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salesforce agentforce rapid deployment
14Jan

Agentforce: Your Partner in Seamless Customer Experiences

Building Deeper Customer Relationships with Agentforce In today’s competitive landscape, building meaningful relationships with customers is no longer optional—it’s essential. Customers expect brands to know them, anticipate their needs, and deliver seamless, personalized experiences across every interaction. While data and AI have the potential to help marketers achieve these goals, many organizations struggle to realize their full potential. In fact, only 32% of marketers report being completely satisfied with how they use customer data to create relevant experiences, according to our State of Marketing report. So, how can marketers close this gap? Meet Agentforce—a proactive, autonomous application designed to provide specialized, always-on support for employees and customers alike. With Agentforce, marketers can strengthen relationships through personalized conversations, proactive engagement, and seamless customer experiences. 1. Automate Personalized, Two-Way Conversations on WhatsApp Interacting one-on-one with every customer responding to a promotional campaign on WhatsApp has historically been a challenge. Limited chatbot capabilities or a complete lack of response options often resulted in missed opportunities. Agentforce changes the game by introducing a customer-facing AI agent that acts as a personal concierge directly within WhatsApp. Imagine a customer receiving an exclusive offer for a product they’ve shown interest in. They reply with a question, and the agent instantly provides tailored product recommendations, current promotions, or details about complementary products. If the customer decides to make a purchase, the agent guides them through the entire checkout process—from completing the transaction to sending real-time order updates. For more complex needs, the agent seamlessly transfers the conversation to a human service representative, ensuring continuity. Why it matters: This approach not only increases conversions but also builds customer satisfaction and loyalty through timely, relevant responses. By reducing the workload on support teams, Agentforce delivers a consistent brand experience that feels personal and effortless. 2. Create Personalized Agendas for Event Attendees Events are powerful tools for fostering customer connections and delivering value. However, ensuring attendees find the most relevant sessions and resources can be daunting. Self-guided experiences often result in missed opportunities or abandoned registrations. Agentforce for Marketing solves this by providing personalized, 1:1 assistance to every visitor on your event website. The agent recommends sessions based on visitor interests and helps attendees create personalized agendas. Leveraging past attendance and engagement data, Agentforce curates agendas tailored to each attendee’s priorities, from keynote presentations to breakout sessions. For repeat attendees, it suggests new content based on their history, completing the registration process with their customized agenda. Why it matters: Personalized agendas enhance the event experience, leading to higher satisfaction and loyalty. By making the registration journey seamless, Agentforce reduces bounce rates and builds long-term engagement with your events. 3. Capture and Qualify Leads Effortlessly on Your Website Visitors often abandon self-guided website experiences before converting into leads. With Agentforce, you can proactively engage them by providing tailored product recommendations, exclusive content offers, and opportunities to share contact information. Depending on visitor behavior, the agent might suggest gated assets like case studies or demo videos—or even register them for an event or webinar. For highly engaged prospects, the agent can schedule follow-up meetings with sales reps, ensuring visitors receive immediate value without friction. Why it matters: Automated lead capture accelerates qualification and increases conversions. By guiding visitors to the right solutions and reducing friction, Agentforce nurtures leads naturally, creating higher-quality opportunities and loyal customers. 4. Improve Customer Journeys with Intelligent Reprioritization Balancing customer engagement with respect for their communication preferences is critical. Before customers hit their communication limit, Agentforce can dynamically reprioritize their journey based on both their interests and your business goals. For example, if a customer is close to their communication cap, the agent can prioritize sending a VIP event invitation or product announcement over less relevant messages. This ensures high-value content is delivered at the right time, without overwhelming the customer. Why it matters: Intelligent reprioritization improves conversion rates, reduces unsubscribe rates, and strengthens customer relationships. By ensuring every interaction is timely and relevant, Agentforce helps keep customers engaged without feeling inundated. 5. Reduce Churn with Proactive, Personalized Promotions Retaining customers is just as important as acquiring new ones. Agentforce can identify at-risk customers using churn indicators—such as low engagement or declining purchase frequency—and automatically send tailored promotions. For instance, if a customer’s churn score nears a threshold, the agent can proactively offer a loyalty discount or renewal incentive. It can guide the customer through the redemption process, rekindling their interest before they decide to leave. Why it matters: Proactive retention strategies powered by AI increase customer lifetime value, reduce churn, and foster loyalty. With minimal effort, Agentforce ensures that marketers stay connected to customers who might otherwise disengage. Agentforce: Your Partner in Seamless Customer Experiences More than just an automation tool, Agentforce is an essential partner for delivering the personalized experiences your customers expect. By implementing these use cases, marketers can: Ready to take your marketing strategy to the next level? With Agentforce, meaningful customer relationships are within reach. 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 Agentforce Platform, Agentic AI, Data0 Comments
Statement Accuracy Prediction based on Language Model Activations
14Jan

Statement Accuracy Prediction based on Language Model Activations

When users first began interacting with ChatGPT, they noticed an intriguing behavior: the model would often reverse its stance when told it was wrong. This raised concerns about the reliability of its outputs. How can users trust a system that appears to contradict itself? Recent research has revealed that large language models (LLMs) not only generate inaccurate information (often referred to as “hallucinations”) but are also aware of their inaccuracies. Despite this awareness, these models proceed to present their responses confidently. Unveiling LLM Awareness of Hallucinations Researchers discovered this phenomenon by analyzing the internal mechanisms of LLMs. Whenever an LLM generates a response, it transforms the input query into a numerical representation and performs a series of computations before producing the output. At intermediate stages, these numerical representations are called “activations.” These activations contain significantly more information than what is reflected in the final output. By scrutinizing these activations, researchers can identify whether the LLM “knows” its response is inaccurate. A technique called SAPLMA (Statement Accuracy Prediction based on Language Model Activations) has been developed to explore this capability. SAPLMA examines the internal activations of LLMs to predict whether their outputs are truthful or not. Why Do Hallucinations Occur? LLMs function as next-word prediction models. Each word is selected based on its likelihood given the preceding words. For example, starting with “I ate,” the model might predict the next words as follows: The issue arises when earlier predictions constrain subsequent outputs. Once the model commits to a word, it cannot go back to revise its earlier choice. For instance: In another case: This mechanism reveals how the constraints of next-word prediction can lead to hallucinations, even when the model “knows” it is generating an incorrect response. Detecting Inaccuracies with SAPLMA To investigate whether an LLM recognizes its own inaccuracies, researchers developed the SAPLMA method. Here’s how it works: The classifier itself is a simple neural network with three dense layers, culminating in a binary output that predicts the truthfulness of the statement. Results and Insights The SAPLMA method achieved an accuracy of 60–80%, depending on the topic. While this is a promising result, it is not perfect and has notable limitations. For example: However, if LLMs can learn to detect inaccuracies during the generation process, they could potentially refine their outputs in real time, reducing hallucinations and improving reliability. The Future of Error Mitigation in LLMs The SAPLMA method represents a step forward in understanding and mitigating LLM errors. Accurate classification of inaccuracies could pave the way for models that can self-correct and produce more reliable outputs. While the current limitations are significant, ongoing research into these methods could lead to substantial improvements in LLM performance. By combining techniques like SAPLMA with advancements in LLM architecture, researchers aim to build models that are not only aware of their errors but capable of addressing them dynamically, enhancing both the accuracy and trustworthiness of AI systems. 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, Salesforce0 Comments
Payload and REDA One Launch REDA Pay Embedded Payment Solution Payload has unveiled REDA Pay, an embedded payment solution tailored for Salesforce users. This new offering stems from a strategic partnership between Payload and REDA One, aiming to streamline and enhance payment processes for businesses. “REDA One needed a payment solution that could be deployed quickly, efficiently, and effectively to expand their platform’s capabilities,” said Zach Jacob, Vice President of Partnerships at Payload. “Payload’s robust APIs proved to be the perfect match, seamlessly integrating with REDA One’s existing infrastructure." Jacob emphasized the ease and speed of implementation, noting, “In no time, REDA One was able to activate REDA Pay, demonstrating the simplicity and adaptability of our payment tools. This collaboration underscores Payload’s commitment to delivering powerful, straightforward solutions that enable SaaS platforms like REDA One to provide cutting-edge payment experiences without unnecessary complexity.” The Growing Role of Embedded Payments Highlighting the importance of embedded payments, Jacob stated, “In today’s digital era, embedded payments are not just a feature—they are an essential element of an exceptional user experience.” With REDA Pay, Salesforce users can now process payments seamlessly within their workflows, reducing friction and enhancing efficiency. Jacob outlined three core advantages of REDA Pay: Intuitive Experience: “Users can send and receive payments directly within the platform, boosting productivity and convenience.” Security: “Built to the highest industry standards, our solutions protect every transaction with advanced security measures.” Scalability and Flexibility: “As REDA One’s customers grow, REDA Pay will evolve to meet their changing requirements.” Elevating SaaS Platforms Jacob also highlighted the broader impact of this launch for SaaS providers: “REDA Pay exemplifies how effortlessly SaaS platforms can elevate their offerings by integrating embedded payment solutions. Payload’s APIs are designed for seamless integration, enabling platforms to quickly add enhanced payment capabilities for their users. This not only improves the user experience but also unlocks new opportunities for growth and revenue.” With REDA Pay, Payload and REDA One have set a new standard for embedded payment solutions in Salesforce, driving innovation and efficiency for businesses in the digital age.
13Jan

Payload and REDA

Payload and REDA One Launch REDA Pay Embedded Payment Solution Payload has unveiled REDA Pay, an embedded payment solution tailored for Salesforce users. This new offering stems from a strategic partnership between Payload and REDA One, aiming to streamline and enhance payment processes for businesses. “REDA One needed a payment solution that could be deployed quickly, efficiently, and effectively to expand their platform’s capabilities,” said Zach Jacob, Vice President of Partnerships at Payload. “Payload’s robust APIs proved to be the perfect match, seamlessly integrating with REDA One’s existing infrastructure.” Jacob emphasized the ease and speed of implementation, noting, “In no time, REDA One was able to activate REDA Pay, demonstrating the simplicity and adaptability of our payment tools. This collaboration underscores Payload’s commitment to delivering powerful, straightforward solutions that enable SaaS platforms like REDA One to provide cutting-edge payment experiences without unnecessary complexity.” The Growing Role of Embedded Payments Highlighting the importance of embedded payments, Jacob stated, “In today’s digital era, embedded payments are not just a feature—they are an essential element of an exceptional user experience.” With REDA Pay, Salesforce users can now process payments seamlessly within their workflows, reducing friction and enhancing efficiency. Jacob outlined three core advantages of REDA Pay: Elevating SaaS Platforms Jacob also highlighted the broader impact of this launch for SaaS providers:“REDA Pay exemplifies how effortlessly SaaS platforms can elevate their offerings by integrating embedded payment solutions. Payload’s APIs are designed for seamless integration, enabling platforms to quickly add enhanced payment capabilities for their users. This not only improves the user experience but also unlocks new opportunities for growth and revenue.” With REDA Pay, Payload and REDA One have set a new standard for embedded payment solutions in Salesforce, driving innovation and efficiency for businesses in the digital age. 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 13, 2025in Salesforce0 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
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 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 AI Tools, Data, Enterprise, Salesforce0 Comments
Salesforce AI Research Introduces BLIP-3-Video
02Jan

Salesforce AI Research Introduces BLIP-3-Video

Salesforce AI Research Introduces BLIP-3-Video: A Groundbreaking Multimodal Model for Efficient Video Understanding Vision-language models (VLMs) are transforming artificial intelligence by merging visual and textual data, enabling advancements in video analysis, human-computer interaction, and multimedia applications. These tools empower systems to generate captions, answer questions, and support decision-making, driving innovation in industries like entertainment, healthcare, and autonomous systems. However, the exponential growth in video-based tasks has created a demand for more efficient processing solutions that can manage the vast amounts of visual and temporal data inherent in videos. The Challenge of Scaling Video Understanding Existing video-processing models face significant inefficiencies. Many rely on processing each frame individually, creating thousands of visual tokens that demand extensive computational resources. This approach struggles with long or complex videos, where balancing computational efficiency and accurate temporal understanding becomes crucial. Attempts to address this issue, such as pooling techniques used by models like Video-ChatGPT and LLaVA-OneVision, have only partially succeeded, as they still produce thousands of tokens. Introducing BLIP-3-Video: A Breakthrough in Token Efficiency To tackle these challenges, Salesforce AI Research has developed BLIP-3-Video, a cutting-edge vision-language model optimized for video processing. The key innovation lies in its temporal encoder, which reduces visual tokens to just 16–32 tokens per video, significantly lowering computational requirements while maintaining strong performance. The temporal encoder employs a spatio-temporal attentional pooling mechanism, selectively extracting the most informative data from video frames. By consolidating spatial and temporal information into compact video-level tokens, BLIP-3-Video streamlines video processing without sacrificing accuracy. Efficient Architecture for Scalable Video Tasks BLIP-3-Video’s architecture integrates: This design ensures that the model efficiently captures essential temporal information while minimizing redundant data. Performance Highlights BLIP-3-Video demonstrates remarkable efficiency, achieving accuracy comparable to state-of-the-art models like Tarsier-34B while using a fraction of the tokens: For context, Tarsier-34B requires 4608 tokens for eight video frames, whereas BLIP-3-Video achieves similar results with only 32 tokens. On multiple-choice tasks, the model excelled: These results highlight BLIP-3-Video as one of the most token-efficient models in video understanding, offering top-tier performance while dramatically reducing computational costs. Advancing AI for Real-World Video Applications BLIP-3-Video addresses the critical challenge of token inefficiency, proving that complex video data can be processed effectively with far fewer resources. Developed by Salesforce AI Research, the model paves the way for scalable, real-time video processing across industries, including healthcare, autonomous systems, and entertainment. By combining efficiency with high performance, BLIP-3-Video sets a new standard for vision-language models, driving the practical application of AI in video-based systems. 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 2, 2025in Artificial Intelligence, Data, Salesforce0 Comments
AI Leader Salesforce
27Dec

Sales Leads and Lead Scoring

Sales teams often face a growing pile of leads, making it overwhelming to determine where to focus their energy. How do you prioritize effectively? Lead scoring is the answer. This methodology helps rank prospects based on their likelihood to convert into customers. By mastering lead scoring, sales teams can win more deals and drive revenue growth. What is Lead Scoring? Lead scoring is a strategy used by sales teams to evaluate and rank potential customers by assigning values based on their behavior, demographics, and interactions with the business. This process identifies high-quality leads and determines their likelihood of conversion. By implementing lead scoring, sales teams can focus their time and resources on the most promising prospects. Why is Lead Scoring Important? According to the Salesforce State of Sales Report, sales reps spend 25% of their workweek researching, prospecting, and prioritizing leads. These activities are essential for moving prospects through the sales funnel, yet balancing them with other responsibilities is a challenge. Lead scoring streamlines this process, enabling teams to be more productive by focusing on high-value leads. This improves conversion rates while helping sales leadership better forecast pipelines and revenue. For example, imagine a sales rep for a medical software company trying to close deals with 100 hospital leads. Pursuing them randomly wastes time. However, with lead scoring, they can identify the top 10 most promising leads based on specific criteria, saving time and increasing success rates. Key Components of an Effective Lead Scoring System 1. Data Categories 2. Implicit vs. Explicit Data 3. Quality Data A reliable lead scoring system depends on accurate and up-to-date data. Keeping CRM records current and synced ensures a dependable scoring process. 4. Rule Definition Define criteria based on your most successful customer profiles. Identify patterns of attributes and behaviors that consistently lead to conversion. Similarly, assess unconverted leads to understand traits that signal low potential. 5. Manual vs. Predictive Scoring Steps to Implement Lead Scoring Common Lead Scoring Mistakes to Avoid Tools and Software for Lead Scoring The right tools can make lead scoring more efficient: If you’re short on data, opt for tools that can leverage anonymized external datasets to build your scoring model, transitioning to your own data over time as you scale. Real-World Examples Lead Scoring: Your Path to Higher Conversions By effectively implementing lead scoring, your sales team can prioritize high-value leads, boost conversion rates, and achieve sustainable revenue growth. Whether you choose manual or predictive methods, the key is to focus on what drives success for your business. Take control of your sales pipeline—lead scoring will show you the way. 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 27, 2024in Data0 Comments
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 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 26, 2024in Data0 Comments
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 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 23, 2024in Data, Enterprise, Marketing Automation, Salesforce, Salesforce Education Cloud0 Comments
Python-Based Reasoning Engine
22Dec

Python-Based Reasoning Engine

Introducing a Python-Based Reasoning Engine for Deterministic AI In the age of large language models (LLMs), there’s a growing need for deterministic systems that enforce rules and constraints while reasoning about information. We’ve developed a Python-based reasoning and validation framework that bridges the gap between traditional rule-based logic and modern AI capabilities, inspired by frameworks like Pydantic. This approach is designed for developers and non-technical experts alike, making it easy to build complex rule engines that translate natural language instructions into enforceable code. Our fine-tuned model automates the creation of rules while ensuring human oversight for quality and conflict detection. The result? Faster implementation of rule engines, reduced developer overhead, and flexible extensibility across domains. The Framework at a Glance Our system consists of five core components: To analogize, this framework operates like a game of chess: Our framework supports two primary use cases: Key Features and Benefits Case Studies Validation Engine: Ensuring Compliance A mining company needed to validate employee qualifications based on age, region, and role. Example Data Structure: jsonCopy code{ “employees”: [ { “name”: “Sarah”, “age”: 25, “role”: “Manager”, “documents”: [“safe_handling_at_work”, “heavy_lifting”] }, { “name”: “John”, “age”: 17, “role”: “Laborer”, “documents”: [“heavy_lifting”] } ] } Rules: jsonCopy code{ “rules”: [ { “type”: “min_age”, “parameters”: { “min_age”: 18 } }, { “type”: “dozer_operator”, “parameters”: { “document_type”: “dozer_qualification” } } ] } Outcome:The system flagged violations, such as employees under 18 or missing required qualifications, ensuring compliance with organizational rules. Reasoning Engine: Solving the River Crossing Puzzle The classic river crossing puzzle demonstrates the engine’s reasoning capabilities. Problem Setup:A farmer must ferry a goat, a wolf, and a cabbage across a river, adhering to specific constraints (e.g., the goat cannot be left alone with the cabbage). Steps: Output:The engine generated a solution in 0.0003 seconds, showcasing its efficiency in navigating complex logic. Advanced Features: Dynamic Rule Expansion The system supports real-time rule adjustments. For instance, adding a “wolf cannot be left with a chicken” constraint introduces a conflict. By extending rules (e.g., allowing the farmer to carry two items), the engine dynamically resolves previously unsolvable scenarios. Sample Code Snippet: pythonCopy codeclass CarryingCapacityRule(Rule): def evaluate(self, state): items_moved = sum(1 for item in [‘wolf’, ‘goat’, ‘cabbage’, ‘chicken’] if getattr(state, item) == state.farmer) return items_moved <= 2 def get_description(self): return “Farmer can carry up to two items at a time” Result:The adjusted engine solved the puzzle in three moves, down from seven, while maintaining rule integrity. Collaborative UI for Rule Creation Our user interface empowers domain experts to define rules without writing code. Developers validate these rules, which are then seamlessly integrated into the system. Visual Workflow: 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 22, 2024in Data0 Comments
salesforce agentforce ai powered agentic agents
18Dec

Marketing Agents for Campaigns

Marketing Agents: The Next Frontier in Campaign Creation and Optimization For years, content creation and distribution have been key challenges for marketers. According to recent research from the Content Marketing Institute, 54% of B2B marketers lack the resources needed to produce high-quality content at scale. Similarly, B2C marketers often struggle to create consistent, repeatable, and scalable processes. On top of these challenges, they must balance resource management to ensure campaigns are efficient, impactful, and engaging enough to stand out from the competition. The solution to these challenges lies in marketing agents, powered by data and AI. These intelligent tools streamline content creation, optimize campaigns, and make marketing processes more efficient. At this year’s Dreamforce, Salesforce introduced Agentforce, a suite of tools designed to create, customize, and deploy marketing agents across the Customer 360 platform. As part of this launch, Agentforce Campaigns is already gaining attention from customers eager to harness its potential. According to Salesforce’s 9th State of Marketing Report, 71% of marketers plan to integrate generative and predictive AI into their workflows within the next 18 months. This insight explores how marketing agents, like those offered in Agentforce, can transform customer engagement while enhancing internal team productivity and collaboration. Redefining Campaign Delivery with Marketing Agents Marketing agents are part of a broader suite of AI-powered virtual assistants that collaborate with humans to streamline workflows and optimize decision-making. These agents analyze data, interpret requests, and execute tasks such as content generation, campaign optimization, and performance analysis. Salesforce’s AI agents are designed to be partners, taking on time-consuming tasks so marketers can focus on high-value, strategic activities. With marketing agents, teams spend less time gathering and managing data and more time delivering personalized, impactful campaigns. A key mindset shift is required to maximize the value of these tools: instead of asking, “How can agents make business as usual more efficient?”, marketers should think, “How can agents transform the way we deliver exceptional customer experiences?” Streamlining Campaign Creation with Agentforce Campaigns 1. Intelligent Recommendations for Focused Action Einstein, Salesforce’s AI engine, analyzes data and provides contextual recommendations to help marketers achieve their goals. Agentforce Campaigns takes this a step further by turning these static recommendations into actionable steps, such as adjusting an audience segment or creating an entirely new campaign. 2. Rapid Campaign Brief Creation Agentforce Campaigns can draft campaign briefs in seconds using natural language prompts. These briefs incorporate organizational goals and marketing guidelines, making them ready for review and sharing within Salesforce. This saves time and ensures alignment from the start. 3. Contextual Content Creation Once a campaign brief is approved, marketing agents can generate campaign content such as emails and landing pages. Agentforce Campaigns automatically drafts subject lines, body copy, and calls to action within branded templates. Content can be refined using natural language commands or manual adjustments to fit the brand’s tone and strategy. 4. Simplified Audience Segmentation Marketers no longer need SQL expertise to build audience segments. With Agentforce Campaigns, they can describe their target audience in natural language, and the AI translates this into the necessary segment attributes. 5. Journey Activation at Scale Marketing agents simplify the process of activating multi-channel campaigns. Using natural language prompts, Agentforce Campaigns can configure a draft journey, complete with personalized content, ready for refinement and deployment. Unlocking New Opportunities with Marketing Agents 6. Unlimited Content Variations Time and budget constraints often limit the number of content variations marketers can produce. Agentforce Campaigns overcomes this limitation by generating multiple personalized content versions in seconds. This allows teams to deliver highly tailored messages for different audience segments without additional effort. 7. Nuanced Segment Exploration Traditional segmentation often depends on data science teams, which may have limited capacity. Marketing agents empower marketers to build their own nuanced segments using natural language. For example, separate churn segments can be created based on engagement scores, location, or purchase history, enabling more precise targeting. 8. A Culture of Testing and Learning Testing often falls by the wayside due to time constraints. Marketing agents make it easy to embed testing into campaigns by automating journey flows, enabling marketers to adopt a culture of continuous experimentation and improvement without increasing workloads. Transforming Marketing with Agentforce By integrating marketing agents into workflows, businesses can improve productivity, enhance personalization, and scale campaigns like never before. Agentforce Campaigns enables marketers to automate routine tasks, explore untapped opportunities, and deliver exceptional customer experiences. Marketers who embrace this shift will not only increase efficiency but also elevate their strategies, creating campaigns that are more targeted, impactful, and scalable. Note: Some features and services mentioned may not yet be available. Customers should base purchase decisions on currently available features. 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 18, 2024in Agentforce Platform, Agentic AI, Data0 Comments
Why Tracking Business Metrics Matters More Than You Think
14Dec

Why Tracking Business Metrics Matters More Than You Think

Without measurement, a business is flying by the seat of its pants. In business, as in many areas of life, tracking progress is essential for growth. For example, one individual has been tracking cycling times on the same routes for over five years, and while performance has slowed, improvements in other areas, like taking more time off with family and building stronger client relationships, have been evident. Despite this, many businesses still fail to measure enough, particularly when it comes to understanding key performance indicators. A recent Salesforce survey found that 60% of small businesses rely primarily on cash flow as their key metric, often neglecting other important indicators of business health. For many, the primary measure of success is simply how much money is in the bank account, which, while important, is only a small part of the larger picture. The importance of measurement and metrics for business success and growth cannot be over emphasized. By tracking the right indicators, businesses gain a competitive edge and the ability to adapt and thrive in an ever-changing market. The Importance of Measurement Today, measuring business performance is more critical than ever for several reasons: Key Metrics to Measure While industry-specific metrics are important, there are several universal indicators that every management team should focus on. Thanks to new digital tools, gathering and analyzing these metrics is easier than ever, offering a comprehensive view of a business’s health. The Consequences of Not Measuring Without measurement, businesses are essentially operating without road signs. Small businesses, in particular, may not measure enough, while larger organizations may suffer from “analysis paralysis” by over-measuring and becoming overwhelmed by data. Measurement makes a difference. Just as an individual may track cycling times without measuring other variables like weight or diet, businesses must decide which metrics are most relevant to their success. While some aspects of business may be left unmeasured, others—such as sales, margins, and marketing performance—are vital for growth and strategic decision-making. In conclusion, businesses that embrace measurement are better equipped to navigate challenges, seize opportunities, and ultimately, thrive in a competitive 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 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 14, 2024in Data, Salesforce0 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

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