool-Augmented LLMs and AI Agents As LLMs became tool-augmented, the emergence of AI agents followed. These agents integrate reasoning, planning, and tool use into an autonomous, goal-driven system that can operate iteratively within a dynamic environment. Unlike traditional chatbot interfaces, AI agents leverage a broader set of tools to interact with various systems and accomplish tasks. Agentic Systems Agentic systems—computational architectures that include AI agents—embody these advanced capabilities. They can autonomously interact with systems, make decisions, and adapt to feedback, forming the foundation for more complex AI applications. Components of an AI Agent AI agents consist of several key components: Characteristics of AI Agents AI agents are defined by the following traits: Conclusion AI agents represent a significant leap from traditional chatbots, offering greater autonomy, complexity, and interactivity. However, the term “AI agent” remains fluid, with no universal industry standard. Instead, it exists on a continuum, with varying degrees of autonomy, adaptability, and proactive behavior defining agentic systems. Value and Impact of AI Agents The key benefits of AI agents lie in their ability to automate manual processes, reduce decision-making burdens, and enhance workflows in enterprise environments. By “agentifying” repetitive tasks, AI agents offer substantial productivity gains and the potential to transform how businesses operate. As AI agents evolve, their applications will only expand, driving new efficiencies and enabling organizations to leverage AI in increasingly sophisticated ways. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 4, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Generative AI, Technology0 Comments
Speed to Launch of Agentforce
03Jan

Speed to Launch of Agentforce

Agentforce isn’t just another AI platform that requires months of customization. At most customers, they quickly saw its power, launching transformative generative AI experiences in just days—no AI engineers needed. For companies with larger admin teams, the benefits can be even greater. Unlike other platforms, Agentforce places a strong emphasis on data privacy, building on the trust that Salesforce is known for, making these virtual assistants invaluable. We began with employee-facing use cases, saving our team several hours per week. Now, with Agentforce, we’re seeing even more opportunities to drive efficiencies and better serve our customers. “We’re excited to leverage Agentforce to completely overhaul recruitment and enrollment at Unity Environmental University. Instead of traditional forms or chatbots, our students will soon engage with an autonomous recruitment agent directly on our website, offering personalized support throughout the college application process.”– Dr. Melik Khoury, President & CEO, Unity Environmental University “For first-generation college students, the 1:385 coach-to-student ratio makes personalized guidance challenging. By integrating Agentforce into our platform, we’re deploying cutting-edge solutions to better support students. These agents enable our coaches to focus on high-touch, personalized experiences while handling vital tasks like sharing deadlines and answering common questions—24/7.”– Siva Kumari, CEO, College Possible “Agentforce offers organizations a unique opportunity to move beyond incremental improvements and achieve exponential ROI. By automating customer interactions, improving outcomes, and reducing costs, it integrates data, flows, and user interfaces to mitigate risks and accelerate value creation. This agent-based platform approach allows businesses to harness AI’s full potential, revolutionizing customer engagement and paving the way for exponential growth.”– Rebecca Wettemann, CEO and Principal Analyst, Valoir “Autonomous agents powered by Salesforce’s Agentforce are revolutionizing customer experiences by providing fast, accurate, and personalized support around the clock. With advanced AI making decisions and taking actions autonomously, businesses can resolve customer issues more efficiently, fostering deeper interactions and enhancing satisfaction. This innovation enables companies to reallocate human resources to more complex tasks, boosting individual productivity and scaling business growth. Agentforce is setting new standards for seamless sales, service, marketing, and commerce interactions, reinforcing its leadership in customer experience.”– Michael Fauscette, CEO and Chief Analyst, Arion Research LLC “The best way to predict the future is to invent it.” — Alan Kay, Computer Science Pioneer Technology progresses in what biologists call punctuated equilibrium, with new capabilities slowly emerging from labs and tinkerers until a breakthrough shifts the axis of possibility. These pioneering feats create new paradigms, unleashing waves of innovation—much like the Apple Macintosh, the iPhone, and the Salesforce Platform, which revolutionized the enterprise software-as-a-service (SaaS) model and sparked an entire industry. The Age of Agentforce Begins At Dreamforce 2024, Salesforce Futures reflected on the launch of Agentforce, inspired by visions like the Apple Knowledge Navigator. In 2023, we used this inspiration to craft our Salesforce 2030 film, which showcased the collaboration between humans and autonomous AI agents. Now, with Agentforce, we’re witnessing that vision come to life. Agentforce is a suite of customizable AI agents and tools built on the Salesforce Platform, offering an elegant solution to the complexity of AI deployment. It addresses the challenges of integrating data, models, infrastructure, and applications into a unified system. With powerful tools like Agent Builder and Model Builder, organizations can easily create, customize, and deploy AI agents. Salesforce’s Atlas Reasoning Engine empowers these agents to handle both routine and complex tasks autonomously. A New Era of AI Innovation At Dreamforce 2024, over 10,000 attendees raced to build their own agents using the “Agent Builder” experience, turning verbal instructions into fully functioning agents in under 15 minutes. This wasn’t just another chatbot—it’s a new breed of AI that could transform how businesses operate and deliver superior customer experiences. Companies like Saks, OpenTable, and Wiley have quickly embraced this technology. As Mick Costigan and David Berthy of Salesforce Futures explain, “When we see signals like this, it pushes us toward the future. Soon, we’ll see complex, multi-agent systems solving higher-order challenges, both in the enterprise and in consumer devices.” Shaping the Future Agentforce isn’t just a product—it’s a platform for experimentation. With hundreds of thousands of Salesforce customers soon gaining access, the full potential of these tools will unfold in ways we can’t yet imagine. As with every major technological shift, the real magic will lie in how people use it. Every interaction, every agent deployed, and every problem solved will shape the future in unexpected ways. Platform Evolution Adam Evans, Salesforce SVP of Product, notes that Agentforce builds on the company’s transformation over the past four years, following the pattern of Salesforce’s original disruption of enterprise software. Unlike traditional solutions, Agentforce eliminates the need for customers to build their own AI infrastructure, providing a ready-to-use solution. At the core of Agentforce is the Atlas Reasoning Engine, delivering results that are twice as relevant and 33% more accurate than competing solutions. This engine integrates Salesforce Data Cloud, Flow for automation, and the Einstein Trust Layer for governance. Early Customer Results Early Agentforce deployments highlight how organizations are using autonomous agents to enhance, rather than replace, human workers: George Pokorny, Senior VP of Global Customer Success at OpenTable, shared, “Just saving two minutes on a ten-minute call lets our service reps focus on strengthening customer relationships, thanks to seamless integration with Service Cloud, giving us a unified view of diner preferences and history.” 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 3, 2025in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce, Salesforce Data Cloud, Technology0 Comments
ai in supply chain
31Dec

AI in Supply Chain

AI has the potential to transform supply chain management by boosting efficiency, enhancing forecasting, and improving customer satisfaction. However, it’s crucial for leaders to weigh the associated costs, technical challenges, and risks to ensure a smooth and strategic implementation. By addressing these disadvantages, companies can fully unlock AI’s benefits while minimizing its drawbacks.

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December 31, 2024in Data, Technology0 Comments
AI Agents Set to Break Through in 2025
20Dec

AI Agents Set to Break Through in 2025

2025: The Year AI Agents Transform Work and Life Despite years of hype around artificial intelligence, its true disruptive impact has so far been limited. However, industry experts believe that’s about to change in 2025 as autonomous AI agents prepare to enter and reshape nearly every facet of our lives. Since OpenAI’s ChatGPT took the world by storm in late 2022, billions of dollars have been funneled into the AI sector. Big tech and startups alike are racing to harness the transformative potential of the technology. Yet, while millions now interact with AI chatbots daily, turning them into tools that deliver tangible business value has proven challenging. A recent study by Boston Consulting Group revealed that only 26% of companies experimenting with AI have progressed beyond proof of concept to derive measurable value. This lag reflects the limitations of current AI tools, which serve primarily as copilots—capable of assisting but requiring constant oversight and remaining prone to errors. AI Agents Set to Break Through in 2025 The status quo, however, is poised for a radical shift. Autonomous AI agents—capable of independently analyzing information, making decisions, and taking action—are expected to emerge as the industry’s next big breakthrough. “For the first time, technology isn’t just offering tools for humans to do work,” Salesforce CEO Marc Benioff wrote in Time. “It’s providing intelligent, scalable digital labor that performs tasks autonomously. Instead of waiting for human input, agents can analyze information, make decisions, and adapt as they go.” At their core, AI agents leverage the same large language models (LLMs) that power tools like ChatGPT. But these agents take it further, acting as reasoning engines that develop step-by-step strategies to execute tasks. Armed with access to external data sources like customer records or financial databases and equipped with software tools, agents can achieve goals independently. While current LLMs still face reasoning limitations, advancements are on the horizon. New models like OpenAI’s “o1” and DeepSeek’s “R1” are specialized for reasoning, sparking hope that 2025 will see agents grow far more capable. Big Tech and Startups Betting Big Major players are already gearing up for this new era. Startups are also eager to carve out their share of the market. According to Pitchbook, funding deals for agent-focused ventures surged by over 80% in 2024, with the median deal value increasing nearly 50%. Challenges to Overcome Despite the enthusiasm, significant hurdles remain. 2025: A Turning Point Despite these challenges, many experts believe 2025 will mark the mainstream adoption of AI agents. A New World of Work No matter the pace, it’s clear that AI agents will dominate the industry’s focus in 2025. If the technology delivers on its promise, the workplace could undergo a profound transformation, enabling entirely new ways of working and automating tasks that once required human intervention. The question isn’t if agents will redefine the way we work—it’s how fast. By the end of 2025, the shift could be undeniable. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 20, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Salesforce, Technology0 Comments
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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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December 18, 2024in Artificial Intelligence, Data, Enterprise, Google0 Comments
AI Agents and Consumer Trust
13Dec

AI Agents Next AI Evolution

AI agents are being hailed as the next big leap in artificial intelligence, but there’s no universally accepted definition of what they are—or what they should do. Even within the tech community, there’s debate about what constitutes an AI agent. At its core, an AI agent can be described as software powered by artificial intelligence that performs tasks once handled by human roles, such as customer service agents, HR representatives, or IT help desk staff. However, their potential spans much further. These agents don’t just answer questions—they take action, often working across multiple systems. For example, Perplexity recently launched an AI agent to assist with holiday shopping, while Google introduced Project Mariner, an agent that helps users book flights, find recipes, and shop for household items. While the idea seems straightforward, it’s muddied by inconsistent definitions. For Google, AI agents are task-based assistants tailored to specific roles, like coding help for developers or troubleshooting issues for IT professionals. In contrast, Asana views agents as digital co-workers that take on assigned tasks, and Sierra—a startup led by former Salesforce co-CEO Bret Taylor—envisions agents as sophisticated customer experience tools that surpass traditional chatbots by tackling complex problems. This lack of consensus adds to the uncertainty around what AI agents can truly achieve. Rudina Seseri, founder and managing partner at Glasswing Ventures, explains this ambiguity stems from the technology’s infancy. She describes AI agents as intelligent systems capable of perceiving their environment, reasoning, making decisions, and taking actions to achieve specific goals autonomously. These agents rely on a mix of AI technologies, including natural language processing, machine learning, and computer vision, to operate in dynamic environments. Optimists, like Box CEO Aaron Levie, believe AI agents will improve rapidly as advancements in GPU performance, model efficiency, and AI frameworks create a self-reinforcing cycle of innovation. However, skeptics like MIT robotics pioneer Rodney Brooks caution against overestimating progress, noting that solving real-world problems—especially those involving legacy systems with limited API access—can be far more challenging than anticipated. David Cushman of HFS Research likens current AI agents to assistants rather than fully autonomous entities, with their capabilities limited to helping users complete specific tasks within pre-defined boundaries. True autonomy, where AI agents handle contingencies and perform at scale without human oversight, remains a distant goal. Jon Turow, a partner at Madrona Ventures, emphasizes the need for dedicated infrastructure to support the development of AI agents. He envisions a tech stack that allows developers to focus on product differentiation while leaving scalability and reliability to the platform. This infrastructure would likely involve multiple specialized models working together under a routing layer, rather than relying on a single large language model (LLM). Fred Havemeyer of Macquarie US Equity Research agrees, noting that the most effective AI agents will combine various models to handle complex tasks. He imagines a future where agents act like autonomous supervisors, delegating tasks and reasoning through multi-step processes to achieve abstract goals. While this vision is compelling, the current state of AI agents suggests we’re still in a transitional phase. The progress so far is promising, but several breakthroughs are needed before agents can operate as envisioned—truly autonomous, multi-functional, and capable of seamless collaboration across diverse systems. This story, originally published on July 13, 2024, has been updated to reflect new developments from Perplexity and Google. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 13, 2024in Artificial Intelligence, Google, Salesforce, Technology0 Comments
salesforce government digital transformation
11Dec

Salesforce Drives Digital Transformation in Governmental Agencies

How Salesforce Drives Digital Transformation in Governmental Agencies in 2025 In the evolving digital age, government agencies face an increasing demand to modernize their services, improve citizen engagement, and deliver seamless digital experiences. These organizations require transformational technologies that not only streamline internal operations but also adopt a citizen-first approach. Salesforce emerges as a key enabler of this transformation, empowering government agencies with tools to build unified, transparent platforms while fostering efficiency and enhancing citizen interaction. Leveraging Salesforce Commerce Cloud and Salesforce CRM, agencies can overcome common challenges and embrace a more digitally enabled public sector. Let’s explore the pressing challenges government agencies face and how Salesforce provides practical, scalable solutions to address them. 1. Citizen Engagement and Accessibility: Bridging the Digital Divide Challenge: Citizens now expect government services to be as user-friendly and accessible as private-sector experiences. Lengthy response times, disconnected platforms, and inconsistent experiences across digital and physical touchpoints erode trust and hinder accessibility. Solution: 2. Data Security and Compliance: Safeguarding Citizen Trust Challenge: Handling sensitive citizen data requires robust security and strict compliance with regulations like GDPR, CCPA, and other local data privacy laws. Solution: 3. Legacy Systems and Integration: Modernizing Infrastructure Challenge: Legacy systems often limit agility, making it difficult to integrate new technologies and slowing the pace of digital transformation. Solution: 4. Budget Constraints: Implementing Cost-Effective Solutions Challenge: Budget limitations often hinder the adoption of new technologies, especially those requiring significant upfront investment. Solution: 5. Efficient Service Delivery: Streamlining Workflows Challenge: Paper-heavy, bureaucratic processes delay service delivery and frustrate both staff and citizens. Solution: 6. Data-Driven Decision-Making: Analytics for Informed Policies Challenge: Generating actionable insights from vast amounts of data is challenging, affecting policymaking and government efficiency. Solution: 7. Enhancing Collaboration: A Unified Workforce Challenge: Siloed departments hinder collaboration and reduce overall productivity, making it difficult to provide cohesive citizen services. Solution: 8. Real-Time Responsiveness: Meeting Citizen Expectations Challenge: Citizens expect real-time support and proactive communication from government agencies. Delays lead to frustration and diminished trust. Solution: Transforming Government Services with Salesforce Salesforce Commerce Cloud and Salesforce CRM are tailored to address public sector challenges in 2025. By leveraging these tools, government agencies can: Salesforce offers a clear path to a digitally empowered future, enabling government agencies to meet today’s demands while laying the foundation for innovation. Ready to Transform?If your agency is ready to embrace digital transformation, streamline operations, and enhance citizen services, Salesforce can help you get there. Let’s discuss how Salesforce solutions, supported by expert implementation, can drive meaningful change for your organization and your citizens. Like1 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 11, 2024in Data, Laws, Mulesoft, Public Sector Solutions, Salesforce, Salesforce Commerce Cloud, Salesforce Government Cloud, Salesforce Service Cloud, Salesforce Shield0 Comments
07Dec

Ushering in the Era of AI-Driven Workforces

Agentforce: Ushering in the Era of AI-Driven Workforces “Agentforce is redefining what’s possible in business and beyond, ushering in a new era of AI abundance and limitless workforces that augment every employee, build deeper customer relationships, and drive unprecedented growth and profitability.”— Marc Benioff, Salesforce CEO Key Features and Takeaways Out-of-the-Box AI Agents Agentforce introduces pre-built, customizable agents that are deployable with low-code or no-code tools, working tirelessly across any channel. Its first generally available solution, the Agentforce Service Agent, surpasses traditional chatbots by handling a diverse range of tasks—from simple inquiries to complex scenarios. Key capabilities include: Seamless Integration with Salesforce Platform Agentforce eliminates the complexity of building AI solutions from scratch. Unlike other platforms that demand intricate data integration and custom automation, Agentforce is fully embedded within the Salesforce ecosystem. With Agentforce, businesses can: Always-On Automation Agentforce operates independently of human intervention. Agents can be triggered by changes in data, predefined business rules, or pre-built automations, ensuring uninterrupted workflows. This blend of autonomous operation and human collaboration creates a symbiotic relationship between people and AI, enhancing productivity and customer satisfaction. A Competitive Edge in the AI Space Salesforce emphasizes that Agentforce goes beyond chatbots and copilots, setting a new benchmark for enterprise AI. In a strategic swipe at competitors, Marc Benioff likened rival offerings—like Microsoft’s Dynamics 365 AI agents—to “Clippy 2.0,” critiquing their inaccuracies and risks of corporate data leaks. By contrast, Agentforce builds on Salesforce’s proven Einstein AI platform, ensuring reliability, scalability, and secure integration. Redefining Customer Success Agentforce isn’t just about automation—it’s about driving outcomes. By enabling businesses to automate complex processes, deepen customer relationships, and scale operations, it paves the way for limitless growth in the age of AI. Ready to transform your workforce? With Agentforce, the future of AI-driven business is already here. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 7, 2024in Agentforce Platform, Data, Enterprise, Mulesoft, Salesforce
Google Gemini 2.0
04Dec

Google Gemini 2.0

Google Gemini 2.0 Flash: A First Look Google has unveiled an experimental version of Gemini 2.0 Flash, its next-generation large language model (LLM), now accessible to developers via Google AI Studio and the Gemini API. This model builds on the capabilities of its predecessors with improved multimodal features and enhanced support for agentic workflows, positioning it as a major step forward in AI-driven applications. Key Features of Gemini 2.0 Flash Performance and Efficiency According to Google, Gemini 2.0 Flash is twice as fast as Gemini 1.5 while outperforming it on standard benchmarks for AI accuracy. Its efficiency and size make it particularly appealing for real-world applications, as highlighted by David Strauss, CTO of Pantheon: “The emphasis on their Flash model, which is efficient and fast, stands out. Frontier models are great for testing limits but inefficient to run at scale.” Applications and Use Cases Agentic AI and Competitive Edge Gemini 2.0’s standout feature is its agentic AI capabilities, where multiple AI agents collaborate to execute multi-stage workflows. Unlike simpler solutions that link multiple chatbots, Gemini 2.0’s tool-driven, code-based training sets it apart. Chirag Dekate, an analyst at Gartner, notes: “There is a lot of agent-washing in the industry today. Gemini now raises the bar on frontier models that enable native multimodality, extremely large context, and multistage workflow capabilities.” However, challenges remain. As AI systems grow more complex, concerns about security, accuracy, and trust persist. Developers, like Strauss, emphasize the need for human oversight in professional applications: “I would trust an agentic system that formulates prompts into proposed, structured actions, subject to review and approval.” Next Steps and Roadmap Google has not disclosed pricing for Gemini 2.0 Flash, though its free availability is anticipated if it follows the Gemini 1.5 rollout. Looking ahead, Google plans to incorporate the model into its beta-stage AI agents, such as Project Astra, Mariner, and Jules, by 2025. Conclusion With Gemini 2.0 Flash, Google is pushing the boundaries of multimodal and agentic AI. By introducing native tool usage and support for complex workflows, this LLM offers developers a versatile and efficient platform for innovation. As enterprises explore the model’s capabilities, its potential to reshape AI-driven applications in coding, data science, and interactive interfaces is immense—though trust and security considerations remain critical for broader adoption. 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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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December 4, 2024in Agentic AI, Data, Enterprise, Google
MoEngage Unveils New Tools to Help Marketers Adapt to Consumer Trends
03Dec

MoEngage Unveils New Tools to Help Marketers Adapt to Consumer Trends

MoEngage, a leading cross-channel customer engagement platform, has launched new features designed to help marketers quickly adapt to shifting consumer behaviors. These updates, introduced at the bi-annual MoEngage NEXT event, include Connected Apps for seamless data integration, a Salesforce CRM integration for streamlined data exchange, and Coupons for managing single-use discounts. “Our new capabilities reinforce our commitment to empowering marketers with tools to understand and adapt to evolving consumer expectations,” said Raviteja Dodda, CEO and Co-Founder of MoEngage. “These innovations enable our clients to scale personalized engagement based on individual preferences and behaviors.” Tackling Fragmented Engagement Tools Marketers often struggle to deliver personalized experiences due to disconnected engagement tools and data silos. To bridge this gap, MoEngage introduced Connected Apps, a low-code framework that integrates data across messaging platforms, advertising channels, IVR systems, data warehouses, and chatbots. Enhanced Integration with Salesforce CRM The new bi-directional native integration with Salesforce CRM simplifies data exchange between the two platforms. Marketers can now trigger real-time personalized campaigns without needing costly custom integrations. This integration not only improves efficiency but also reduces operational costs. Streamlining Coupon Management To enhance customer engagement, MoEngage launched Coupons, a feature that helps marketers allocate and manage single-use discount codes from a centralized dashboard. The tool includes real-time updates on coupon status, alerts for shortages and expiration dates, and ingestion tracking, ensuring smooth campaign execution while optimizing costs. Driving Scalable and Personalized Engagement With these innovations, MoEngage continues to solidify its position as a go-to platform for marketers seeking to adapt quickly to consumer trends. By addressing common pain points like data fragmentation and inefficient tools, MoEngage enables marketers to deliver meaningful, personalized customer experiences at scale. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 3, 2024in Data, Salesforce, Third Party Apps

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