Agentic AI Archives - gettectonic.com
28Aug

Mastering the AI Agent Revolution

Mastering the AI Agent Revolution: Boomi’s Blueprint for Enterprise Success The AI Imperative: Transform or Fall Behind AI is reshaping business at unprecedented speed – from automating routine tasks to enabling breakthrough innovations. Yet most enterprises struggle to harness its full potential, trapped by what Boomi identifies as “the data problem everyone ignores.” “AI is only as effective as the data foundation it’s built on,” warns Chris Hallenbeck, Boomi’s SVP of AI & Platform. “Without addressing data quality, integration and governance, AI initiatives are doomed to underdeliver.” The Rise of Agentic AI: Opportunity Meets Complexity Agentic AI represents the next evolutionary leap – autonomous digital workers that: “Within two years, we won’t be logging into systems – AI agents will handle everything,” predicts Boomi CEO Steve Lucas. “Enterprises will manage millions of agents, creating unprecedented scale.” But this power comes with profound challenges: The Governance Imperative: Beyond “Nice-to-Have” As AI agents enter production environments, robust governance becomes non-negotiable. Organizations must track:✔ Model versions and approval chains✔ Decision rationale with explainable AI✔ Comprehensive activity logging✔ Confidence scoring for autonomous actions “Auditors will demand full visibility into agent operations,” Hallenbeck emphasizes. “Retrofitting governance is exponentially harder than building it in from the start.” Boomi’s Agent Lifecycle Solution Boomi’s AI Agent Management Platform provides an enterprise-grade framework for agent orchestration: “We’re creating the connective tissue for the agent ecosystem,” explains Lucas. “Our platform unifies fragmented frameworks from Google, Amazon and Microsoft while preventing vendor lock-in.” Building Trust Through Measured Adoption Successful AI integration requires more than technology – it demands organizational trust. Boomi’s proven approach: “Our sales teams achieved 50% productivity lifts using AI agents,” shares CMO Alison Biggan. “When employees see tangible benefits, adoption follows naturally.” The Competitive Divide Enterprises face a stark choice: “The question isn’t whether to adopt agentic AI,” concludes Lucas. “It’s whether your organization has the vision and discipline to do it right.” Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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August 28, 2025in Agentforce Platform, Agentic AI, Data, Enterprise, Generative AI, Google, Salesforce, Salesforce Development Services, Salesforce Optimization, Technology
Agentic AI Race
25Aug

Transforming Business Operations Through Autonomous Intelligence

Understanding Agentic AI Agentic AI represents a paradigm shift in artificial intelligence, moving beyond static automation to dynamic systems capable of independent decision-making and real-time adaptation. Unlike traditional rule-based automation, these AI agents can: According to Thadeous Goodwyn of Booz Allen Hamilton, agentic AI achieves objectives by breaking them into subtasks delegated to specialized AI models. This capability is accelerating rapidly due to advances in large language models and generative AI. 10 Transformative Use Cases of Agentic AI 1. Cybersecurity & Risk Management AI agents are revolutionizing security operations by: 2. Supply Chain Optimization Agentic AI transforms logistics by: 3. Advanced Customer Service Beyond basic chatbots, agentic AI enhances support by: 4. Call Center Automation Modern contact centers leverage agentic AI to: 5. Scientific Discovery & R&D In research applications, AI agents: 6. Defense Logistics Planning Military applications include: 7. Smart Manufacturing Agentic systems streamline production by: 8. Utility Infrastructure Management Energy providers use agentic AI for: 9. Multimedia Content Creation Beyond basic generation, agentic AI: 10. Knowledge Management Modern retrieval systems: Implementation Considerations While 26% of enterprises are actively exploring agentic AI (per Deloitte), adoption requires addressing: The Future of Autonomous Operations As noted by industry experts, agentic AI represents more than incremental improvement – it enables fundamentally new ways of working. Organizations that successfully implement these systems will gain: ✔ Enhanced operational resilience✔ Improved decision velocity✔ Greater process efficiency✔ New competitive advantages The transition requires careful planning but offers transformative potential across virtually every industry sector. As the technology matures, agentic AI will increasingly become the cornerstone of intelligent business operations. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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August 25, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Generative AI, Technology
Qlik’s AI Trust Score
15Aug

Qlik’s AI Trust Score

Qlik’s AI Trust Score: Ensuring Data Integrity for Reliable AI In an era where AI’s success hinges on high-quality data, Qlik has announced the general availability of its AI Trust Score, a groundbreaking feature within the Qlik Talend Cloud platform. Launched in July, the tool empowers organizations to evaluate whether their data is truly prepared to power AI models—before deployment. Why Data Trust Matters in AI AI’s explosive growth has made data reliability a top priority. Poor-quality data leads to hallucinations, bias, and inaccurate outputs—risks that Qlik’s AI Trust Score helps mitigate. “Many enterprises struggle with a fundamental blind spot—not knowing if their data is trustworthy for AI. This tool directly addresses that.”— Mike Leone, Analyst, Enterprise Strategy Group (Omdia) How It Works The AI Trust Score grades data across multiple dimensions, delivering a single, actionable score that reveals:✔ Completeness – Are critical fields missing?✔ Diversity – Is the data representative (to avoid bias)?✔ Timeliness – Is it up-to-date for accurate insights?✔ Discoverability – Can teams easily access and use it? If issues arise, the tool pinpoints breakdowns, allowing fixes before flawed data corrupts AI models. Real-World Impact “Customers told us they had no reliable way to verify if their data was AI-ready. This score changes that.”— Drew Clarke, EVP of Products & Technology, Qlik What’s Next? The Bottom Line With AI adoption accelerating, trust in data is non-negotiable. Qlik’s AI Trust Score provides the missing link—ensuring enterprises build AI on reliable, bias-free, and up-to-date data. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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August 15, 2025in Agentic AI, Data, Enterprise, Technology
Building the Intelligent Enterprise Network
12Aug

Building the Intelligent Enterprise Network

Blueprint for the Agentic AI Era: Building the Intelligent Enterprise Network The Next Frontier: Agentic AI Demands a New Network Paradigm At Cisco Live 2024, company executives unveiled a strategic vision for enterprise AI that goes beyond today’s generative capabilities. As Jeetu Patel, Cisco’s Chief Product Officer, stated: “We’re witnessing one of the most consequential technological shifts in history—the move from reactive AI assistants to autonomous agentic systems that execute complex workflows.” This transition requires fundamental changes to enterprise infrastructure. Where generative AI focused on content creation, agentic AI introduces self-directed software agents that:✅ Operate autonomously across systems✅ Make real-time decisions without human intervention✅ Coordinate multi-step business processes Cisco’s Three Pillars for Agentic AI Success 1. Simplified Network Operations with AI Cisco is unifying its Catalyst and Meraki platforms into a single AI-powered management console featuring: “The future isn’t just AI-assisted ops—it’s agentic ops where AI systems autonomously maintain network health,” noted DJ Sampath, SVP of AI Platform at Cisco. 2. AI-Optimized Hardware Infrastructure New product releases specifically designed for AI workloads:🔹 Catalyst 9800-X Series – 400Gbps switches with AI-optimized ASICs🔹 Silicon One G200 Routers – Built-in NGFW and SD-WAN for distributed AI🔹 Wi-Fi 7 Access Points – 320MHz channels for high-density AI agent traffic 3. Security-Infused Network Fabric Cisco’s “Zero Trust by Design” approach incorporates: Why Networking is AI’s Make-or-Break Factor Patel highlighted a critical insight: “GPUs are only as good as their data pipelines. An idle GPU waiting for packets is like burning cash.” Cisco’s internal benchmarks show: 📉 30% GPU utilization on poorly configured networks📈 92% utilization on Cisco’s AI-optimized infrastructure The difference comes from: The Agentic AI Future: Beyond Hype to Transformation While some dismiss AI as overhyped, Cisco executives argue the true revolution is just beginning: “Agentic AI won’t just answer questions—it will create original insights and solve problems we couldn’t approach before. But this requires rethinking every layer of infrastructure.”— Jeetu Patel, EVP & Chief Product Officer, Cisco Early adopters are already seeing results: Preparing Your Enterprise Cisco recommends three immediate actions: “The companies that win will be those that build networks where AI agents thrive as first-class citizens,” Patel concluded. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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August 12, 2025in Agentic AI, Data, Enterprise, Generative AI
02Aug

Agentforce AI Platform Expands with 200+ Prebuilt Agents

Salesforce has rapidly scaled its Agentforce AI platform, now offering over 200 prebuilt AI agents—a significant leap from the handful available at its October 2024 launch. This expansion addresses a critical challenge for businesses: how to effectively deploy AI automation without extensive technical overhead. Solving the AI Implementation Challenge Enterprises are eager to adopt AI but often struggle with execution. Martin Kihn, SVP of Market Strategy at Salesforce Marketing Cloud, explains: “Customers were excited about AI’s potential but asked, ‘Can I really make this work?’ We took that feedback and built ready-to-use agents that simplify adoption.” Rather than leaving businesses to build AI solutions from scratch, Salesforce’s strategy focuses on preconfigured, customizable agents that accelerate deployment across industries. Proven Business Impact Early adopters of Agentforce are already seeing measurable results: According to Slack’s upcoming Workforce Index, AI agent adoption has surged 233% in six months, with 8,000+ Salesforce clients now using Agentforce. Adam Evans, EVP & GM of Salesforce AI, states: “Agentforce unifies AI, data, and apps into a digital labor platform—helping companies realize agentic AI’s potential today.” Agentforce 3: Scaling AI with Transparency In June 2025, Salesforce launched Agentforce 3, introducing key upgrades for enterprise-scale AI management: Kihn notes: “Most prebuilt agents are a starting point—helping customers overcome hesitation and envision AI’s possibilities.” Once businesses embrace the technology, the use cases become limitless. The Human vs. AI Agent Debate A major challenge for enterprises is how human-like AI agents should appear. Early chatbots attempted to mimic people, but Kihn warns: “Humans excel at detecting non-humans. If an AI pretends to be human, then transfers you to a real agent, it erodes trust.” Salesforce’s Approach: Clarity Over Imitation Kihn illustrates the risk: “Imagine confiding in a ‘sympathetic’ AI agent about a health issue, only to learn it’s not human. That damages trust.” What’s Next for Agentforce? With thousands of AI agents already deployed, Salesforce continues refining the platform. Kihn compares the rapid evolution to “learning to drive an F1 car while racing.” As businesses increasingly adopt AI automation, Agentforce’s library of prebuilt solutions positions Salesforce as a leader in practical, scalable AI deployment. The future? More agents, smarter workflows, and seamless enterprise AI integration. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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August 2, 2025in Agentforce Platform, Agentic AI, Data, Enterprise, Salesforce, Salesforce Marketing Cloud, Technology
26Jul

Agentic AI

Agentic AI: The Next Frontier in Business Transformation The AI Maturity Gap: A Wake-Up Call for Businesses Despite massive investments in AI, only 1% of companies believe they’ve reached full maturity, according to recent data. Even with billions poured into Generative AI, Capgemini reports that just 24% of organizations have scaled it across most functions—meaning 76% are still experimenting without significant impact. Enter Agentic AI—the next evolution in artificial intelligence. Unlike today’s reactive, prompt-dependent AI, Agentic AI systems operate autonomously, making decisions, adapting to changes, and executing workflows with minimal human intervention. These agents combine reasoning with automation, transforming not just customer experience (CX) but also revolutionizing how employees work. From firsthand experience in developing proof-of-concepts (PoCs) for incident management, we’ve seen how Agentic AI enhances employee experience (EX), which in turn drives better customer outcomes. The link between EX and CX has never been stronger—improvements in one directly fuel progress in the other. The Internal Revolution: Elevating Employee Experience Agentic AI shifts from rule-based automation to goal-driven autonomy. These agents learn from outcomes, adapt in real time, and make decisions within defined parameters—freeing employees from repetitive tasks and enabling strategic work. Transforming Incident Management We recently worked with a client to develop an Agentic AI solution for Major Incident Management (MIM)—a critical process where delays can lead to revenue loss and reputational damage. The goal? Reduce root-cause identification and resolution time for high-priority incidents (P1/P2). While full results remain confidential, early indicators show: Technical Gains ✔ Faster detection & response✔ Consistent troubleshooting✔ Preserved institutional knowledge✔ Parallel task processing Efficiency Improvements ✔ Reduced Mean Time to Resolution (MTTR)✔ 24/7 operations without fatigue✔ Automated documentation✔ Optimized human resource allocation Business Impact ✔ Better EX & CX✔ Lower operational costs✔ Reduced risk exposure Beyond Incident Management: Vodafone’s AI Leap Vodafone’s hybrid GenAI strategy is already unlocking efficiencies in network management, with AI agents like VINA enabling autonomous operations. Partnering with Google Cloud, Vodafone uses GenAI for network automation, including image-based site assessments for solar panel installations. Additionally, Vodafone is deploying Agentic AI with ServiceNow to predict and mitigate service disruptions, improving both employee workflows and customer service. The CX Cascade Effect: How Internal AI Elevates Customer Experience When internal processes become smarter and faster, customers reap the benefits—through faster resolutions, proactive support, and seamless service. The Cascade in Action Vodafone’s £140M investment in SuperTOBi (a GenAI-powered chatbot built on Microsoft Azure OpenAI) has cut response times and enhanced answer quality. Meanwhile, AI tools analyzing call success rates are helping create “super agents” who improve with each interaction. Other companies seeing success: This shift toward anticipatory service—where AI predicts issues before they arise—is becoming a competitive necessity. The Future: Orchestrating AI Agents at Scale The next frontier is connecting multiple AI agents across internal and customer-facing workflows, enabling end-to-end automation. A Framework for Orchestration Real-World Success Stories Lessons from the Field: How to Succeed with Agentic AI While enthusiasm is high, most companies struggle to extract real business value from GenAI. Agentic AI requires a new mindset. Here’s what works: ✅ Start with well-defined processes (high-volume, measurable tasks)✅ Maintain human oversight (security, compliance, risk mitigation)✅ Prioritize change management (training, communication, overcoming resistance)✅ Build governance frameworks (role-based access, audit trails) Preparing for the Agentic Future: Strategy Over Scale Agentic AI adoption is accelerating fast (Slack reports 233% growth in AI usage in six months). Companies must act strategically: 🔹 Pilot First: Vodafone & Google Cloud’s 2024 hackathon generated 13 real-world use cases—proving rapid experimentation works.🔹 Invest in Platform Capabilities: Pre-built agent skills speed deployment.🔹 Focus on Business Outcomes: This is not just efficiency—it’s transformation. Some firms are even exploring “zero-FTE” departments (fully AI-operated). But the real opportunity lies in human-AI collaboration, not replacement. Final Thoughts: The Competitive Edge Goes to Early Movers Agentic AI isn’t just an incremental upgrade—it’s a paradigm shift toward autonomous, intelligent workflows. Companies that adopt early will outperform competitors in both employee productivity and customer satisfaction. The future isn’t about managing AI—it’s about collaborating with AI agents that think, act, and optimize in real time. The Choice Is Yours: Lead or Follow? The Agentic AI revolution has begun. Will your organization pioneer the change—or play catch-up? Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 26, 2025in Agentic AI, AI Tools, Artificial Intelligence, Data, Generative AI, Google, Technology
Agentic AI: The Next Frontier in Intelligent Automation
21Jul

Agentic AI Revolution in Customer Service

The Agentic AI Revolution in Customer Service: Lessons from Salesforce’s Million-Interaction Milestone From Chatbot Frustration to AI Partnership The agentic AI arms race has exploded onto the customer service scene in less than a year, with Salesforce emerging as a pioneer by deploying its Agentforce solution across its help portal. The results? Over 1 million customer interactions handled – and counting. But as Salesforce’s journey reveals, success with AI agents requires more than just advanced technology—it demands a fundamental shift in customer service philosophy. Breaking the “Deflection” Mindset Bernard Slowey, SVP of Digital Customer Success at Salesforce, calls out the industry’s problematic approach: “That word ‘deflection’ breaks my heart. When companies focus on driving out costs by keeping customers away from humans, they make stupid decisions.” Unlike traditional chatbots designed as “first line of defense,” Agentforce was built to:✔ Accelerate resolutions through intelligent assistance✔ Maintain human availability when needed✔ Enhance rather than replace the service experience Key Lessons from a Million Conversations 1. The Heart Matters as Much as the Brain Early versions focused on factual accuracy but lacked emotional intelligence. Salesforce: Result: Abandonment rates dropped from 26% to 8-9% 2. The Content Imperative Agent performance depends entirely on data quality. Salesforce encountered: 3. Knowing When to Step Aside The system now: The Human-AI Balance Sheet Metric Before Agentforce After Optimization Customer Abandonment 26% 8-9% Human Handoff Rate 1% 5-8% Support Engineer Capacity Static Reallocated to higher-value work The Road Ahead for Agentic AI As Slowey notes: “AI does some things amazingly well; it doesn’t create relationships. We’re entering an era of digital and human collaboration.” For companies ready to move beyond the chatbot dark ages, Salesforce’s million-interaction milestone proves agentic AI can work—when implemented with both technological rigor and human-centric design. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 21, 2025in Agentforce Platform, Agentic AI, Data, Salesforce, Technology
Agentforce 3 and AI Agents
21Jul

Agentforce 3 to Accelerate Agentic AI Adoption

Salesforce Launches Agentforce 3 to Accelerate Agentic AI Adoption A few weeks ago, Salesforce introduced Agentforce 3, designed to deliver rapid time-to-value and address ROI concerns around agentic AI. As the technology rapidly evolves, Salesforce is leading the charge into the agent-first Service era, betting big on Agentforce’s potential to transform customer service by proactively resolving issues and educating users on new features. Salesforce customer 1-800 Accountant is already seeing the benefits, reporting measurable improvements in customer service efficiency. Here’s what both companies had to say. Customer Zero: Salesforce’s Own Agentforce Journey As its own first customer, Salesforce has a vested interest in ensuring Agentforce enhances its customer service operations. Bernard Slowey, SVP of Digital Customer Success, shared insights with analysts, noting that most self-service journeys for Salesforce customers begin on Google before landing on the company’s Help portal, which handles 2 million reactive support cases annually. Slowey posed a key question: “What if your service team had infinite capacity and complete knowledge?” To move toward this vision, Salesforce is deploying AI agents to absorb repetitive tasks, proactively engage customers, and seamlessly hand off complex issues to humans when needed. By July, Agentforce had already facilitated 1 million customer conversations with an 85% resolution rate. Early results show a 2% increase in Help portal traffic alongside a 5% reduction in case volume, signaling strong ROI. Salesforce tracks performance via scorecards comparing AI and human agents, ensuring smooth transitions when escalations are necessary. So far, customers aren’t frustrated when an AI agent can’t resolve an issue—validating the hybrid approach. Andy White, SVP of Business Technology, highlighted lessons from the rollout: Looking ahead, White emphasized Agentforce’s advantage over public LLMs: “We know who the customer is and can engage them proactively—before they even reach the portal.” For businesses starting their agentic AI journey, White advises: “Begin with a small, controlled use case—like a single customer service topic—before scaling.” 1-800 Accountant: Transforming Tax Season with Agentforce Ryan Teeples, CTO of 1-800 Accountant, shared how the firm—the largest U.S. accounting provider for small businesses—deployed Agentforce to handle high-volume, time-sensitive client queries during tax season. With a long-standing focus on automation, 1-800 Accountant saw agentic AI as the next logical step. Teeples explained: “Our accountants often lack time for client nurturing. Agentforce lets us automate communications while freeing them to focus on high-value advisory work.” Key outcomes: Employee reactions were mixed, but leadership emphasized that AI complements accountants by handling soft skills and routine tasks, allowing them to focus on deep expertise. ROI is clear—saved accountant hours translate directly into cost savings. Retention impact will be measured next tax season. Why It Matters:Agentic AI is proving its value in real-world customer service, with Salesforce and 1-800 Accountant demonstrating tangible efficiency gains, cost savings, and improved experiences. The key? Start small, measure rigorously, and keep humans in the loop. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 21, 2025in Agentic AI, Data, Google, Salesforce, Technology
AWS Salesforce
18Jul

AWS Unveils New Agent-Based AI Tools

AWS Unveils New Agent-Based AI Tools, Doubles Down on Developer-Focused Innovation At the AWS Summit New York City 2025, Amazon Web Services (AWS) announced a suite of new agent-based AI tools, reinforcing its commitment to agentic AI—a paradigm shift where AI systems not only generate responses but autonomously take actions. Key Announcements: Why Agentic AI? AWS believes agentic AI is transforming technology by enabling hyper-automation—where AI doesn’t just analyze or summarize but acts on behalf of users. To accelerate adoption, AWS is investing an additional 0M in its Generative AI Innovation Center. “The goal is to help organizations move beyond generative AI to AI that can take action,” said Taimur Rashid, AWS Managing Director of Generative AI Innovation. Industry Reactions: A Developer-First Approach Analysts note AWS is targeting enterprise developers with advanced tooling, differentiating itself from low-code platforms like Salesforce. However, Mark Beccue (Omdia) cautions:“AWS risks missing buyers by focusing too narrowly on developers. They need a clearer end-to-end story.” Partner Perspective: Solving Real-World AI Challenges John Balsavage (A&I Solutions Inc.), an AWS partner, highlights AgentCore Observability as critical for improving AI agent accuracy:“90% accuracy isn’t enough—we need full traceability to reach 100%.” He also praised Kiro, AWS’s new agentic IDE, for simplifying AI prompting:“It generates better requirements, helping developers build more effectively.” AWS Marketplace Expansion & New Integrations AWS also launched: Challenges Ahead While AWS aims to simplify AI development, analysts question: “AWS is trying to be the middle ground between raw AI tools and fully packaged solutions,” said Andersen. “Execution will be key.” The Bottom Line AWS is betting big on agentic AI, arming developers with powerful tools—but success hinges on bridging the gap between technical capability and business impact. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 18, 2025in Agentic AI, AI Tools, Data, Enterprise, Generative AI, Google, Salesforce, Technology
AWS Salesforce
17Jul

AWS Doubles Down on Agentic AI with New Developer Tools at NYC Summit

At its AWS Summit New York City 2025 conference, Amazon Web Services unveiled a comprehensive suite of agent-based AI tools, signaling its strategic bet on what it calls “the next fundamental shift in enterprise AI.” Core Offerings: Building Blocks for Agentic Systems The cloud leader introduced Amazon Bedrock AgentCore, now in preview, which provides seven foundational services for deploying AI agents at scale: “This represents a step function change in what’s possible for AI agents,” said Swami Sivasubramanian, AWS VP for Agentic AI, during his keynote. The suite supports any AI framework or model while addressing critical enterprise requirements around security and scalability. Complementary AI Infrastructure Updates AWS also announced: The company is backing these technical investments with an additional $100 million for its Generative AI Innovation Center, focusing on hyperautomation use cases. Developer-Centric Approach Faces Mixed Reactions Analysts note AWS’s strategy differs from competitors by targeting professional developers rather than citizen developers: “It’s geared toward the hardcore professional developer,” said Jason Andersen of Moor Insights & Strategy, contrasting AWS’s CLI-heavy approach with Salesforce’s low-code solutions. However, Omdia’s Mark Beccue cautioned: “When talking about agents, you must have the complete story.” He suggested the developer focus might overlook key decision-makers. Ecosystem Expansion Notable ecosystem developments include: Early adopters like A&I Solutions President John Balsavage highlight observability tools as critical for improving agent accuracy beyond current 90% benchmarks. Challenges Ahead While AWS aims to simplify complex AI orchestration, analysts question whether it can: The summit also revealed AWS Academy is providing free certification exam vouchers to over 6,600 students, potentially growing its AI-skilled workforce. Meanwhile, Anthropic (an AWS partner) launched new analytics for its Claude Code assistant. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 17, 2025in Agentic AI, AI Tools, Data, Enterprise, Generative AI, Google, Salesforce
They're Here - Agentic AI Agents
12Jul

They’re Here – Agentic AI Agents

AI Isn’t Coming—It’s Already Here. Is Your Business Keeping Up? The race to harness artificial intelligence isn’t some distant future challenge—it’s happening right now. Companies leveraging AI are pulling ahead, automating decisions, personalizing customer experiences, and unlocking efficiencies that competitors can’t match. But before jumping on the bandwagon, leaders need to ask a hard question: Is my organization actually prepared for AI, or are we setting ourselves up for failure? An AI Maturity Assessment isn’t just a buzzword—it’s a reality check. It reveals where you stand, what’s missing, and how to bridge the gap between ambition and real-world results. Why Skipping the Assessment Is a Costly Mistake Too many businesses dive into AI without proper groundwork, leading to: Mature AI adoption isn’t about buying the latest tech—it’s about aligning strategy, data, people, and governance to make AI work for you, not against you. The Five Make-or-Break Factors of AI Success Where Do You Stand? AI maturity isn’t about being perfect—it’s about being honest. Most companies fall into one of four stages: The goal? Move forward with clarity—not guesswork. How We Help You Win with AI At Tectonic, we cut through the noise. Our approach isn’t about selling tools—it’s about making AI work in the real world. We help you: The Bottom Line AI isn’t magic—it’s a tool. And like any tool, it’s only as good as the hands wielding it. Before you invest another dollar in AI, ask yourself: Do we really know what we’re doing? If the answer isn’t a confident “yes,” it’s time for a reality check. Let’s talk. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 12, 2025in Agentic AI, Artificial Intelligence, Data
Denodo’s DeepQuery Takes Generative AI Beyond Basic Facts to Advanced Data Analysis
09Jul

Denodo’s DeepQuery Takes Generative AI Beyond Basic Facts to Advanced Data Analysis

Palo Alto, Calif. – Denodo, a leader in data virtualization and metadata management, has unveiled DeepQuery, a new AI-powered tool designed to move beyond simple fact retrieval and enable complex, multi-source data analysis with generative AI. Currently in private preview, DeepQuery will soon be part of the Denodo AI SDK, offering users deeper insights into why trends occur—not just what happened. Beyond Basic AI Queries: The Need for Deep Analysis Over the past two years, many data management vendors have introduced natural language querying—allowing users to ask questions of their data without SQL. However, most tools stop at surface-level answers, providing summaries or basic facts rather than explaining causality or recommending next steps. Pablo Alvarez, Denodo’s VP of Product Management, explains: “Existing GenAI tools help users get quick answers, but decision-makers need more—they need to understand why something happened and what to do next. DeepQuery is built for exactly that.” How DeepQuery Works Unlike traditional retrieval-augmented generation (RAG) systems—which retrieve and rephrase existing data—DeepQuery:✅ Pulls real-time data from multiple internal and external sources (e.g., ERP, CRM, market data).✅ Performs multi-hop reasoning to uncover hidden patterns (e.g., “Why did Q2 2025 sales drop vs. Q2 2024?”).✅ Delivers explainable insights with AI-generated narratives, not just raw numbers. Michael Ni, Analyst at Constellation Research, notes: “Denodo’s virtualization strength allows it to synthesize and reason across all governed enterprise data in real time—a unique advantage over BI tools and public LLMs.” Competitive Edge in a Fast-Moving Market While DeepQuery could differentiate Denodo from rivals like AtScale, Datameer, and Snowflake, analysts caution that AI innovation moves quickly. William McKnight, President of McKnight Consulting Group, suggests: “To stay ahead, Denodo should explore agentic AI—automating data integration, improving governance, and enabling predictive analytics.” The Road Ahead: AI Governance & Decision Orchestration Denodo is also embracing Model Context Protocol (MCP), an open standard for LLM-data interactions (joining Databricks and Snowflake). Future focus areas include:🔹 AI governance & security🔹 Semantic consistency across hybrid data🔹 Expanding into AI-driven decision orchestration Alvarez emphasizes: “High-impact decisions require analyzing data across systems, apps, and third-party sources—not just in isolated lakehouses.” The Bottom Line DeepQuery marks a shift from passive AI assistants to active analytical partners. If Denodo continues evolving—integrating agentic workflows and predictive analytics—it could cement its role as a next-gen data intelligence layer. Now in private preview, DeepQuery is set to redefine how enterprises extract value from their data. Will your organization be ready? Key Takeaways:✔ DeepQuery goes beyond fact retrieval to deliver causal insights & recommendations.✔ Real-time, multi-source analysis sets it apart from traditional BI and RAG systems.✔ Future-proofing requires deeper AI integration—agentic automation, predictive analytics, and governance. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 9, 2025in Agentic AI, AI Tools, Data, Enterprise, Generative AI, Snowflake
The Next Frontier in Government Efficiency
08Jul

The Next Frontier in Government Efficiency

Agentic AI: The Next Frontier in Government Efficiency As federal agencies face mounting pressure to streamline operations and reduce costs, AI-powered automation is emerging as a critical solution—and Salesforce is leading the charge. With its newly secured FedRAMP High authorization for Agentforce, Salesforce now enables civilian agencies handling sensitive data to deploy AI agents that automate complex workflows while maintaining strict compliance. Why This Matters Now The Department of Government Efficiency (DOGE) is aggressively pursuing cost-cutting measures, including workforce reductions—making AI-driven automation a strategic imperative. “Agencies are asking us, ‘Can you build a digital agent to solve this problem?’” says Paul Tatum, head of Salesforce’s Global Public Sector Solutions Engineering. “Their teams are doing incredible work, but they’re stretched thin.” How AI Agents Transform Government Workflows Salesforce’s AI agents specialize in decision-making support, particularly in high-stakes adjudication processes—such as:✔ Benefits approvals✔ Payment processing✔ Service request evaluations “Government policies are dense, complex, and constantly updated,” Tatum explains. “AI agents excel at parsing these rules and providing real-time recommendations—freeing up staff to focus on final decisions.” The Federal AI Copilot Model Rather than replacing humans, these AI agents act as intelligent assistants: Government Readiness for Agentic AI Federal agencies are uniquely positioned for AI adoption because:🔹 Data is well-structured & clean🔹 Use cases are clearly defined🔹 Documentation is thorough “The government is primed for this,” says Tatum. “AI will make agencies faster, more efficient, and more responsive to citizens.” A Competitive AI Landscape Salesforce isn’t alone in this space—Amazon, Google, and ServiceNow have also secured FedRAMP approvals for their AI agents. But with its deep federal footprint and seamless integration into existing Salesforce environments, Agentforce is positioned to be the game-changer. What’s Next? Salesforce is currently running demos and proofs of concept with multiple agencies. As AI adoption accelerates, one thing is clear: The future of government efficiency is automated, intelligent, and powered by AI. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 8, 2025in Agentic AI, Data, Google, Public Sector Solutions, Salesforce
Intelligent Adoption Framework
08Jul

Exploring Open-Source Agentic AI Frameworks

Exploring Open-Source Agentic AI Frameworks: A Comparative Overview Most developers have heard of CrewAI and AutoGen, but fewer realize there are dozens of open-source agentic frameworks available—many released just in the past year. To understand how these frameworks work and how easy they are to use, several of the more popular options were briefly tested. This article explores what each one offers, comparing them to the more established CrewAI and AutoGen. The focus is on LangGraph, Agno, SmolAgents, Mastra, PydanticAI, and Atomic Agents, examining their features, design choices, and underlying philosophies. What Agentic AI Entails Agentic AI revolves around building systems that enable large language models (LLMs) to access accurate knowledge, process data, and take action. Essentially, it uses natural language to automate tasks and workflows. While natural language processing (NLP) for automation isn’t new, the key advancement is the level of autonomy now possible. LLMs can handle ambiguity, make dynamic decisions, and adapt to unstructured tasks—capabilities that were previously limited. However, just because LLMs understand language doesn’t mean they inherently grasp user intent or execute tasks reliably. This is where engineering comes into play—ensuring systems function predictably. For those new to the concept, deeper explanations of Agentic AI can be found here and here. The Role of Frameworks At their very core, agentic frameworks assist with prompt engineering and data routing to and from LLMs. They also provide abstractions that simplify development. Without a framework, developers would manually define system prompts, instructing the LLM to return structured responses (e.g., API calls to execute). The framework then parses these responses and routes them to the appropriate tools. Frameworks typically help in two ways: Additionally, they may assist with: However, some argue that full frameworks can be overkill. If an LLM misuses a tool or the system breaks, debugging becomes difficult due to abstraction layers. Switching models can also be problematic if prompts are tailored to a specific one. This is why some developers end up customizing framework components—such as create_react_agent in LangGraph—for finer control. Popular Frameworks The most well-known frameworks are CrewAI and AutoGen: LangGraph, while less mainstream, is a powerful choice for developers. It uses a graph-based approach, where nodes represent agents or workflows connected via edges. Unlike AutoGen, it emphasizes structured control over agent behavior, making it better suited for deterministic workflows. That said, some criticize LangGraph for overly complex abstractions and a steep learning curve. Emerging Frameworks Several newer frameworks are gaining traction: Common Features Most frameworks share core functionalities: Key Differences Frameworks vary in several areas: Abstraction vs. Control Frameworks differ in abstraction levels and developer control: They also vary in agent autonomy: Developer Experience Debugging challenges exist: Final Thoughts The best way to learn is to experiment. While this overview highlights key differences, factors like enterprise scalability and operational robustness require deeper evaluation. Some developers argue that agent frameworks introduce unnecessary complexity compared to raw SDK usage. However, for those building structured AI systems, these tools offer valuable scaffolding—if chosen wisely. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 8, 2025in Agentic AI, Data, Enterprise
The AI Adoption Paradox
07Jul

Dining and Virtual AI

Restaurants are increasingly adopting AI virtual assistants and bots to manage routine tasks like menu inquiries, loyalty program questions, and order tracking, allowing human staff to focus on complex service needs. Platforms like Salesforce Agentforce and Microsoft Copilot are integrated into customer-facing chat systems, apps, and call centers, handling common queries, updating loyalty credits, tracking deliveries, and escalating issues or creating internal tickets without human intervention. Some operators report a 50% reduction in simple inquiries, with guest satisfaction improving due to faster, consistent responses. Salesforce Agentforce, built on Service Cloud and Data Cloud, delivers a conversational concierge experience by analyzing customer history—past orders, loyalty status, and open cases—to provide instant answers or flag issues. For example, ezCater uses Agentforce for natural language order creation, while OpenTable scales global customer support, reducing reliance on human agents for basic tasks. Beyond chatbots, AI-powered operational tools are transforming restaurant efficiency. Computer vision systems, powered by platforms like NVIDIA NIM, Ultralytics, and Viso Suite, monitor dining areas, kitchens, and back-of-house spaces in real time. These systems actively analyze footage, detecting uncleared tables, long lines, or understaffed zones, and alerting staff to act—speeding up table turns and reducing wait times. In fast-casual settings, vision tools manage order queues and crowded pickup areas. In back-of-house, AI vision ensures food safety and equipment compliance, flagging open cooler doors or blocked pathways with automated alerts to managers or centralized teams. These systems reduce reliance on manual checks with real-time anomaly detection, integrating with facility management and workforce platforms for a cohesive response. Future applications could include predictive maintenance, labor forecasting based on traffic patterns, and training gap identification. As edge AI and APIs evolve, smart vision systems are becoming critical restaurant infrastructure. Smartbridge reports a global restaurant group processed over 6 million guest surveys using an Azure-based generative AI tool, automating sentiment analysis, ticket organization, and feedback summaries at scale. This helps chains quickly identify complaints and menu improvement opportunities. Behind the scenes, integrations rely on edge/cloud orchestration and API frameworks. Customer queries route through secure chat interfaces to Agentforce, pulling from CRM or ticket logs, while camera and sensor data feed into AI pipelines on AWS, Azure, or NVIDIA Jetson devices, triggering alerts in Slack, Jira, or ServiceNow. This enables instant responses to issues like spills, tech glitches, or guest requests without human triage. These virtual assistants form an invisible team, handling thousands of queries, freeing staff for hospitality, and moving restaurants toward “agentic AI” that proactively flags issues, prepares for busy periods, and manages inventory shortages. Virtual assistants are no longer just chatbots—they’re essential team members, enhancing operational efficiency, service consistency, and satisfaction for both customers and staff. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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July 7, 2025in Agentforce Platform, Agentic AI, Data, Generative AI, Salesforce

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