An Agentforce Agent is a proactive, autonomous application that delivers specialized, always-on support to employees or customers. Each agent is equipped with the business knowledge needed to execute tasks based on its role.
LNNs mark a significant departure from traditional, rigid AI structures, drawing deeply from the adaptable nature of biological neural systems. MIT researchers explored how organisms manage complex decision-making and dynamic responses with minimal neurons, translating these principles into the design of LNNs
Throughout history, disruptive technologies have been the catalyst for major social and economic revolutions. The invention of the plow and irrigation systems 12,000 years ago sparked the Agricultural Revolution, while Johannes Gutenberg’s 15th-century printing press fueled the Protestant Reformation and helped propel Europe out of the Middle Ages into the Renaissance. In the 18th century, James Watt’s steam engine ushered in the Industrial Revolution. More recently, the internet has revolutionized communication, commerce, and information access, shrinking the world into a global village. Similarly, smartphones have transformed how people interact with their surroundings. Now, we stand at the dawn of the AI revolution. Large Language Models (LLMs) represent a monumental leap forward, with significant economic implications at both macro and micro levels. These models are reshaping global markets, driving new forms of currency, and creating a novel economic landscape. The reason LLMs are transforming industries and redefining economies is simple: they automate both routine and complex tasks that traditionally require human intelligence. They enhance decision-making processes, boost productivity, and facilitate cost reductions across various sectors. This enables organizations to allocate human resources toward more creative and strategic endeavors, resulting in the development of new products and services. From healthcare to finance to customer service, LLMs are creating new markets and driving AI-driven services like content generation and conversational assistants into the mainstream. To truly grasp the engine driving this new global economy, it’s essential to understand the inner workings of this disruptive technology. These posts will provide both a macro-level overview of the economic forces at play and a deep dive into the technical mechanics of LLMs, equipping you with a comprehensive understanding of the revolution happening now. Why Now? The Connection Between Language and Human Intelligence AI did not begin with ChatGPT’s arrival in November 2022. Many people were developing machine learning classification models in 1999, and the roots of AI go back even further. Artificial Intelligence was formally born in 1950, when Alan Turing—considered the father of theoretical computer science and famed for cracking the Nazi Enigma code during World War II—created the first formal definition of intelligence. This definition, known as the Turing Test, demonstrated the potential for machines to exhibit human-like intelligence through natural language conversations. The test involves a human evaluator who engages in conversations with both a human and a machine. If the evaluator cannot reliably distinguish between the two, the machine is considered to have passed the test. Remarkably, after 72 years of gradual AI development, ChatGPT simulated this very interaction, passing the Turing Test and igniting the current AI explosion. But why is language so closely tied to human intelligence, rather than, for example, vision? While 70% of our brain’s neurons are devoted to vision, OpenAI’s pioneering image generation model, DALL-E, did not trigger the same level of excitement as ChatGPT. The answer lies in the profound role language has played in human evolution. The Evolution of Language The development of language was the turning point in humanity’s rise to dominance on Earth. As Yuval Noah Harari points out in his book Sapiens: A Brief History of Humankind, it was the ability to gossip and discuss abstract concepts that set humans apart from other species. Complex communication, such as gossip, requires a shared, sophisticated language. Human language evolved from primitive cave signs to structured alphabets, which, along with grammar rules, created languages capable of expressing thousands of words. In today’s digital age, language has further evolved with the inclusion of emojis, and now with the advent of GenAI, tokens have become the latest cornerstone in this progression. These shifts highlight the extraordinary journey of human language, from simple symbols to intricate digital representations. In the next post, we will explore the intricacies of LLMs, focusing specifically on tokens. But before that, let’s delve into the economic forces shaping the LLM-driven world. The Forces Shaping the LLM Economy AI Giants in Competition Karl Marx and Friedrich Engels argued that those who control the means of production hold power. The tech giants of today understand that AI is the future means of production, and the race to dominate the LLM market is well underway. This competition is fierce, with industry leaders like OpenAI, Google, Microsoft, and Facebook battling for supremacy. New challengers such as Mistral (France), AI21 (Israel), and Elon Musk’s xAI and Anthropic are also entering the fray. The LLM industry is expanding exponentially, with billions of dollars of investment pouring in. For example, Anthropic has raised $4.5 billion from 43 investors, including major players like Amazon, Google, and Microsoft. The Scarcity of GPUs Just as Bitcoin mining requires vast computational resources, training LLMs demands immense computing power, driving a search for new energy sources. Microsoft’s recent investment in nuclear energy underscores this urgency. At the heart of LLM technology are Graphics Processing Units (GPUs), essential for powering deep neural networks. These GPUs have become scarce and expensive, adding to the competitive tension. Tokens: The New Currency of the LLM Economy Tokens are the currency driving the emerging AI economy. Just as money facilitates transactions in traditional markets, tokens are the foundation of LLM economics. But what exactly are tokens? Tokens are the basic units of text that LLMs process. They can be single characters, parts of words, or entire words. For example, the word “Oscar” might be split into two tokens, “os” and “car.” The performance of LLMs—quality, speed, and cost—hinges on how efficiently they generate these tokens. LLM providers price their services based on token usage, with different rates for input (prompt) and output (completion) tokens. As companies rely more on LLMs, especially for complex tasks like agentic applications, token usage will significantly impact operational costs. With fierce competition and the rise of open-source models like Llama-3.1, the cost of tokens is rapidly decreasing. For instance, OpenAI reduced its GPT-4 pricing by about 80% over the past year and a half. This trend enables companies to expand their portfolio of AI-powered products, further fueling the LLM economy. Context Windows: Expanding Capabilities
Setting Up Gamification in Salesforce Experience Cloud to Boost Engagement When someone mentions “gamification,” many think of “games,” “fun,” and “entertainment.” While this is true, in the context of Salesforce, it takes on new dimensions. Here, it’s not just about fun; it’s about enhancing user engagement, productivity, and overall experience. Keep reading as we explore the intricacies of implementing gamification in Salesforce Experience Cloud and how you can leverage this game-changing experience for your organization (pun intended). Gamification, Fully Explained Gamification employs game-like mechanics to motivate users while they interact with your website, application, or service through engaging content. The essence of gamification lies in rewarding users with points and badges for completing specific actions. Examples include: A prime example of gamification in Salesforce is Trailhead, where users earn badges and points for completing various trails and modules. As a proud Triple Star Ranger with 566 badges, 162,075 points, and 89 trails completed, I’m a trailblazing fool. Time to put in the work! Using Gamification in Salesforce Experience Cloud: Common Benefits When implemented correctly, gamification can significantly enhance user engagement and experience. Here are some common advantages of using gamification in Salesforce Experience Cloud: Main Gamification Functionality in Salesforce Gamification in Salesforce Experience Cloud revolves around three key pillars: Recognition Badges, Missions, and Reputation Leaderboards. Before exploring the setup, let’s understand these key elements: How to Set Gamification Up in Salesforce Experience Cloud: Your Step-by-Step Tutorial Now that we’ve covered the basics, let’s walk through the process of implementing gamification in a Salesforce Experience Cloud site. Follow these simple steps—it’s straightforward! Step 1: Locating Gamification in the Experience Builder Step 2: Turning the Thanks Settings On Step 3: Creating a Recognition Badge Step 4: Creating a Mission Badge Step 5: Enabling Reputation on an Experience Cloud Site Step 6: Adjusting Reputation Levels and Points Step 7: Assembling Gamification Components on the Site’s Layout Step 8: Enjoying Gamification from a User’s Perspective Final Thoughts Implementing gamification in Salesforce Experience Cloud is straightforward. While it involves several steps, the benefits are well worth the effort. A couple of tips as you embark on your gamification journey: 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
Agentforce Salesforce Agentforce in Action: A Practical Example of Using Agents in Salesforce Autonomous Agents on the Agentforce Platform Agentforce represents a transformative shift in Salesforce’s strategy, poised to redefine how users engage with their CRM. By introducing both assistive AI—enhanced by generative AI for capabilities like summaries and sales emails—and autonomous AI, which empowers agents to automate actions without human oversight, Agentforce helps users operate more efficiently in Salesforce. Despite the excitement around Agentforce, most blogs and marketing materials focus on AI hype rather than practical applications. This insight focuses on illustrating how these tools work and the tangible value they can provide for your organization’s custom processes. Curious about setting up Agentforce agents using both out-of-the-box actions and custom actions? Let’s dive in. What is Agentforce? Agentforce is Salesforce’s conversational AI tool for CRM. In simple terms, it lets users “talk” to Salesforce. Powered by generative AI and the Atlas Reasoning Engine, Agentforce processes user input to perform tasks like summarizing data from objects, updating fields, and generating content such as emails or knowledge articles. This innovative tool is only at the beginning of its journey, likely setting the stage for a future where CRM interactions may evolve beyond traditional form-based interfaces to more intuitive chatbot-style engagement. Scenario: Managing Sales Pipeline Consider a salesperson with the daily objectives of tracking deals, managing pipeline opportunities, and identifying potential risks. Traditionally, this would require manually navigating numerous Salesforce objects, risking data inconsistencies and user errors. Agentforce’s assistive actions can streamline much of this, automating processes to identify key deals, summarize progress, and track deal risks across the pipeline. Let’s take a closer look at configuring a custom action for a pipeline summary. All powered by Salesforce Agentforce. Step-by-Step Guide to Configuring a Pipeline Summary Action Agentforce Use Cases: Getting Started Agentforce offers powerful tools for implementing AI-based functions within Salesforce, but to realize productivity gains, consider the following: Agentforce’s standard actions are a great starting point, providing immediate productivity impacts that can be enhanced as you customize actions to meet specific needs. For tailored guidance on integrating Agentforce, explore Tectonic’s Salesforce Agentforce Consulting Services. Tectonic’s expertise can support your organization in optimizing user experience, boosting productivity, and training users to responsibly leverage Agentforce’s capabilities across industries and channels. 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
AI’s Impact on Future Information Ecosystems The proliferation of generative AI technology has ignited a renewed focus within the media industry on how to strategically adapt to its capabilities. Media professionals are now confronted with crucial questions: What are the most effective ways to leverage this technology for efficiency in news production and to enhance audience experiences? Conversely, what threats do these technological advancements pose? Is legacy media on the brink of yet another wave of disintermediation from its audiences? Additionally, how does the evolution of technology impact journalism ethics? AI’s Impact on Future Information Ecosystems. In response to these challenges, the Open Society Foundations (OSF) launched the AI in Journalism Futures project earlier this year. The first phase of this ambitious initiative involved an open call for participants to develop future-oriented scenarios that explore the potential driving forces and implications of AI within the broader media ecosystem. The project sought to answer questions about what might transpire among various stakeholders in 5, 10, or 15 years. As highlighted by Nick Diakopoulos, scenarios are a valuable method for capturing a diverse range of perspectives on complex issues. While predicting the future is not the goal, understanding a variety of plausible alternatives can significantly inform current strategic thinking. Ultimately, more than 800 individuals from approximately 70 countries contributed short scenarios for analysis. The AI in Journalism Futures project subsequently utilized these scenarios as a foundation for a workshop, which refined the ideas outlined in their report. Diakopoulos emphasizes the importance of examining this broad set of initial scenarios, which OSF graciously provided in anonymized form. This analysis specifically explores (1) the various types of impacts identified within the scenarios, (2) the associated timeframes for these impacts—whether they are short, medium, or long-term, and (3) the global differences in focus across regions, highlighting how different parts of the world emphasized distinct types of impacts. While many additional questions could be explored regarding this data—such as the drivers of impacts, final outcomes, severity, stakeholders involved, or technical capabilities emphasized—this analysis focuses primarily on impacts. Refining the Data The initial pool of 872 scenarios underwent a rigorous process of cleaning, filtering, transformation, and verification before analysis. Firstly, scenarios shorter than 50 words were excluded from consideration, resulting in 852 scenarios for analysis. Additionally, 14 scenarios that were not written in English were translated using Google Sheets. To enable geographic and temporal analysis, the country of origin for each scenario writer was mapped to their respective continents, and the free-text “timeframe” field was converted into numerical representations of years. Next, impacts were extracted from each scenario using an LLM (GPT-4 in this case). The prompts for the LLM were refined through iteration, with a clear definition established for what constitutes an “impact.” Diakopoulos defined an impact as “a significant effect, consequence, or outcome that an action, event, or other factor has in the scenario.” This definition encompasses not only the ultimate state of a scenario but also intermediate outcomes. The LLM was instructed to extract distinct impacts, with each impact represented by a one-sentence description and a short label. For instance, one impact could be described as, “The proliferation of flawed AI systems leads to a compromised information ecosystem, causing a general doubt in the reliability of all information,” labeled as “Compromised Information Ecosystem.” To ensure the accuracy of this extraction process, a random sample of five scenarios was manually reviewed to validate the extracted impacts against the established definition. All extracted impacts passed the checks, leading to confidence in scaling the analysis across the entire dataset. This process resulted in the identification of 3,445 impacts from the 852 scenarios. AI’s Impact on Future Information Ecosystems A typology of impact types was developed based on the 3,445 impact descriptions, utilizing a novel method for qualitative thematic analysis from a Stanford University study. This approach clusters input texts, synthesizes concepts that reflect abstract connections, and produces scoring definitions to assess the relevance of each original text. For example, a concept like “AI Personalization” might be defined by the question, “Does the text discuss how AI personalizes content or enhances user engagement?” Each impact description was then scored against these concepts to tabulate occurrence frequencies. Impacts of AI on Media Ecosystems Through this analytical approach, 19 impact themes emerged, along with their corresponding scoring definitions: Interestingly, many scenarios articulated themes around how AI intersects with fact-checking, trust, misinformation, ethics, labor concerns, and evolving business models. Although some concepts may not be entirely distinct, this categorization offers a meaningful overview of the key ideas represented in the data. Distribution of Impact Themes Comparing these findings with those in the OSF report reveals some discrepancies. For instance, while the report emphasizes personalization and misinformation, these themes were less prevalent in the analyzed scenarios. Moreover, themes such as the rise of AI agents and audience fragmentation were mentioned but did not cluster significantly in the analysis. To capture potentially interesting but less prevalent impacts, the clustering was rerun with a smaller minimum cluster size. This adjustment yielded hundreds more concept themes, revealing insights into longer-tail issues. Positive visions for generative AI included reduced language barriers and increased accessibility for marginalized audiences, while concerns about societal fragmentation and privacy were also raised. Impacts Over Time and Around the World The analysis also explored how the impacts varied based on the timeframe selected by writers and their geographic locations. Using a Chi-Squared test, it was determined that “AI Personalization” trends towards long-term implications, while both “AI Fact-Checking” and “AI and Misinformation” skew toward shorter-term issues. This suggests that scenario writers perceive misinformation impacts as imminent threats, likely reflecting ongoing developments in the media landscape. When examining the distribution of impacts by region, it was found that “AI Fact-Checking” was more frequently noted by writers from Africa and Asia, while “AI and Misinformation” was less prevalent in scenarios from African writers but more so in those from Asian contributors. This indicates a divergence in perspectives on AI’s role in the media ecosystem.
Salesforce Report Highlights AI Agents as Tools to Rebuild Consumer Trust For businesses of any size, the to-do list never ends. Monitoring customers, understanding their needs, and delivering products and services that align with their expectations are critical. Salesforce’s latest research, however, points to a troubling trend: consumer trust is at an all-time low. Yet, the report, State of the AI Connected Customer, also suggests that AI—particularly agentic AI—could help reverse this decline. Trust in Decline The key finding of the Salesforce report is stark: consumer trust in companies has taken a significant hit. Among 15,015 surveyed consumers, 72% say they trust companies less today than they did a year ago. Compounding this is the rapid advancement of AI; 60% of respondents believe that the rise of AI increases the importance of businesses being trustworthy. One major culprit behind eroding trust is the perceived mishandling of customer data. A staggering 65% of respondents feel companies are careless with data, adding to the skepticism. While high prices remain the top reason customers abandon brands, 43% pointed to poor customer service as a major deterrent. Can AI Agents Fill the Gap? The Salesforce report suggests that AI agents—when deployed transparently—could address many of the factors driving distrust and disengagement. Younger consumers, particularly Gen Z and millennials, appear more open to interacting with AI agents. Notable insights from the research include: However, trust is non-negotiable. Transparency is a critical factor for AI adoption: As Michael Affronti, SVP and General Manager of Salesforce Commerce Cloud, explains: “AI agents can help brands deliver consistent, personalized experiences for shoppers across every channel — deepening customer loyalty and ultimately driving more sales.” Building Trust Through Transparency The research underscores the potential for AI to transform customer interactions, but it also highlights the challenges. Transparency and accountability are essential for AI systems to inspire confidence and loyalty. Salesforce’s AI solutions are designed to prioritize transparency and foster reliable consumer experiences. Features such as clear agent identification and robust escalation paths are steps in the right direction. However, companies must double down on governance frameworks and safeguards to ensure AI agents handle data responsibly. Final Thoughts While the idea of using AI to rebuild consumer trust is promising, it’s not without its challenges. Establishing trust in AI itself remains a work in progress. Consumers expect companies to prioritize not only innovation but also ethics, security, and accountability. The Salesforce report demonstrates that younger consumers are already embracing AI as a way to address today’s service expectations. For Salesforce and other companies leveraging agentic AI, the key to success will lie in balancing cutting-edge technology with meaningful protections for customer data and experiences. The future of AI-driven customer engagement isn’t just about meeting expectations—it’s about exceeding them in a way that inspires confidence and loyalty. With the right approach, AI agents could be a vital tool for restoring consumer trust in an era where skepticism runs high. 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
As artificial intelligence gains momentum in healthcare, it’s critical for health systems and related stakeholders to develop robust AI governance programs. AI’s potential to address challenges in administration, operations, and clinical care is drawing interest across the sector. As this technology evolves, the range of applications in healthcare will only broaden.
Nurture marketing significantly increases the chances that your ideal buyers will consider your products or services when they’re ready to purchase.
Healthcare IT advancements, especially electronic health records (EHRs), have made it easier to gather and store data, which, in turn, fuels population health initiatives and improves patient outcomes. The Agency for Healthcare Research and Quality highlights that using health IT tools can significantly enhance chronic disease management by promoting efficient care delivery, information-sharing, and patient education. However, selecting and adopting the right analytics tools remains challenging. Here are five essential data analytics tools that healthcare providers can leverage for effective chronic disease management.
The Most Commonly Requested Salesforce Customizations Salesforce’s flexibility is one of its biggest strengths, allowing businesses to tailor the platform to meet their unique needs. Here are the most frequently requested types of customizations: 1. Declarative Customization Make adjustments using Salesforce’s built-in tools—no coding required. Examples: Ideal For:Businesses looking for straightforward changes to enhance usability without requiring programming expertise. 2. Integration Customization Connect Salesforce with third-party systems to streamline workflows and centralize data. Examples: Benefits:Boost operational efficiency by enabling seamless communication between systems. 3. Custom Code Development Go beyond standard functionality with tailored solutions using Apex, Visualforce, or Lightning Web Components. Examples: Best For:Organizations with advanced or highly specific requirements that declarative tools can’t fulfill. 4. User Interface (UI) Customization Adapt the look and feel of Salesforce to improve user experience and align with your brand. Examples: Goal:Create an intuitive, visually appealing interface that boosts productivity and user adoption. 5. Workflow Automation Save time by automating repetitive tasks and processes. Examples: Impact:Streamline operations, reduce manual workloads, and improve efficiency. 6. Reporting and Analytics Customization Provide actionable insights with tailored reports and dashboards. Examples: Advantage:Empower teams to make data-driven decisions with clear, relevant insights. 7. Mobile Optimization Ensure a seamless Salesforce experience for users on mobile devices. Examples: Purpose:Keep teams connected and productive, regardless of location. Conclusion Salesforce customization goes beyond CRM—it transforms the platform into a tailored solution that aligns with your unique business processes. Whether you’re looking for simple adjustments or advanced integrations, these customizations unlock Salesforce’s full potential to drive operational success. Ready to Get Started?Discover how our Salesforce customization services can help tailor the platform to your specific needs. Let’s work together to maximize your investment and achieve your business goals! 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
This principle was instrumental in shaping our data-focused enterprise architecture transformation in 2023, making data a core pillar alongside loan origination and servicing. Given that the company’s future growth depends on it, the focus on data was essential.
Treating AI Agents as Personas: Introducing the Era of Agent-Computer Interaction The UX landscape is evolving. While the design community has quickly adopted Large Language Models (LLMs) as tools, we’ve yet to fully grasp their transformative potential. With AI agents now deeply embedded in digital products, they are shifting from tools to active participants in our digital ecosystems. This change demands a new design paradigm—one that views AI agents not just as extensions of human users but as independent personas in their own right. The Rise of Agent-Computer Interaction AI agents represent a new class of users capable of navigating interfaces autonomously and completing complex tasks. This marks the dawn of Agent-Computer Interaction (ACI)—a paradigm where user experience design encompasses the needs of both human users and AI agents. Humans still play a critical role in guiding and supervising these systems, but AI agents must now be treated as distinct personas with unique goals, abilities, and requirements. This shift challenges UX designers to consider how these agents interact with interfaces and perform their tasks, ensuring they are equipped with the information and resources necessary to operate effectively. Understanding AI Agents AI agents are intelligent systems designed to reason, plan, and work across platforms with minimal human intervention. As defined during Google I/O, these agents retain context, anticipate needs, and execute multi-step processes. Advances in AI, such as Anthropic’s Claude and its ability to interact with graphical interfaces, have unlocked new levels of agency. Unlike earlier agents that relied solely on APIs, modern agents can manipulate graphical user interfaces much like human users, enabling seamless interaction with browser-based applications. This capability creates opportunities for new forms of interaction but also demands thoughtful design choices. Two Interaction Approaches for AI Agents Design teams must evaluate these methods based on the task’s complexity and transparency requirements, striking the right balance between efficiency and oversight. Designing Experiences Considering AI Agents Personas As AI agents transition into active users, UX design must expand to accommodate their specific needs. Much like human personas, AI agents require a deep understanding of their capabilities, limitations, and workflows. Creating AI Agent Personas Developing personas for AI agents involves identifying their unique characteristics: These personas inform interface designs that optimize agent workflows, ensuring both agents and humans can collaborate effectively. New UX Research Methodologies UX teams should embrace innovative research techniques, such as A/B testing interfaces for agent performance and monitoring their interaction patterns. While AI agents lack sentience, they exhibit behaviors—reasoning, planning, and adapting—that require careful study and design consideration. Shaping the AI Mind AI agents derive their reasoning capabilities from Large Language Models (LLMs), but their behavior and effectiveness are shaped by UX design. Designers have a unique role in crafting system prompts and developing feedback loops that refine LLM behavior over time. Key Areas for Designer Involvement: This work positions UX professionals as co-creators of AI intelligence, shaping not just interfaces but the underlying behaviors that drive agent interactions. Keeping Humans in the Loop Despite the rise of AI agents, human oversight and control remain essential. UX practitioners must prioritize transparency and trust in agent-driven systems. Key Considerations: Using tools like agentic experience maps—blueprints that visualize the interactions between humans, agents, and products—designers can ensure AI systems remain human-centered. A New Frontier for UX The emergence of AI agents heralds a shift as significant as the transition from desktop to mobile. Just as mobile devices unlocked new opportunities for interaction, AI agents are poised to redefine digital experiences in ways we can’t yet fully predict. By embracing Agent-Computer Interaction, UX designers have an unprecedented opportunity to shape the future of human-AI collaboration. Those who develop expertise in designing for these intelligent agents will lead the way in creating systems that are not only powerful but also deeply human-centered. 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
Empowering AI Agents with Real-Time Data: MuleSoft’s Full Lifecycle AsyncAPI Support MuleSoft has officially launched full lifecycle AsyncAPI support, providing organizations with the tools to connect real-time data to AI agents via event-driven architectures (EDAs). This integration empowers businesses to deploy AI agents that can autonomously act on dynamic, real-time events across various operations. MuleSoft Empowering AI Agents. AI Agents in Action with AsyncAPI The integration of Agentforce, Salesforce’s AI agent suite, with AsyncAPI takes automation to a new level. By utilizing real-time data streams, businesses can create AI agents capable of immediate, autonomous decision-making. Why AsyncAPI Matters Event-driven architectures are critical for real-time data processing, yet 43% of IT leaders struggle to integrate existing systems with their EDAs. AsyncAPI provides a scalable, standardized way to connect applications and AI agents, overcoming these challenges. Key Features of MuleSoft’s AsyncAPI Support Why It’s a Game-Changer for AI Agents AsyncAPI integration enables AI agents to function asynchronously within EDAs, meaning they can process tasks without waiting for updates. For example: Driving Innovation Across Industries Organizations in sectors like retail, IT, and financial services can leverage these capabilities: Expert Insights Andrew Comstock, VP of Product, Integration at Salesforce:“AI is reshaping how we think about modern architectures, but connectivity remains foundational. By supporting AsyncAPI, we’re empowering businesses to build event-driven, autonomous systems on a flexible and robust platform.” Maksim Kogan, Solution Architect, OBI Group Holding:“Integrating AsyncAPI into Anypoint Platform simplifies the developer experience and increases resilience, enabling real-time services that directly enhance customer satisfaction.” Availability MuleSoft’s full lifecycle AsyncAPI support is now available via the Anypoint Platform, with compatibility for Kafka, Solace, Anypoint MQ, and Salesforce Platform Events. Tools like Anypoint Code Builder and Anypoint Exchange further streamline the development process. MuleSoft Empowering AI Agents With full AsyncAPI support, MuleSoft unlocks the potential for AI agents to operate seamlessly within real-time event-driven systems. From improving customer experiences to enhancing operational efficiency, this innovation positions businesses to thrive in today’s fast-paced digital landscape. Learn more about empowering your AI agents with MuleSoft’s AsyncAPI capabilities 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
Salesforce Research Highlights Rising Stakes for Trust in the AI Era Salesforce’s latest State of the AI Connected Customer research reveals a trust crisis among consumers and highlights how AI is reshaping customer expectations. With 60% of consumers believing advances in AI make trust even more essential, businesses face mounting pressure to deliver trustworthy AI experiences. The stakes are especially high as AI agents gain traction, presenting an opportunity for brands to rebuild trust and drive engagement this holiday season—particularly among Gen Z, with nearly a third open to having AI shop on their behalf. Why It Matters As the holiday shopping season approaches, brands face the dual challenge of declining consumer trust and evolving expectations. With AI projected to influence more than 0 billion in global online sales this season, getting AI right is critical. AI agents—intelligent software capable of handling customer inquiries autonomously—can boost margins and enhance customer service by addressing issues like clunky purchasing and return processes. However, trust in these agents hinges on transparency and robust data practices. Key Insights from the Research Trust Is at an All-Time Low High Expectations for Seamless Experiences Customer service remains a critical loyalty driver: Younger Consumers Are Most Open to AI Agents Generations Z and millennials lead the charge in embracing AI agents for improved shopping experiences: However, transparency remains vital: Building Confidence in AI Agents The research underscores a mixed consumer sentiment toward AI, marked by curiosity (41%) and suspicion (44%). This presents an opportunity for brands to demystify AI’s benefits: Expert Perspectives Salesforce View:“Retailers face fierce competition this season as they aim to drive higher margins and meet rising customer expectations. AI agents enable consistent, personalized experiences across channels, fostering loyalty and boosting sales.”— Michael Affronti, SVP & GM, Commerce Cloud, Salesforce Customer Experience at Saks:“Agentforce has unlocked new potential for enhancing luxury shopping. By automating routine tasks like order tracking, our teams can focus on high-touch, personalized interactions. We’re excited to see how AI continues to elevate our service.”— Mike Hite, CTO, Saks Global 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
