Jason Estevan

If Salesforce wanted to really make admins happy, here is what they should build... An AI that is completely tied to your entire platform setup. Then on any page or place, you could ask a question about anything in the setup and it would help you troubleshoot, find items, and more. Second, build a report building AI.

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The year is 2024 and we are talking about Agentforce and Einstein Trust Layer but still have a barrage of Salesforce consultants and developers who believe that - "with sharing"/"without sharing" keywords in the class signature automatically enforces OLS and FLS on any DML operations within the Apex class citing things like "system context" /"user context". The reason - Rotting Salesforce Documentation.. Please stop! Go back learn about how security and access works as layers and how uniquely database level sharing has been implemented on Salesforce platform. And finally please understand the difference between - Recording sharing, Object level Security and Field Level Security. There are different ways to control them at Apex level.

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What you build is more important than where and how you build it. But when you're a Salesforce AppExchange partner, it actually matters a great deal how you build it—especially if you want to fully leverage the platform's extensibility. I was reminded of this when Ross Belmont commented on one of my posts a few months ago. I had asked, "What makes a great product architect?" His response stuck with me: "The humility to build your solution in the most Salesforce-y way you can, whether or not you think that's best (because admins are already used to that approach)." Admins are the unsung heroes of Salesforce, and building with them in mind makes all the difference. Here are 3 key patterns we've established at Kaptio that embrace the platform's extendability and make implementation smoother for admins: 𝟭. 𝗢𝘃𝗲𝗿𝗿𝗶𝗱𝗮𝗯𝗹𝗲 𝗙𝗹𝗼𝘄𝘀 This is one of my favorite patterns. Start with a default flow but allow it to be cloned and customized to suit specific business needs. For example, with our Package Search feature, when entering Package Search from an Account, Person Account, or Contact record, you can define a flow that pre-populates search parameters from the customer record. The default flow sets fields like currency, selling channel, and relevant tax profile, but admins can extend this to pre-populate most filters, offering them complete flexibility. 𝟮. 𝗟𝗶𝗴𝗵𝘁𝗻𝗶𝗻𝗴 𝗪𝗲𝗯 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝘀 When designing a component, take extra time to consider what attributes admins can configure to better serve their users. I’ve been prototyping LWC components, and by using AI to brainstorm attributes, I found valuable flexibility I hadn’t thought of. One suggestion was a toggle for showing a multi-checklist for each record and embedding custom actions to trigger a flow or open another LWC. While not an immediate need, it anticipates future complexities. We use JSON for custom actions—not the most admin-friendly yet, but waiting for Salesforce to support nested attribute structures (hint, Ross Belmont!). 𝟯. 𝗘𝘅𝘁𝗲𝗻𝗱𝗮𝗯𝗹𝗲 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 Sometimes you invest 40-50% of your engineering capacity in building a critical Lightning App Page, made of composable LWC components with specific behaviors. Let each component have configurable attributes (as mentioned above), but also allow admins to embed their own screen flows or custom LWCs. Enabling no-code tools within your app taps into the full potential of Salesforce’s extendability. Plus, with people like Igor Kudryk and AI code generation, admins/citizen developers will start writing components to extend your app further. As an AppExchange partner, your success often hinges on how easy you make it for admins to manage and extend your product. What other patterns have you seen that empower admins? #SalesforceAdmins #AppExchange #NoCodeSolutions

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Salesforce has just unveiled Agentforce, a suite of low-code tools for creating autonomous AI agents. This development signals a new era in AI technology – one that focuses on higher-order reasoning and task execution. The reason I'm posting this is not to keep you up to date of yet another piece of new tech per se, but more to get you to think about the implication of this trend and what it means for your business: The Dawn of Agentic AI Agentforce represents a shift from thinking about AI as 'content and analysis production' to truly autonomous AI agents that can execute tasks. These agents can: - Take independent actions - Use human-like reasoning - Integrate seamlessly with existing workflows Small Business Implications 👇 While the starting price of $2 per conversation may be steep for small businesses, this doesn't mean you should sit on the sidelines. The principles behind Agentforce can be applied to businesses of all sizes. Indeed the functionality of 'big guns' like Salesforce and Hubspot can be largely replicated to suit smaller budgets, and dare I say the ability to customise your tools gives smaller businesses a potential advantage: 1. AI Automation Agencies: Companies like Appy Automations can help small businesses recreate ERP-like functionality with intelligent work systems across your business communicating with each other. 2. Agentic Tools: Explore other agentic AI tools that fit your budget and needs. LOTS are appearing, and you can build your own. 3. Value Creation: Start considering how these technologies can create value for your business now, before larger competitors gain too much of an advantage. (Your breakfast is currently being eaten, and lunch will be gone by 2026) The Bigger Picture 🌊 This new wave of AI technology isn't just about streamlining operations – it's about reimagining your company's value proposition. As agentic AI becomes more prevalent, businesses that adapt early will be better positioned to thrive in an increasingly AI-driven marketplace. The future of business is here, and it's increasingly powered by intelligent, autonomous agents. Whether you're a large company or a small local business, now is the time to start exploring how agentic AI can transform your operations and boost your competitive edge. What are your thoughts on AI agents? How do you see it impacting your industry? How do you think it will impact your IP? Let's discuss this in the comments! 🤨 #AI #BusinessInnovation #Salesforce #AgenticAI #FutureOfWork

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I just came to see a post advising not to use shared developer org for Salesforce projects and author claims that using shared developer org is anti-pattern. After that I couldn't locate that post. He suggests to use individual developer org. That is, if your team has 25 developers, there will be 25 developer sandboxes for development! Author simply stick on theory. But, real-life scenarios are not theories, but practical. Strategy of having multiple developer sandboxes makes the developer's life hell. Usuly these kind of text book strategies make Salesforce DevOps a scary dream. Suppose that there are 25 developer sandboxes. I am using one of them and I made changes in a couple of components and there are some manual pre and post deployment steps. All the deployable chsnges I made may be available to othere 24 orgs via backsync deployment mechanism. I agree. But, what about the manual pre and post deployment steps I have made? Who will make those changes especially manual changes in other 24 orgs? If your org misses any changes done by others in their org, it may turn to a nightmare in future. I have come across with this nightmare in past. Speaking from 16 years of Salesforce experience, I saying, it is theoritically easy, but it is not practical. Staging hunk is the simple basic git concept. If you know how to stage hunk your changes to feature branch, you can perform your development in a shared developer sandbox multiple users. I promise you. Note: If your org has multiple project teams with same production org, you can have different developer sandbox for each team. But there should be a proper backsync mechanism for deployable and manual changes happened in those orgs. #salesforce #salesforcedevops

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A tip to remember for Salesforce CLI users when you retrieve metadata for something that contains a new feature. If the API version in your sfdx-project.json version is not updated to a version that contains the feature you are using, the metadata will not be returned properly. I encountered this when I added a Create Record component with a duplicate check to an existing flow. All of the updates I made to the flow to include the Create Record component were present when I retrieved the metadata with the CLI, or at least that is what I thought. After building a package version for the project and deploying it to a sandbox, I tested and discovered that I could still create duplicates. WTH? I had thoroughly tested this in my scratch org! Why was it not working? I checked the flow to make sure my updates were there. At first glance, they were, but a closer inspection of the flow revealed that my duplicate check criteria was not there. After spending a decent amount of time trying to figure out what was going on, I opened a case with Salesforce Support, figuring something was missed in the Summer '24 update with the CLI. I found a problem!! Well, yes, but the problem was me, not the CLI. As I was meeting with them and explaining the issue to the support rep, the source of the problem suddenly came to me. It wasn't the CLI. The API version for the project did not include the feature I was using. Case closed. #SalesforceDevelopers #SalesforceDX

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Will one LLM reign supreme? IT leaders are feeling the pressure to navigate this complex landscape to achieve successful AI adoption. See how you can rule all the LLMs in our latest edition of #TechnicallySpeaking: MuleSoft Community MuleSoft Salesforce #llm #Mule #MuleSoft #MuleSoftDevelopers #MuleSoftCertified #Muleys #MuleSoftArchitects #Integration #Salesforce #MuleSoftCommunity

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🔍 Unlocking Performance in Pega Reports: Managing Large Datasets Efficiently! In Pega, the default Report Definition fetches a maximum of 10,000 records. But what if you need more? With two key tweaks, you can scale beyond this limit: 1. Max Records in RD Rule: Adjust the "Max Records to Retrieve" field for flexibility. Be cautious! Fetching more than 50,000 records may affect performance. 2. Dynamic System Setting (DSS): By altering the reporting/maxRecords, you can expand globally, but pulling large datasets (e.g., over 50k records) can slow your system. 💡 Best Practices: • Implement Pagination to optimize load times. • Use BIX to export large data efficiently. • Offload heavy reporting to a data warehouse. • Leverage indexing for faster retrieval. 🔧 Example: A financial institution needing millions of transactions for audits can use pagination and BIX to streamline the process—avoiding system slowdowns. Scaling with Pega? Use these tactics for seamless performance! 🚀 #Pega #DataStrategy #BigData #PerformanceOptimization #Scalability #Automation #Reporting #BIX #TechLeadership

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With the MuleSoft EDI accelerator for Salesforce OMS, powered by Anypoint Partner Manager, B2B enterprises can now ingest EDI orders from customers directly into Salesforce Order Management, enabling unified visibility of orders received via all digital channels. MuleSoft MuleSoft Community Salesforce #Mule #MuleSoft #MuleSoftDevelopers #MuleSoftCertified #Muleys #MuleSoftArchitects #Integration #Salesforce #mulesoftcommunity

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Understanding the Role of a MuleSoft Administrator: A Step-by-Step Breakdown ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Chat with me 👉 https://vist.ly/3mmxpwm A MuleSoft Administrator plays a crucial role in managing and maintaining the MuleSoft Anypoint Platform, ensuring that integration solutions run smoothly and efficiently. Here’s a step-by-step look at their key responsibilities: 1. Platform Management 🛠️ - Environment Setup: Configure and manage various environments (development, testing, production) within the Anypoint Platform. - Access Control: Set up user permissions and roles to ensure secure access to the platform. 2. Deployment and Monitoring 🚀 - Application Deployment: Deploy applications and APIs to CloudHub or on-premise environments using Anypoint Platform. - Performance Monitoring: Use Anypoint Monitoring and other tools to track application performance and health. 3. Security Management 🔒 - Data Security: Implement security best practices, including data encryption and secure transmission protocols. - API Security: Configure API gateways to enforce security policies and protect APIs from unauthorized access. 4. Troubleshooting and Support 🧰 - Issue Resolution: Diagnose and resolve issues related to platform performance or application functionality. - Technical Support: Provide support to developers and other team members for platform-related queries. 5. Platform Optimization 📈 - Resource Management: Optimize resource allocation to ensure efficient use of computing power and memory. - Scalability Planning: Plan for scaling applications and services according to business needs. 6. Backup and Recovery 🔄 - Data Backup: Ensure regular backups of data and configurations to prevent data loss. - Disaster Recovery: Develop and implement disaster recovery plans to minimize downtime in case of platform failures. 7. Collaboration and Communication 🤝 - Team Collaboration: Work closely with developers, architects, and IT teams to ensure alignment on integration strategies. - Stakeholder Communication: Communicate effectively with stakeholders to provide updates on platform performance and any issues. 8. Continuous Learning and Improvement 📚 - Staying Updated: Keep abreast of new features and updates in the MuleSoft ecosystem. - Training Participation: Engage in ongoing training and certification programs to enhance skills. The role of a MuleSoft Administrator is critical for ensuring that the integration infrastructure is reliable, secure, and optimized for performance. By excelling in these responsibilities, administrators help organizations maximize the value of their MuleSoft investments. 🌟 Chat with me 👉 https://vist.ly/3mmxpwm #MuleSoft #SystemAdministration #Integration #PlatformManagement #TechRoles #ITSupport #APIGateway #DigitalTransformation #CareerInTech

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Key DevOps Principles for Salesforce: Infrastructure as Code (IaC) Infrastructure as Code (IaC) is a DevOps practice that involves managing and provisioning infrastructure resources through machine-readable definition files, rather than manual processes. In the context of Salesforce, IaC principles can be applied to automate the setup and configuration of environments, such as sandboxes, scratch orgs, and production environments. By adopting IaC practices, Salesforce teams can: - Achieve Consistency: IaC ensures that infrastructure resources are consistently configured across different environments, reducing the risk of misconfigurations and inconsistencies. - Enable Automation: IaC allows teams to automate the provisioning, configuration, and management of infrastructure resources, reducing manual effort and increasing efficiency. - Enhance Collaboration: IaC definition files can be version-controlled and shared among team members, promoting collaboration and enabling easier knowledge transfer. - Improve Scalability: IaC makes it easier to scale infrastructure resources up or down as needed, enabling teams to respond quickly to changing business requirements. By embracing these key DevOps principles, Salesforce teams can streamline their development and deployment processes, improve collaboration, and deliver high-quality solutions faster and more efficiently.

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Today's #mulesoftmonday article highlights just ONE of the ways to keep external systems in sync with your Salesforce Org's data. #integration #Salesforce #MuleSoft

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Salesforce announced a variety of capabilities this morning regarding agentic management tools to automate testing, prototype in sandbox environments, and manage usage. The two aspects that I am most interested in across-the-board are: The #AI generated testing where I think it is going to be vital for agents to be stress tested with the help of AI. It will obviously be easier for AI to bring up a wide variety of potential tests for an agent. In the next few months, it will honestly be fairly trivial to build a standard agent within most large enterprise application platforms. But the challenge will be in testing these agents to run at enterprise scale, and with the variety of languages, context, grammar, jargon, and patois that may exist across the world in describing demands. As George Bernard Shaw said “England and America are two countries separated by a common language”. and that can be multiplied by the countries and rules and backgrounds that global companies are trying to support with their salesforce agents. The other part that I most excited about is what Salesforce calls Utterance Analysis. This is a real time analysis on the usage of an agent based on the user inputs, requests, and query outputs. There has long been a struggle in translating event logs into useful data simply because logs are overwhelming. Salesforce’s efforts in this area are an important step forward in incorporating log data into more practical and consumable analytic form factors. The one big question this press release does not tackle is around the orchestration and ongoing management of agent portfolios. Is it possible to find duplicate or similar agents and avoid the technical debt associated with managing 100s or thousands of agents going forward? It is a stated goal of Marc Benioff to have *1 billion* agents built in a year. That is a great goal, but anyone who has ever worked IT or in sales ops knows that 1 billion custom objects, workflows, tests, agents, or any other documented item is always going to be an administrative burden. Although I believe that Salesforce is making progress in this area, it is no secret that we look to Salesforce as providing a standard around enterprise governance for #CRM and related applications. And I think this is an opportunity for Salesforce to show leadership in the ongoing management of agent portfolios at a time when the data and #metadata in Salesforce are increasingly important to the valuation of the company as a strategic partner and to a publicly traded market capitalization

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Do you consider Salesforce Apex unit tests an asset or a liability? What about AI/GPT unit tests? Many Developers would agree that unit tested code increases application reliability and stability. AI brings about a whole new set of testing requirements and risks. AI models and LLMs are subject to drift and entropy post-deployment. Manually testing conversational AI using pre-developed scripts is a long and tedious task. And still, there is no way to detect drift in production. Enter Dialogue Script from iDialogue. The foundation to our Kaizen AI DevOps suite. Entire conversations can be scripted and automated with assertions after each AI response. AI-driven Continuous Integration and Deployment (CI/CD) will be a dramatic differentiator, separating the fast from the slow. Teams can develop and deploy in confidence knowing hundreds of regression tests are executed before each deployment. Ongoing monitoring and sampling of AI conversations with pass/fail scoring provides orgs with constant feedback regarding AI health and application integrity.

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What is Salesforce Experience Cloud and what does it do? Come listen to Brad Endres (Summit Advisory Team) give and overview of the product (..and a bit more if we are lucky) See you at the next Wilmington NC Salesforce Saturday.

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This is an overdue post about Mike King and Melissa Murphy’s new Salesforce B2C Solution Architecture handbook. I remember reading Mike’s edition of this book several years ago when we were colleagues at Slalom; he put his heart and soul into the first version and I’m thrilled that 1) he did it again and 2) that the 2nd edition has a new section focused on Data Cloud penned by Melissa. Some key highlights. ☑️ 400+ pages covering many different products, leading practices, and considerations for designing and delivering well-architected solutions ☑️ Section 5 - “Know Your Customer with Data Cloud” provides some great callouts across 30+ pages that are dedicated to data cloud  ☑️ Some great specific callouts on using formula fields during ingestion vs. transforms (batch and/or streaming) to be mindful of the different ways you can achieve similar results ☑️ There are even questions at the end of the section to test your knowledge Whether you’re reading all of it, or specific sections that are relevant to you, I believe there’s valuable information in here for everyone. A big thanks to Deepak Kumar at Packt for sending a copy to review. #salesforce #datacloud 

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Hello, Pega Community! 🌟 I recently came across an intriguing concept that I believe can revolutionize our approach to data management and analytics in Pega – Data Farming. 🌾 What is Data Farming? Data farming isn't about growing crops; it's about cultivating vast amounts of data to harvest actionable insights. It's a method that involves systematically collecting, processing, and analyzing data to generate predictive models and simulations. Think of it as applying the principles of agriculture to data – planting the seeds (data points), nurturing them (processing and analysis), and reaping the harvest (insights and predictive outcomes). Why Should We Care? In the world of Pega, where decision-making is driven by data, the ability to predict outcomes and optimize processes is invaluable. Data farming offers a structured approach to this. Here’s how it can benefit us: Enhanced Decision-Making: By cultivating large datasets and running simulations, we can predict customer behavior, identify trends, and make informed decisions. This means more personalized customer experiences and optimized workflows. Improved Efficiency: Data farming helps in identifying bottlenecks and inefficiencies in our processes. By analyzing large datasets, we can pinpoint areas for improvement and streamline operations. Risk Management: Predictive modeling through data farming allows us to foresee potential risks and mitigate them proactively. Whether it’s forecasting demand or predicting system failures, being ahead of the curve is always a win. How Does It Work in Pega? Imagine you're working on a Pega Customer Service application. Here’s how data farming can be applied: Data Collection: Gather extensive customer interaction data from various touchpoints – calls, emails, chats, etc. Data Processing: Use Pega’s robust data processing capabilities to clean and organize this data. Tools like Pega Data Flows and Decision Management can be instrumental here. Simulation and Modeling: Run simulations to predict customer behavior based on historical data. Pega’s Decisioning Services, combined with machine learning models, can help in creating these predictive models. Insight Generation: Harvest the insights from these models to enhance customer interactions, tailor marketing campaigns, and optimize service processes. Real-World Example: Consider a retail bank using Pega for customer relationship management. By applying data farming, they can simulate the impact of new financial products on customer engagement. This allows them to tailor their offerings and marketing strategies, ensuring they meet customer needs more effectively. Why Now? With the explosion of data and advancements in AI and machine learning, there's never been a better time to embrace data farming. It aligns perfectly with Pega’s vision of leveraging data for intelligent automation and decisioning. #Pega #DataFarming #PredictiveAnalytics #CustomerExperience #Innovation

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