Web-Expert

Salesforce interviews reward people who can explain why they would build something a certain way, not just recite definitions. Whether you are aiming for an admin, developer, consultant or Agentforce-focused role, the same pattern shows up: a few fundamentals, a scenario question or two, and a conversation about trade-offs. This guide covers the roles, the skills worth building, the questions that come up most, and a practical way to prepare. Which Salesforce Role Fits You? Role What you do Interviewers look for Administrator Configure the org: objects, fields, security, flows, reports, user support. Data model, security model, Flow, and clear communication with business users. Developer Build custom logic and UI with Apex, Lightning Web Components and integrations. Apex, triggers, governor limits, SOQL, testing, LWC basics, integration patterns. Consultant / Business Analyst Turn business needs into a Salesforce solution and guide delivery. Requirements gathering, solution tr...

In my previous post I summarized Anthropic's AI-Native SDLC Playbook , which explains how to redesign the software lifecycle once AI agents can write code faster than your process can absorb it. This post is the practical follow-up: how to actually apply it to a web development project, step by step , with files you can copy into your own repo. Throughout this post I'll use one running example: a Next.js + TypeScript e-commerce app that needs a new "Wishlist" feature. The same steps work for React + Node, Vue, Laravel, Django, or any other web stack. Only the commands change. The workflow at a glance Each stage ends by committing a file, and the next stage starts by reading it: Plan → docs/intent/wishlist.md (what and why) Design → docs/specs/wishlist.md (how) Build → code + tests (guided by CLAUDE.md and Skills) Test → passing tests + CI evals Deploy → PR with AI review findings → human approval → CI/CD Maintain...

AI coding agents like Claude Code can now write code faster than most engineering teams can plan, review, test, or ship it. That speed exposes a problem: our software development lifecycle was designed for a world where writing code was the expensive part. Anthropic's new free course, The AI-Native SDLC Playbook , is a practical guide to redesigning that lifecycle around agents without giving up human accountability. Here's what it covers and why it's worth your time. The problem: the bottleneck moved When agents speed up the build phase, the work doesn't disappear. It piles up somewhere else. The course lists three things that happen: Bottlenecks move to the stages around build: planning, review, and deployment. Manual review stops working. A security team sized for human output either builds a queue or lets code through without a proper review. Governance costs spike. Weekly or monthly exception committees can't keep up with how much agents produce. T...

 With Agentforce now central to Salesforce's roadmap, demand for people who can actually configure, ground, and govern AI agents has spiked — and Salesforce's certification track has changed to match. If you're weighing whether to get certified in 2026, here's what the credential actually covers and whether it's worth your time. The Certification Landscape Changed in 2026 Salesforce retired the older AI Associate and AI Specialist certifications in early 2026. The current credential is the Salesforce Certified Agentforce Specialist (exam code AI-201 ) — a single, more practical exam replacing the older, more theory-focused ones. What the Exam Actually Tests Rather than abstract AI theory, the Agentforce Specialist exam focuses on real configuration and governance skills: Designing AI workflows using prompts, actions, data, and automation. Grounding agents with relevant, trustworthy data. Building and customizing agents for specific business needs. I...

 An AI agent is only as good as what it knows about your business. You can build a beautifully instructed Agentforce agent and it will still give confidently wrong answers if it's reasoning over stale, incomplete, or disconnected data. That's the problem Data Cloud — renamed Data 360 in 2026 — is built to solve. The Core Problem: Agents Need Grounding Left on its own, a large language model answers from general training knowledge. That's fine for generic questions, but useless (or actively dangerous) for "what's the status of this customer's order" or "has this account had a support escalation in the last 90 days." Agents need to be grounded in your actual, current business data — not general knowledge about businesses in general. What Data 360 Actually Does Ingests and harmonizes data from hundreds of sources in real time — CRM records, SQL databases, PDFs, emails, chat transcripts — into unified customer profiles. Handles unstruc...

 Reading about Agentforce is one thing — actually building an agent is where it clicks. You don't need to write a line of Apex to get a basic agent running. Here's the step-by-step process using Agentforce Builder . Step 1: Set Up the Agent Start by giving your agent a descriptive name — something like "Order Status Agent" — which auto-generates its API name. You'll also write a short description of your company/use case and assign a user profile that determines what data and objects the agent is allowed to touch. Think of this as the agent's permission boundary before it ever runs. Step 2: Define Topics Topics are the foundation of what an agent can do. Each topic covers one area of responsibility — for example, "Check Order Status" or "Process a Return." For every topic you define: A name for the topic. A description of when the agent should use it. A scope that specifies exactly what questions or tasks it covers — and, i...