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llm-d

LLM-D

llm-d is a Kubernetes-native distributed inference serving stack – a well-lit path for anyone to serve large language models at scale, with the fastest time-to-value and competitive performance per dollar for most models across most hardware accelerators. With llm-d, users can operationalize GenAI deployments with a modular solution that leverages the latest distributed inference optimizations like KV-cache aware routing and disaggregated serving, co-designed and integrated with the Kubernetes operational tooling in Inference Gateway (IGW). Built by leaders in the Kubernetes and vLLM projects, llm-d is a community-driven, Apache-2 licensed project with an open development model. 🧱 Architecture llm-d adopts a layered architecture on top of industry-standard open technologies: vLLM, Kubernetes, and Inference Gateway. Key features of llm-d include: Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Agentic AI Race

The Evolution Beyond AI Agents

The Evolution Beyond AI Agents: What Comes Next? The Rapid Progression of AI Terminology The landscape of artificial intelligence has undergone a remarkable transformation in just three years. What began with ChatGPT and generative AI as the dominant buzzwords quickly evolved into discussions about copilots, and most recently, agentic AI emerged as 2024‘s defining concept. This accelerated terminology cycle mirrors fashion industry trends more than traditional technology adoption curves. Major players including Adobe, Qualtrics, Oracle, OpenAI, and Deloitte have recently launched agentic AI platforms, joining earlier entrants like Microsoft, AWS, and Salesforce. This rapid market saturation suggests the industry may already be approaching the next conceptual shift before many organizations have fully implemented their current AI strategies. Examining the Staying Power of Agentic AI Industry analysts present diverging views on the longevity of the agentic AI concept. Brandon Purcell, a Forrester Research analyst, acknowledges the pattern of fleeting AI trends while recognizing agentic AI’s potential for greater staying power. He cites three key factors that may extend its relevance: Klaasjan Tukker, Adobe’s Senior Director of Product Marketing, draws parallels to mature technologies that have become invisible infrastructure. He predicts agentic AI will follow a similar trajectory, becoming so seamlessly integrated that users will interact with it as unconsciously as they use navigation apps or operate modern vehicles. The Automotive Sector as an AI Innovation Catalyst The automotive industry provides compelling examples of advanced AI applications that transcend current “agentic” capabilities. Modern autonomous vehicles demonstrate sophisticated AI behaviors including: These implementations suggest that what the tech industry currently labels as “agentic” may represent only an intermediate step toward more autonomous, context-aware systems. The Definitional Challenges of Agentic AI The technology sector faces significant challenges in establishing common definitions for emerging AI concepts. Adobe’s framework describes agents as systems possessing three core attributes: However, as Scott Brinker of HubSpot notes, the term “agentic” risks becoming overused and diluted as vendors apply it inconsistently across various applications and functionalities. Interoperability as the Critical Success Factor For agentic AI systems to deliver lasting value, industry observers emphasize the necessity of cross-platform compatibility. Phil Regnault of PwC highlights the reality that enterprise environments typically combine solutions from multiple vendors, creating integration challenges for AI implementations. Three critical layers require standardization: Without such standards, organizations risk creating new AI silos that mirror the limitations of legacy systems. The Future Beyond Agentic AI While agentic AI continues its maturation process, the technology sector’s relentless innovation cycle suggests the next conceptual breakthrough may emerge sooner than expected. Historical naming patterns for AI advancements indicate several possibilities: As these technologies evolve, they may shed specialized branding in favor of more utilitarian terminology, much as “software bots” became normalized after their initial hype cycle. The automotive parallel suggests that truly transformative AI implementations may become so seamlessly integrated that their underlying technology becomes invisible to end users—the ultimate measure of technological maturity. Until that point, the industry will likely continue its rapid cycle of innovation and rebranding, searching for the next paradigm that captures the imagination as powerfully as “agentic AI” has in 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Data Governance in Salesforce

Salesforce Doubles Down on Trust

Salesforce Doubles Down on Trust with New AI Agent Governance Tools As businesses increasingly rely on AI agents to interact with customers and employees, trust in these systems is non-negotiable. That’s why Salesforce recently introduced a suite of governance, security, and compliance features designed to ensure AI agents operate safely and responsibly. The move comes as concerns about AI trustworthiness persist. According to Salesforce’s State of IT survey—which polled over 2,000 enterprise IT security leaders—48% worry their data infrastructure isn’t ready for agentic AI, while 55% lack confidence in their existing guardrails for deployment. Salesforce’s new capabilities aim to address these gaps by enabling end-to-end data governance across its platform, whether data resides within Salesforce applications or external sources. Key products powering this initiative include: Unlike piecemeal solutions, Salesforce promises a fully integrated, enterprise-grade framework for secure and governed AI. Agentforce, in particular, provides granular control, visibility, and compliance at every stage—from development to deployment. Key Features “Enterprise AI’s potential is huge, but it demands trusted data and secure development,” said Rahul Auradkar, EVP & GM of Data Cloud. “By unifying data, simplifying agent development, and embedding governance from the start, we’re enabling powerful—yet responsible—AI deployments.” Developer Tools for Safer AI Testing Before agents go live, Salesforce offers: Developers can also fine-tune agent reasoning using custom variables (e.g., customer verification status) and apply filters to restrict certain actions—ensuring AI operates within defined boundaries. With these updates, Salesforce is betting that trust, not just capability, will determine the success of AI agents in the enterprise. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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agetnforce for nonprofits

AgentForce Flex Pricing

Salesforce Introduces Flexible Pricing for Agentforce to Accelerate AI Adoption Across Enterprises Salesforce, the global leader in AI-powered CRM, last week announced a new flexible pricing model for Agentforce, its digital labor platform, designed to meet surging demand for AI-driven automation across every employee, department, and business process. As AI adoption accelerates, CIOs face mounting pressure to balance innovation with cost control. According to Salesforce’s CIO AI Trends research, 90% of IT leaders say managing AI expenses is hindering their ability to drive value—a challenge underscored by recent findings from CIO.com. To address this, Salesforce is introducing three groundbreaking pricing innovations that empower businesses to scale AI adoption efficiently, align costs with outcomes, and adapt investments as needs evolve: 1. Flex Credits: Pay Only for the AI Actions You Use Moving beyond traditional per-conversation pricing, Salesforce now offers Flex Credits, a consumption-based model where customers pay only for the specific AI actions performed—whether updating records, automating workflows, or resolving cases. 2. Flex Agreement: Shift Investments Between Human & Digital Labor The new Flex Agreement allows organizations to dynamically reallocate budgets—converting user licenses into Flex Credits (or vice versa)—ensuring optimal resource allocation as business priorities shift. 3. Agentforce User Licenses & Add-Ons: Unlimited AI for Every Employee Salesforce is simplifying AI adoption with per-user-per-month (PUPM) pricing, offering unlimited employee-facing AI agent usage. Seamlessly integrated with Salesforce and Slack, these licenses eliminate usage caps, enabling businesses to deploy AI at scale across sales, service, HR, and IT. Industry & Customer Reactions Availability & Pricing With this move, Salesforce reinforces its commitment to making AI accessible, scalable, and cost-effective for enterprises worldwide. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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salesforce service assistant

Salesforce Service Assistant

Salesforce Service Assistant is a new skill within Agentforce. It’s an AI-powered agent designed to assist human service reps in resolving cases and improving customer experiences. Service Assistant leverages Agentforce’s generative AI capabilities and is grounded in unique data from Salesforce. It helps agents by generating case summaries and actionable resolution steps.  In simpler terms: Salesforce has created a new AI assistant called “Service Assistant” that’s part of their Agentforce platform. This assistant helps service reps handle cases more efficiently by using AI to analyze data and provide guidance. Here’s a more in depth look at what Service Assistant does: Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Financial Services Cloud and Core

AgentForce for Financial Services

Salesforce Revolutionizes Financial Services with AI-Powered Agentforce Platform AI Agents Take on Banking, Insurance, and Advisory Roles Salesforce has launched a suite of prebuilt AI agent templates designed to automate critical functions across financial services—from loan processing to insurance underwriting and wealth management. Embedded within Financial Services Cloud, these AI assistants aim to reduce administrative burdens, enhance customer experiences, and boost operational efficiency for banks, insurers, and investment firms. Key Features of Agentforce for Financial Services ✔ Prebuilt AI agents for loan officers, financial advisors, and insurance agents✔ 24/7 automated customer service (balance inquiries, claims processing, policy quotes)✔ Meeting intelligence (automated note-taking, follow-ups, and data-driven insights)✔ Regulatory compliance baked into every AI action✔ Seamless integration with core banking and CRM systems How AI Agents Transform Financial Workflows 1. Financial Advisors: Smarter, Faster Client Interactions 2. Banking & Insurance: Instant, Accurate Customer Service 3. Digital Loan Officers: Faster Approvals, Fewer Delays Why Financial Firms Need Specialized AI Agents Traditional customer service struggles with:❌ Long hold times❌ Repetitive data entry❌ Inconsistent compliance checks Agentforce AI solves these pain points by:✅ Reducing manual work (80%+ of routine tasks automated)✅ Improving accuracy (data-driven decisions, no human errors)✅ Ensuring compliance (built-in regulatory safeguards) Real-World Impact: “Agentforce has already transformed our service operations. The speed of deployment and immediate productivity gains have us exploring AI for claims and procurement next.”— Matt Brasch, VP of Digital Operations, Cumberland Mutual LLC The Future of AI in Finance Salesforce emphasizes that AI won’t replace human experts—it will empower them. By offloading repetitive tasks, financial professionals can focus on:✔ High-value client relationships✔ Complex decision-making✔ Strategic business growth Coming Next: Final Takeaway Salesforce’s Agentforce for Financial Services marks a major leap in AI-driven banking and insurance automation. Firms adopting this technology can expect:🔹 Faster customer service🔹 Higher advisor productivity🔹 Stronger compliance🔹 Increased revenue per employee Ready to deploy AI agents in your financial workflows? Contact Tectonic. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Grok 3 Model Explained

Grok 3 Model Explained: Everything You Need to Know xAI has introduced its latest large language model (LLM), Grok 3, expanding its capabilities with advanced reasoning, knowledge retrieval, and text summarization. In the competitive landscape of generative AI (GenAI), LLMs and their chatbot services have become essential tools for users and organizations. While OpenAI’s ChatGPT (powered by the GPT series) pioneered the modern GenAI era, alternatives like Anthropic’s Claude, Google Gemini, and now Grok (developed by Elon Musk’s xAI) offer diverse choices. The term grok originates from Robert Heinlein’s 1961 sci-fi novel Stranger in a Strange Land, meaning to deeply understand something. Grok is closely tied to X (formerly Twitter), where it serves as an integrated AI chatbot, though it’s also available on other platforms. What Is Grok 3? Grok 3 is xAI’s latest LLM, announced on February 17, 2025, in a live stream featuring CEO Elon Musk and the engineering team. Musk, known for founding Tesla, SpaceX, and acquiring Twitter (now X), launched xAI on March 9, 2023, with the mission to “understand the universe.” Grok 3 is the third iteration of the model, built using Rust and Python. Unlike Grok 1 (partially open-sourced under Apache 2.0), Grok 3 is proprietary. Key Innovations in Grok 3 Grok 3 excels in advanced reasoning, positioning it as a strong competitor against models like OpenAI’s o3 and DeepSeek-R1. What Can Grok 3 Do? Grok 3 operates in two core modes: 1. Think Mode 2. DeepSearch Mode Core Capabilities ✔ Advanced Reasoning – Multi-step problem-solving with self-correction.✔ Content Summarization – Text, images, and video summaries.✔ Text Generation – Human-like writing for various use cases.✔ Knowledge Retrieval – Accesses real-time web data (especially in DeepSearch mode).✔ Mathematics – Strong performance on benchmarks like AIME 2024.✔ Coding – Writes, debugs, and optimizes code.✔ Voice Mode – Supports spoken responses. Previous Grok Versions Model Release Date Key Features Grok 1 Nov. 3, 2023 Humorous, personality-driven responses. Grok 1.5 Mar. 28, 2024 Expanded context (128K tokens), better problem-solving. Grok 1.5V Apr. 12, 2024 First multimodal version (image understanding). Grok 2 Aug. 14, 2024 Full multimodal support, image generation via Black Forest Labs’ FLUX. Grok 3 vs. GPT-4o vs. DeepSeek-R1 Feature Grok 3 GPT-4o DeepSeek-R1 Release Date Feb. 17, 2025 May 24, 2024 Jan. 20, 2025 Developer xAI (USA) OpenAI (USA) DeepSeek (China) Reasoning Advanced (Think mode) Limited Strong Real-Time Data DeepSearch (web access) Training data cutoff Training data cutoff License Proprietary Proprietary Open-source Coding (LiveCodeBench) 79.4 72.9 64.3 Math (AIME 2024) 99.3 87.3 79.8 How to Use Grok 3 1. On X (Twitter) 2. Grok.com 3. Mobile App (iOS/Android) Same subscription options as Grok.com. 4. API (Coming Soon) No confirmed release date yet. Final Thoughts Grok 3 is a powerful reasoning-focused LLM with real-time search capabilities, making it a strong alternative to GPT-4o and DeepSeek-R1. With its DeepSearch and Think modes, it offers advanced problem-solving beyond traditional chatbots. Will it surpass OpenAI and DeepSeek? Only time—and benchmarks—will tell.  Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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AI Agents and Work

From AI Workflows to Autonomous Agents

From AI Workflows to Autonomous Agents: The Path to True AI Autonomy Building functional AI agents is often portrayed as a straightforward task—chain a large language model (LLM) to some APIs, add memory, and declare autonomy. Yet, anyone who has deployed such systems in production knows the reality: agents that perform well in controlled demos often falter in the real world, making poor decisions, entering infinite loops, or failing entirely when faced with unanticipated scenarios. AI Workflows vs. AI Agents: Key Differences The distinction between workflows and agents, as highlighted by Anthropic and LangGraph, is critical. Workflows dominate because they work reliably. But to achieve true agentic AI, the field must overcome fundamental challenges in reasoning, adaptability, and robustness. The Evolution of AI Workflows 1. Prompt Chaining: Structured but Fragile Breaking tasks into sequential subtasks improves accuracy by enforcing step-by-step validation. However, this approach introduces latency, cascading failures, and sometimes leads to verbose but incorrect reasoning. 2. Routing Frameworks: Efficiency with Blind Spots Directing tasks to specialized models (e.g., math to a math-optimized LLM) enhances efficiency. Yet, LLMs struggle with self-assessment—they often attempt tasks beyond their capabilities, leading to confident but incorrect outputs. 3. Parallel Processing: Speed at the Cost of Coherence Running multiple subtasks simultaneously speeds up workflows, but merging conflicting results remains a challenge. Without robust synthesis mechanisms, parallelization can produce inconsistent or nonsensical outputs. 4. Orchestrator-Worker Models: Flexibility Within Limits A central orchestrator delegates tasks to specialized components, enabling scalable multi-step problem-solving. However, the system remains bound by predefined logic—true adaptability is still missing. 5. Evaluator-Optimizer Loops: Limited by Feedback Quality These loops refine performance based on evaluator feedback. But if the evaluation metric is flawed, optimization merely entrenches errors rather than correcting them. The Four Pillars of True Autonomous Agents For AI to move beyond workflows and achieve genuine autonomy, four critical challenges must be addressed: 1. Self-Awareness Current agents lack the ability to recognize uncertainty, reassess faulty reasoning, or know when to halt execution. A functional agent must self-monitor and adapt in real-time to avoid compounding errors. 2. Explainability Workflows are debuggable because each step is predefined. Autonomous agents, however, require transparent decision-making—they should justify their reasoning at every stage, enabling developers to diagnose and correct failures. 3. Security Granting agents API access introduces risks beyond content moderation. True agent security requires architectural safeguards that prevent harmful or unintended actions before execution. 4. Scalability While workflows scale predictably, autonomous agents become unstable as complexity grows. Solving this demands more than bigger models—it requires agents that handle novel scenarios without breaking. The Road Ahead: Beyond the Hype Today’s “AI agents” are largely advanced workflows masquerading as autonomous systems. Real progress won’t come from larger LLMs or longer context windows, but from agents that can:✔ Detect and correct their own mistakes✔ Explain their reasoning transparently✔ Operate securely in open environments✔ Scale intelligently to unforeseen challenges The shift from workflows to true agents is closer than it seems—but only if the focus remains on real decision-making, not just incremental automation improvements. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce

Salesforce Go

Introducing Salesforce Go: Your One-Stop Hub for Discovering & Enabling Features Salesforce is making it easier than ever for admins to explore, set up, and manage features with Salesforce Go—a new, intuitive experience designed to simplify feature discovery and configuration. No more hunting through menus—Salesforce Go puts everything you need in one place, helping you: ✅ Quickly find and evaluate new features✅ Understand setup steps before enabling them✅ Access relevant tools and documentation in context Best of all? No activation needed—it’s automatically available in your org! How It Works Who Can Use It? Why You’ll Love It 🔹 Save time – No more jumping between Setup and Help docs.🔹 Make informed decisions – Watch demos, explore Trailhead modules, and share resources with stakeholders.🔹 Monitor usage – Track adoption and manage licenses (where applicable). Now Live – With More Enhancements Coming! Salesforce Go is already rolling out, with new improvements in Spring ‘25, including deeper usage analytics and streamlined purchasing for add-ons (via Your Account). Ready to explore? Open Salesforce Go today and unlock the full power of your Salesforce org! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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The Paradox of Jagged Intelligence in AI

The Paradox of Jagged Intelligence in AI

AI systems are breaking records on complex benchmarks, yet they falter on simpler tasks humans handle intuitively—a phenomenon dubbed jagged intelligence. This ainsight explores this uneven capability, tracing its evolution in frontier models and the impact of reasoning models. We introduce SIMPLE, a new public benchmark with easy reasoning tasks solvable by high schoolers, vital for enterprise AI where reliability trumps advanced math skills. Since ChatGPT’s 2022 debut, foundation models have been marketed as chat interfaces. Now, reasoning models like OpenAI’s o3 and DeepSeek’s R1 leverage extra inference-time computation for step-by-step internal reasoning, boosting performance in math, engineering, and coding. This shift to scaling inference compute arrives as pretraining gains may be plateauing. Benchmarking the Gaps Traditional AI benchmarks measure peak performance on tough tasks, like graduate exams or complex code, creating new challenges as old ones are mastered. However, they overlook reliability and worst-case performance on basic tasks, masking jaggedness in “solved” areas. Modern models outshine humans on some challenges but stumble unpredictably on others, unlike specialized tools (e.g., calculators or photo editors). Despite advances in modeling and training, this inconsistent jaggedness persists. SIMPLE targets easy problems where AI still lags, offering insights into jaggedness trends. Evolution of Jaggedness Will jaggedness shrink or grow as models advance? This question shapes enterprise AI success. Lacking jaggedness benchmarks, we created SIMPLE—a dataset of 225 simple questions, each solvable by at least 10% of high schoolers. Example Questions from SIMPLE Performance Trends Evaluating current and past top models on SIMPLE traces jaggedness over time. Green tasks are high school-level; blue are expert-level. School-level benchmarks saturated by 2023-2024, shifting focus to harder tasks. SIMPLE, using the best of gpt-4, gpt-4-turbo, gpt-4o, o1, and o3-mini, scores lowest on school-level questions. Yet, reasoning models show a ~30% improvement, suggesting they reduce jaggedness by double-checking work, linking reasoning to better simple-task performance. Case Study Insights and Implications Reasoning models transfer top-line gains to simple tasks to some extent, but SIMPLE remains unsaturated. Jaggedness persists, with top-line progress outpacing worst-case improvements. This mirrors computing’s history: excelling in narrow domains, outpacing human limits once applied, yet always facing new challenges. Jaggedness may not just define AI—it could be computation’s inherent nature. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce prompt builder

Mastering Agentforce

Mastering Agentforce: How to Supercharge Salesforce with AI-Powered Prompts Unlocking the Power of Agentforce Salesforce’s Agentforce is transforming how businesses automate marketing and sales—using generative AI to handle repetitive tasks, respond to prospect behavior in real time, and drive smarter strategies with less effort. But to fully leverage Agentforce, you need to master prompt engineering—the art of crafting effective AI instructions. (Don’t let the term “engineering” intimidate you—it simply means writing clear, structured prompts!) AI Prompts 101: The Key to Personalized Automation An AI prompt is a detailed instruction that guides Salesforce’s large language model (LLM) to generate relevant, business-specific responses. Why Prompts Matter Introducing Salesforce Prompt Builder Prompt Builder is Agentforce’s central hub for creating, managing, and applying reusable prompt templates across your AI Agents. How It Works 3 Types of Prompt Templates Step-by-Step: How to Use Prompt Builder 1. Get Access 2. Open Prompt Builder 3. Craft Your Prompt Every effective prompt should include:✅ Who’s involved? (Roles, relationships, data)Example: “You are a marketer named {!user.firstname} writing to {!account.name}, a potential customer.” ✅ Context (Tone, style, language)Example: “Write a professional yet conversational email in British English.” ✅ Goal (What should the AI accomplish?)Example: “Persuade {!account.name} to book a 15-minute intro call.” ✅ Constraints (Word limits, data boundaries)Example: “Keep under 300 words. Avoid jargon and unsupported claims.” 📌 Pro Tip: Draft prompts in a separate doc first for easy editing. 4. Test & Refine Before going live:✔ Verify responses match your goals & brand voice.✔ Check for bias, errors, or inconsistencies.✔ Fine-tune by adding more context or rephrasing. 5. Deploy Activate your prompt for use in: Why This Changes Everything With Agentforce + Prompt Builder, Salesforce users can:🚀 Scale hyper-personalized outreach without manual work.🤖 Automate repetitive tasks while maintaining brand consistency.📈 Drive higher ROI with AI that adapts to real-time data. Ready to transform your Salesforce automation? Start engineering smarter prompts today! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Slack Operating System

U.S. Government Secures Major Discounts on Slack for Federal Agencies

U.S. Government Secures Major Discounts on Slack for Federal Agencies Through New GSA-Salesforce Partnership May 19, 2025 – In a major step to boost federal efficiency, the U.S. General Services Administration (GSA) has signed a landmark OneGov agreement with Salesforce, slashing the cost of Slack for government agencies. The deal provides deep discounts on Slack’s enterprise collaboration tools, enabling federal offices to modernize operations while cutting costs. Key Details of the Agreement Under the new terms, federal agencies will receive: These reduced rates will be available until November 30, 2025, giving agencies a six-month window to adopt the platform at significantly lower costs. Unlike past agreements, where agencies negotiated individual discounts, this deal leverages the total purchasing power of the federal government, ensuring better pricing and streamlined procurement. The move reflects the GSA’s push to centralize IT acquisitions, eliminating redundancies and maximizing savings. Leadership and Strategic Goals Josh Gruenbaum, GSA Federal Acquisition Service Commissioner, highlighted the partnership’s significance: “Through the OneGov initiative, we’re demonstrating that the federal government is a strong partner for industry, securing top-tier tools at the best value for taxpayers.” The OneGov program, backed by the current administration, aims to foster long-term partnerships with tech providers, potentially leading to more discounted offerings in the coming fiscal year. Boosting Government Productivity Slack, now part of Salesforce, serves as a unified hub for communication, app integration, and AI-driven workflows. Its adoption across federal agencies could improve interdepartmental collaboration, reduce inefficiencies, and accelerate decision-making. The inclusion of Slack AI for Enterprise introduces advanced automation, helping agencies process data faster and optimize operations—a critical advantage for large, complex organizations. A Shift in Federal Tech Procurement This agreement signals a broader move toward modernizing government IT infrastructure while controlling costs. As noted by NextGov, the deal is part of GSA’s strategy to offer cost-effective, scalable solutions under the OneGov framework. By standardizing collaboration tools across agencies, the federal government could enhance interoperability, reduce reliance on fragmented systems, and lower long-term technical debt. Looking Ahead Industry analysts suggest that this partnership could pave the way for more AI and cloud-based solutions in government. The steep discounts may drive rapid adoption, setting a precedent for future public-private tech collaborations. As agencies integrate Slack into their workflows, the impact on federal productivity and service delivery will be closely monitored—potentially serving as a model for future digital transformation efforts. Sources: BizSugar, Investing.com, NextGov Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Once Upon a Time in Data Land

Once Upon a Time in Data Land: Building the Artificial Intelligence-Ready Warehouse In the early days of data, businesses simply wanted to know what had already happened in the past. Questions like “How many units shipped?” or “What were last month’s sales?” drove the first major digital settlements—the Digitally Filed Data Warehouse. Looking back this seems like the aluminum carport you can have erected in your driveway. The Meticulously Organized Library (The Digitally Filed Data Warehouse Era) Imagine a grand, meticulously organized library. Data from sales, finance, and inventory wasn’t just dumped inside—it went through ETL (Extract, Transform, Load), where it was cleaned, standardized, and structured into predefined formats. Need quarterly sales figures? They were always in the same place, ready for reliable reporting. But then, the world outside got messy. Suddenly, businesses weren’t just dealing with neat rows and columns—they faced website clicks, customer emails, sensor data, social media streams, images, and videos. The rigid Digitally Filed Data Warehouse struggled to adapt. Trying to force unstructured data through ETL was like trying to shelve a waterfall—slow, expensive, and often impossible. The Everything Shed (The Rise of the AI-Powered Warehouse) Enter the AI-Powered Warehouse—a vast, flexible storage space built for raw, unstructured data. Instead of forcing structure upfront, it embraced “store first, organize later” (schema-on-read). Data scientists could explore everything, from tweets to video transcripts, without constraints. But freedom had a cost. Without governance, many AI-Powered Warehouses became “data swamps”—cluttered, unreliable, and slow. Finding clean, trustworthy data was a treasure hunt, and building reliable AI pipelines was a challenge. Organizing the Shed (The AI-Ready Warehouse Paradigm) The solution? Structure without sacrifice. The AI-Ready Warehouse kept the flexibility of raw storage but added intelligence on top. Technologies like Delta Lake, Apache Iceberg, and Apache Hudi introduced:✔ ACID transactions (no more corrupted data)✔ Data versioning (“time travel” to past states)✔ Schema enforcement (order without rigidity)✔ Performance optimizations (speed at scale) A key innovation was the Medallion Architecture, organizing data by quality: This hybrid approach unified BI dashboards, analytics, and machine learning—all on the same foundation. The AI Factory (The Modern AI-Functioning Warehouse) Just as businesses adapted, AI evolved. Generative AI, autonomous agents, and real-time decision-making demanded more than batch-processed data. The AI-Ready Warehouse transformed into a fully integrated AI factory, built for: 🔹 Real-Time & Streaming Data 🔹 Seamless MLOps Integration 🔹 Vector Databases & Embeddings 🔹 Robust AI Governance Why This Matters for AI Agents Autonomous AI agents don’t just analyze data—they act on it. The AI-Functioning Warehouse gives them:✔ Context: Real-time data + historical insights✔ Consistency: Features match training data✔ Memory: Logged actions for continuous learning The Future: An AI-Native Data Ecosystem The journey from Digitally Filed Data Warehouse to AI-Powered Warehouse to AI-Functioning Warehouse reflects a shift from static reporting to dynamic intelligence. For businesses embracing AI, the question is no longer “Do we need a data strategy?” but “Is our data foundation AI-ready?” The answer will separate the leaders from the laggards in the age of AI. Next Steps: The future belongs to those who build not just for data, but for AI.  Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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AI Agent Revolution

The Salesforce AI Agent Maturity Model

The Salesforce AI Agent Maturity Model: A Roadmap for Scaling Intelligent Automation With 84% of CIOs believing AI will be as transformative as the internet, strategic adoption is no longer optional—it’s a competitive imperative. Yet many organizations struggle with where to begin, how to scale AI agents, and how to measure success. To help enterprises navigate this challenge, Salesforce has introduced the Agentic Maturity Model, a four-stage framework that guides businesses from basic automation to advanced, multi-agent ecosystems. “While agents can be deployed quickly, scaling them effectively requires a thoughtful, phased approach,” said Shibani Ahuja, SVP of Enterprise IT Strategy at Salesforce. “This model provides a clear roadmap to help organizations progress toward higher levels of AI maturity.” How Leading Companies Are Using the Framework Wiley: Building a Future-Ready AI Foundation “Visionary leadership is essential in today’s rapidly evolving AI landscape,” said Kevin Quigley, Director of Process Improvement at Wiley. “Salesforce’s framework ensures the building blocks we create today will support our long-term AI strategy.” Alpine Intel: Accelerating Efficiency in Insurance “Every minute saved counts in our high-volume claims business,” said Kelly Bentubo, Director of Architecture at Alpine Intel. “This model brings clarity to scaling AI—helping us move from time-saving automations to advanced multi-agent applications.” The Four Levels of Agentic Maturity Level 0: Fixed Rules & Repetitive Tasks (Chatbots & Co-pilots) What it is: Basic automation with no reasoning—think FAQ bots or scripted workflows.Example: A chatbot handling password resets via predefined decision trees. How to Advance to Level 1:✔ Identify rigid processes ripe for AI reasoning.✔ Measure time/cost savings from automation.✔ Start with low-risk, employee-facing agents. Level 1: Information Retrieval Agents What it is: AI that fetches data and suggests actions (but doesn’t act alone).Example: A support agent recommending troubleshooting steps from a knowledge base. How to Advance to Level 2:✔ Shift from recommendations to autonomous actions.✔ Improve data quality and governance.✔ Track metrics like case deflection and CSAT. Level 2: Simple Orchestration (Single Domain) What it is: Agents automating multi-step tasks within one system.Example: Scheduling meetings + sending follow-ups using calendar/email data. How to Advance to Level 3:✔ Choose between specialized agents or a “mega-agent.”✔ Extend capabilities with API integrations.✔ Design scalable architecture for future growth. Level 3: Complex Orchestration (Cross-Domain) What it is: AI coordinating workflows across departments (e.g., sales + service).Example: An agent analyzing CRM, support tickets, and financial data to optimize deals. How to Advance to Level 4:✔ Build a universal communication layer for agents.✔ Implement dynamic agent discovery & governance.✔ Measure ROI via cost savings and revenue impact. Level 4: Multi-Agent Ecosystems What it is: AI teams collaborating across systems with human oversight.Example: Agents processing orders, managing inventory, and routing feedback in real time. Maximizing Value:✔ Strengthen security for ecosystem-wide AI.✔ Develop new business models powered by agent collaboration.✔ Track revenue growth, retention, and operational efficiency. Beyond Technology: Key Implementation Factors “AI success hinges on more than just tech,” notes Ahuja. Organizations must: By addressing these pillars, businesses can accelerate AI adoption—turning experimentation into scalable, measurable value. Contact Tectonic today to harness the power of AI and move along the AI Agent maturity continuum. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Informatica, Agentforce, and Salesforce

Informatica, Agentforce, and Salesforce

Informatica and Salesforce Deepen AI Partnership to Power Smarter Customer Experiences Las Vegas, [May, 2025] – At Informatica World, Informatica (NYSE: INFA) announced an expanded collaboration with Salesforce to integrate its Intelligent Data Management Cloud (IDMC) with Salesforce Agentforce, enabling enterprises to deploy AI agents fueled by trusted, real-time customer data. Bringing Trusted Data to AI-Powered Workflows The integration centers on Informatica’s Master Data Management (MDM), which distills fragmented customer data into unified, accurate “golden records.” These records will enhance Agentforce AI agents—used by sales and service teams—to deliver: “Data is foundational for agentic AI,” said Tyler Carlson, SVP of Business Development at Salesforce. “With Informatica’s MDM, Salesforce customers can ground AI interactions in high-quality data for more targeted service and engagement.” Key Capabilities (Available H2 2025 on Salesforce AppExchange) “This is about action, not just insights,” emphasized Rik Tamm-Daniels, GVP of Strategic Ecosystems at Informatica. “We’re embedding reliable enterprise data directly into Agentforce to drive measurable outcomes.” Why It Matters As AI agents handle more customer interactions, data quality becomes critical. This partnership ensures Agentforce operates on clean, governed data—reducing hallucinations and bias while improving relevance. The MDM SaaS tools for Agentforce will enter pilot testing soon, with general availability slated for late 2025. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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