PHAs 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
When Will Quantum Computing Be Ready?
20Aug

Post-Quantum Cryptography (PQC)

Post-Quantum Cryptography (PQC): Securing the Future Against Quantum Threats What is PQC?Post-quantum cryptography (PQC) is a new generation of cryptographic algorithms designed to withstand attacks from both quantum and classical computers. Unlike traditional cryptography, which may be vulnerable to quantum computing breakthroughs, PQC relies on mathematical problems that remain hard to solve even with quantum algorithms. Key Objectives How PQC WorksPQC algorithms are built on complex mathematical challenges—such as lattice-based, hash-based, or multivariate cryptography—that are currently resistant to both classical and quantum computational methods. PQC vs. Quantum CryptographyWhile both aim to counter quantum threats, they differ in approach: NIST Standards & Industry AdoptionThe National Institute of Standards and Technology (NIST) has finalized PQC standards, including: Google, a pioneer in PQC adoption, has protected internal communications with PQC since 2022 and began testing it in Chrome as early as 2016. Preparing for the PQC TransitionOrganizations can take proactive steps: By adopting PQC now, businesses can future-proof their security against the quantum era. 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 20, 2025in Data, Google, Technology
Salesforce Flow Builder
28Jul

The Complete Guide to Migrating from Workflow Rules & Process Builder to Salesforce Flow

The End of an Era: Why Salesforce is Consolidating Automation Tools Salesforce has officially announced the retirement of Workflow Rules and Process Builder, marking a pivotal shift in platform automation. After Spring ’25: This consolidation addresses long-standing challenges: Why Flow is the Undisputed Future Salesforce Flow represents a quantum leap in automation capabilities: Capability Workflow Process Builder Flow Visual Designer ❌ ✔️ ✔️ Multi-Step Logic ❌ ✔️ ✔️ User Screens ❌ ❌ ✔️ External Integrations ❌ ❌ ✔️ Error Handling ❌ Limited ✔️ Scheduled Actions Basic ✔️ Advanced Reusable Components ❌ Limited ✔️ Key Advantages of Flow: Urgent Action Required: Migration Timeline Critical Milestones Risks of Delaying Migration Proven Migration Methodology Phase 1: Discovery & Assessment Phase 2: Design & Build Phase 3: Testing & Deployment Common Migration Pitfalls & Solutions Challenge Solution Logic gaps Comprehensive test cases covering edge conditions Performance issues Optimize with bulkification patterns Null handling differences Explicit null checks in flow logic Trigger order conflicts Use Flow Trigger Orchestration Pro Tip: The Migrate to Flow tool handles ~70% of use cases—plan to manually rebuild complex logic. Strategic Considerations Getting Help For organizations needing support: Critical Decision Point: Organizations with 50+ automations should consider engaging Salesforce-certified partners to accelerate migration while minimizing risk. The Bottom Line This transition represents more than just a technical change—it’s a strategic opportunity to modernize your automation foundation. By migrating to Flow now, organizations can: ✔ Eliminate technical debt✔ Unlock advanced capabilities✔ Future-proof their Salesforce investment✔ Position for AI and next-gen automation The clock is ticking—start your migration journey today to ensure a smooth transition before the sunset deadline. 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 28, 2025in Enterprise, Salesforce, Salesforce Flow
Machine-Augmented World
25Jul

Machine-Augmented World

A machine-augmented world, in the context of Salesforce, refers to a future where technology, particularly Augmented Reality (AR) and Artificial Intelligence (AI) and Machine Learning (ML), enhances and expands human capabilities and interactions, especially within the Salesforce ecosystem.  Here’s how Salesforce is embracing the “machine-augmented world”: Essentially, Salesforce’s approach to the machine-augmented world is centered on leveraging technology to enhance human capabilities and interactions within the CRM platform, leading to: 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 25, 2025in Artificial Intelligence, Data, Salesforce, Technology
Ensuring Trust in AI Agent Deployment
24Jul

Ensuring Trust in AI Agent Deployment

Ensuring Trust in AI Agent Deployment: A Secure Approach to Business Transformation The Imperative for Trustworthy AI Agents AI agents powered by platforms like Agentforce represent a significant advancement in business automation, offering capabilities ranging from enhanced customer service to intelligent employee assistance. However, organizations face a critical challenge in adopting this technology: establishing sufficient trust to deploy AI agents with sensitive data and core business operations. Recent industry research highlights prevalent concerns: Salesforce has maintained trust as its foundational value throughout its 25-year history, adapting this principle across technological evolutions from cloud computing to generative AI. The company now applies this same rigorous approach to AI agent deployment through a comprehensive trust framework. The Four Essential Components of Trusted AI Implementation 1. Comprehensive Data Governance Framework The reliability of AI agents depends fundamentally on data quality and security. The Salesforce platform addresses this through: Data Protection Systems Advanced Data Management Industry experts emphasize that robust AI systems require equally robust data foundations. 2. Secure Integration Architecture AI agents require safe interaction channels with other systems: 3. Built-in Development Safeguards The platform incorporates multiple layers of protection throughout the AI lifecycle: 4. Proprietary Trust Layer A specialized security interface between users and large language models offers: Case Study: Healthcare Transformation with Precina Precina’s implementation demonstrates the platform’s capabilities in a regulated environment. By unifying patient records through Agentforce while maintaining HIPAA compliance, the organization achieved: Precina’s CTO noted that Salesforce’s cybersecurity standards enabled trust equivalent to their own care standards when handling patient information. Enterprise AI: Balancing Innovation and Responsibility Salesforce leadership emphasizes that the company’s quarter-century of experience in secure solutions uniquely positions it to guide enterprises through AI adoption. The integration of unified data management, intuitive development tools, and embedded governance enables organizations to deploy AI solutions that are both transformative and responsible. The recommended implementation approach includes: In the evolving landscape of enterprise AI, Salesforce positions trust not just as a corporate value but as a critical competitive differentiator for organizations adopting these technologies. 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 24, 2025in Cloud Computing, Data, Enterprise, Generative AI, Mulesoft, Salesforce, Technology
Autonomous Agents on the Agentforce Platform
23Jul

InsideTrack Joins Salesforce Accelerator to Develop AI Tools for Student Success

Student success nonprofit InsideTrack has partnered with Salesforce Accelerator – Agents for Impact, an initiative that provides nonprofits with technology, funding, and expertise to build AI-powered solutions. Over the next two years, InsideTrack will receive $333,000 in funding and in-kind technology services to create an AI-driven tool designed to enhance the work of student success coaches. Student success coaches are professionals who provide support and guidance to students, helping them navigate academic and personal challenges to achieve their goals. They offer a more holistic approach than academic advisors, focusing on areas like time management, study skills, and goal setting, while also addressing non-academic barriers to success.  Key Roles and Responsibilities: Distinction from Academic Advisors: While academic advisors focus on course selection and degree requirements, success coaches take a broader view, addressing the multifaceted needs of students. They help students develop the skills and strategies to succeed in all areas of their lives, not just academics. Benefits of Success Coaching: Where to Find Student Success Coaches: This new solution will help coaches analyze unstructured data—such as session notes—to identify trends, generate summaries, and recommend next steps, enabling them to support more students effectively. InsideTrack, which assists over 200,000 learners annually through 2.2 million coaching interactions, aims to use AI to streamline reporting and provide deeper insights while preserving the human connections vital to student success. “AI adoption must support—not erode—the relationships that drive student success,” said Ruth Bauer, President of InsideTrack. “By centering this work on the experiences of students and coaches, we’re developing human-centered tools that expand capacity and help learners achieve their goals.” Ron Smith, Salesforce’s VP of Philanthropy, emphasized that “AI should enhance human connection, not replace it,” ensuring ethical and responsible integration in higher education. Dr. Tim Renick of Georgia State University, an InsideTrack advisor, added: “We need tools that empower frontline staff to act quickly on insights and provide meaningful support—because knowing who needs help is only the first step.” The initiative reflects a growing effort to leverage AI for scalable, equitable student support while maintaining the personal engagement that drives long-term success. 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 23, 2025in Agentforce Platform, AI Tools, 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
Role of Trusted Data in AI Success
18Jul

AI Revolutionizes Telemedicine

AI Revolutionizes Telemedicine: Transforming Virtual Care Delivery The Rapid Adoption of AI in Healthcare The healthcare industry is experiencing an AI transformation, with physician adoption rates skyrocketing from 38% in 2023 to 66% in 2024, according to the American Medical Association. Telemedicine—remote healthcare delivered via telecommunications—has emerged as a prime beneficiary of AI innovation. Market analysts project 26% annual growth in AI telemedicine investments, surpassing $156 billion by 2033. “AI is enabling earlier and more frequent medical interventions, often preventing hospitalizations,” said Dr. Elizabeth Krupinski, Director of the Southwest Telehealth Resource Center and Professor at Emory University. “We’re seeing AI enhance both the quality and accessibility of virtual care.” Key AI Applications Reshaping Telemedicine 1. Virtual Health Assistants & Chatbots 2. Intelligent Triage & Symptom Analysis 3. Medical Imaging & Diagnostics 4. Personalized Treatment Planning 5. Remote Patient Monitoring 6. Mental Health Support Operational & Administrative Benefits Challenges & Considerations While promising, AI adoption presents hurdles: The Future of AI in Telemedicine Industry experts anticipate groundbreaking advancements: “We’re still in the early stages,” notes Krupinski. “The next decade will reveal AI’s full potential to improve outcomes while making healthcare more accessible and efficient.” As adoption grows, maintaining rigorous oversight will be crucial to ensure AI systems remain accurate, equitable, and patient-centered. The transformation of telemedicine through AI represents not just technological progress, but a fundamental shift toward more proactive, personalized, and preventive care. 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 AI Chatbots, 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
Preparing for Salesforce's Permission Set Revolution
09Jul

Preparing for Salesforce’s Permission Set Revolution

Tectonic Shift: Preparing for Salesforce’s Permission Set Revolution The Future of Salesforce Access Management Salesforce is fundamentally transforming how enterprises manage user permissions. By Spring 2026, the platform will begin phasing out profile-based permissions in favor of a permission set-centric model—a tectonic shift in access governance that demands strategic preparation. This evolution presents both challenges and opportunities: Why Salesforce is Making This Change Legacy profile-based permissions have become unsustainable for modern enterprises, creating: The new permission set model delivers:✔ Modular, role-based access controls✔ Reduced management overhead✔ Enhanced audit capabilities✔ Dynamic alignment with business needs Note: Some profile functionality (login hours, page layouts) will remain, but core object/field permissions will migrate to permission sets. Tectonic’s Proven Transition Framework As a leader in Salesforce transformations, Tectonic has developed a comprehensive approach to permission set migration: 1. Strategic Assessment 2. Intelligent Design 3. Automated Deployment 4. Organizational Enablement Beyond Compliance: Strategic Advantages This transition represents more than a technical requirement—it’s an opportunity to: Building Your Transition Team The permission set revolution will reshape Salesforce talent needs. Tectonic offers dual solutions: 1. Expert Consultants 2. Managed Services Why Partner with Tectonic? Prepare for the Shift The clock is ticking toward Spring 2026. Organizations that start their transition now will: Ready to transform your access management strategy? Tectonic’s Salesforce experts can guide your organization through every phase of this critical transition. 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 Enterprise, 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
PepsiCo Pioneers Enterprise AI with Salesforce Agentforce
03Jul

PepsiCo Pioneers Enterprise AI with Salesforce Agentforce

A Global First: PepsiCo Deploys Salesforce Agentforce at Scale PepsiCo has made history as the first major food and beverage company to implement Salesforce Agentforce AI agents across its global operations. This landmark partnership signals a transformative shift in how enterprises leverage AI for customer engagement, sales, and supply chain optimization. The announcement follows Salesforce’s Agentforce World Tour, where demonstrations in Tel Aviv, London, Zurich, Seoul, and Melbourne drew thousands of business leaders eager to explore AI’s potential. Now, with PepsiCo’s adoption, Agentforce moves from concept to real-world enterprise deployment. Why PepsiCo Chose Agentforce PepsiCo—a $92 billion market leader—isn’t just experimenting with AI; it’s reinventing its operations. The company will deploy Agentforce across: ✅ Customer Support – AI-powered, hyper-personalized interactions✅ Sales Optimization – Real-time inventory insights via Consumer Goods Cloud✅ Data-Driven Decision Making – Unified customer profiles via Salesforce Data Cloud Ramon Laguarta, PepsiCo Chairman & CEO, explains: “AI is reshaping our business in ways that were once unimaginable. This collaboration unlocks smarter decision-making, fuels innovation, and powers sustainable growth.” The AI + Human Collaboration Model Salesforce and PepsiCo emphasize augmentation over automation—where AI agents enhance, not replace, human roles. Marc Benioff, Salesforce CEO, highlights the vision: “PepsiCo is reimagining work by uniting human expertise with AI intelligence. This is the future of digital labor.” Athina Kanioura, PepsiCo’s Chief Strategy Officer, adds: With Agentforce, we’re building an enterprise where humans and AI collaborate—driving efficiency, resilience, and readiness for the future.” Addressing AI’s Impact on Jobs At the London Agentforce Tour, Zahra Bahrololoumi (Salesforce UK & Ireland CEO) clarified: “Our goal is to boost human productivity, not eliminate jobs. Some tasks are best handled by AI, others require human judgment.” A Blueprint for Enterprise AI Adoption PepsiCo’s deployment is a watershed moment for AI in consumer goods: 🔹 Scale: Impacts billions of daily product interactions across 200+ countries🔹 Integration: Combines Data Cloud, Consumer Goods Cloud, and Agentforce AI🔹 Innovation: Moves beyond automation to AI-driven decision intelligence What’s Next? If successful, PepsiCo’s implementation could accelerate global AI adoption—proving that enterprise-ready AI isn’t just theoretical. The Bigger Picture: AI’s Role in the Future of Business PepsiCo’s bold move underscores a critical shift: Will your business be next? 📈 Explore how Agentforce can transform your operations – Contact Salesforce AI Experts 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 3, 2025in Agentforce Platform, Agentic AI, Atlas Reasoning Engine, Data, Enterprise, Salesforce, Salesforce Data Cloud, Technology
The Fragmented World of AI Agents and the Path to True Interoperability
02Jul

Navigating the AI Revolution as a Product Designer

The AI landscape is evolving at a breakneck pace, leaving many designers grappling with both its potential and its disruptions. Anthropic’s CEO warns that AI could displace up to 50% of entry-level white-collar jobs, while Zapier’s CEO emphasizes hiring for AI fluency. Meanwhile, new roles like “model designer” are emerging, and the industry is shifting toward super IC (individual contributor) roles. For product designers, the challenge isn’t just staying relevant—it’s continuing to grow, adapt, and find fulfillment in their craft amid these seismic shifts. Three Pillars for Thriving as an AI-Native Designer To navigate this transformation, designers must focus on three key areas: Combined with strategic thinking and human-centric skills, these pillars form the foundation for the next generation of designers. 1. AI Tools: Speed as the New Standard “Man is a tool-making animal.” — Benjamin Franklin AI represents a quantum leap in tool evolution, shifting from manual execution to intelligent collaboration. Speed is no longer optional—teams like ProcessMaker have gone from shipping twice a year to every two weeks, thanks to AI automation. According to Figma’s State of Design (2025), 68% of design teams now use AI for:✔ Wireframing automation✔ Visual asset generation✔ User feedback analysis Building a Personalized AI Stack There’s no one-size-fits-all approach. A UX researcher’s toolkit differs vastly from that of a conversational AI designer or a visual artist. After experimenting with over 60 AI tools, many designers find that only 4-10 truly enhance their workflow. The key is intentional adoption—not chasing trends, but asking:🔹 Is there a smarter, faster, or more thoughtful way to do this? As design leader Agustín Sánchez notes: “You’re not a great designer because you know the latest tools. You’re great because you know what to do with them.” Prompting as a Core Design Skill Early frustrations with AI outputs often stem from poor prompting, not model limitations. Treating AI as a collaborator—structuring context, tone, and intent—dramatically improves results. John Maeda frames it well: “Prompting is just like getting the AI up to speed—or nudging it in the right direction.” For those looking to sharpen their prompting skills, key resources include: 2. AI Fluency: Designing for Probabilistic Systems AI fluency means confidently navigating intent-driven, layered, and unpredictable systems. Unlike traditional GUI interfaces (click, scroll, menus), agentic AI requires a focus on outcomes over actions. Real-world AI products involve:✔ Orchestration & memory✔ Tool integrations✔ Agentic UX flows Understanding variability, failure modes, and misuse potential is critical for responsible design. Foundational AI Learning Resources Designing AI Interactions 3. Human Advantage: The Unautomatable Edge With GPT-4o and Veo-3 producing high-quality outputs at scale, designers must ask: What remains our uniquely human advantage? Craftsmanship in the Age of AI AI generates averages, not originality. Designer Michal Malewicz describes today’s creative landscape as an “era of meh”—flooded with generic AI outputs. This raises the bar: distinctive perspective, narrative intent, and aesthetic judgment matter more than ever. As Richard Sennett argues in The Craftsman, tools evolve, but mastery remains human. Creative Direction & Agency AI handles execution; humans define vision. Two designers using the same tools can produce radically different work based on values, intent, and creative direction. Julie Zhuo emphasizes: “Even as AI matches our skills, our ability to choose why and where to apply them remains distinctly human.” 4. The AI-Native Designer of 2030 The World Economic Forum predicts that by 2030, the most valuable skills will be:✔ Analytical & creative thinking✔ Technology literacy✔ Resilience & adaptability As Fabricio Teixeira notes, design fundamentals—collaboration, communication, problem-solving—are timeless, outlasting any tool. Meanwhile, “Super IC” roles are redefining seniority—valuing deep expertise over management. In a world where creation is faster and more accessible, a designer’s true moat lies in:🔹 Unique, reliable, and memorable AI experiences🔹 Mastery of storytelling and human-centered design Conclusion: Designing the Future, Not Just Adapting to It AI isn’t replacing designers—it’s redefining their role. The designers who thrive will be those who: The future belongs to those who orchestrate AI, not just use it. 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 2, 2025in Agentic AI, AI Tools, Generative AI, Google, Technology
Mulesoft
30Jun

Salesforce’s MuleSoft Paves the Way for Autonomous AI Agents in Enterprise IT

AI agents are coming to the enterprise—and MuleSoft is building the roads they’ll run on. As AI agents emerge as the next evolution of workplace automation, MuleSoft—Salesforce’s integration powerhouse—is rolling out new standards to bring order to the chaos. The company recently introduced two key protocols, Model Context Protocol (MCP) and Agent2Agent (A2A), designed to help AI agents operate autonomously across enterprise systems while maintaining security and oversight. This builds on Salesforce’s Agentforce toolkit, now in its third iteration, which provides developers with the building blocks to create AI agents within the Salesforce ecosystem. The latest update adds a centralized control hub and support for MCP and A2A—two emerging standards that could help AI agents work together seamlessly, even when built by different vendors. Why MuleSoft? The Missing Link for AI Agents MuleSoft, acquired by Salesforce in 2018, originally specialized in connecting siloed enterprise systems via APIs. Now, it’s applying that same expertise to AI agents, ensuring they can access data, execute tasks, and collaborate without requiring custom integrations for every new bot. The two new protocols serve distinct roles: But autonomy requires guardrails. MuleSoft’s Flex Gateway acts as a traffic controller, determining which agents can access what data, what actions they’re permitted to take, and when to terminate an interaction. This lets enterprises retrofit existing APIs for agent use without overhauling their infrastructure. How AI Agents Could Reshape Workflows A typical use case might look like this: This kind of multi-agent collaboration could automate complex workflows—but only if the agents play by the same rules. The Challenge: Agents Are Still Unpredictable While the vision is compelling, AI agents remain more promise than product. Unlike traditional software, agents interpret, learn, and adapt—which makes them powerful but also prone to unexpected behavior. Early adopters like AstraZeneca (testing agents for research and sales) and Cisco Meraki (using MuleSoft’s “AI Chain” to connect LLMs with partner portals) are still in experimental phases. MuleSoft COO Ahyoung An acknowledges the hesitation: many enterprises are intrigued but wary of the risks. Early implementations have revealed issues like agents stuck in infinite loops or processes that fail to terminate. To ease adoption, MuleSoft is offering training programs, entry-level pricing for SMBs, and stricter security controls. The Bigger Picture: Who Controls the Interface Controls the Market Salesforce isn’t trying to build the best AI agent—it’s building the platform that connects them all. Much like early cloud providers didn’t just sell storage but the tools to manage it, MuleSoft aims to be the orchestration layer for enterprise AI. The two protocols are set for general release in July. If successful, they could help turn today’s fragmented AI experiments into a scalable ecosystem of autonomous agents—with MuleSoft at the center. Key Takeaways: ✅ MuleSoft’s new protocols (MCP & A2A) standardize how AI agents interact with systems and each other.✅ Flex Gateway provides governance, ensuring agents operate within defined boundaries.✅ Early use cases show promise, but widespread adoption hinges on reliability and security.✅ Salesforce is positioning MuleSoft as the “operating system” for enterprise AI agents. The bottom line: AI agents are coming—and MuleSoft is laying the groundwork to make them enterprise-ready. 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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June 30, 2025in Data, Enterprise, Mulesoft, Salesforce

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