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“Log a ticket and wait 5 days.”  “Get your employee record fixed in 5 seconds.”  That’s the difference Agentforce & MuleSoft offers👇 Every enterprise has a version of this problem: Employee records live across dozens of systems  HR (Workday), Identity (Okta), Access (LenelS2),   Vendor (FIELDGLASS, INC.), CRM (Salesforce), and more. When one goes out of sync? 🚨 Onboarding delays  🚨 Access issues  🚨 Compliance risks  🚨 Duplicate cases  🚨 Frustrated employees and overwhelmed ops Here’s how it’s fixed using Agentforce, Data Cloud, and MuleSoft APIs working together: Agentforce understands the entire employee data landscape With Data Cloud feeding it full integration context, the agent understands:  → Which systems own which attributes  → How employee records flow  → What’s wrong and what to do about it It doesn’t just answer, it acts. Then MuleSoft APIs make it executable Most systems already expose reprocessing APIs.  Those APIs get unified through a MuleSoft Process API.    Now Agentforce calls it directly. Now, Agentforce can:  → Detect a discrepancy  → Identify the source of truth  → Trigger reprocessing in the right system instantly 📉 No more operations backlog.  📉 No more duplicate cases.  📉 No more “file here instead” nonsense.  If the agent can’t fix it? It still wins. → Gives user exact root cause  → Directs them to the right team  → Adds full context  → Case resolved first try itself This is what Agentic Architecture looks like in the enterprise:  ✅ Context from Data Cloud  ✅ Actions via MuleSoft  ✅ Intelligence from Agentforce  ✅ Real-time, no-touch resolution at scale You don’t need more dashboards.  You need fewer cases. 👇 Is your enterprise architecture built for agents?  Tag your MuleSoft, HRIS, or Internal Tools leads, this is the future of employee experience. #Agentforce #DataCloud #EmployeeExperience ##APIIntegration MuleSoft Community

Most LLM outputs fail before they ever reach production.  But no one talks or tries to understand why. Most teams blame the LLM. But here’s what MuleSoft proved 👇 The issue isn’t the model.  It’s what happens after the prompt is sent. Same prompts, same models used, with two different approaches: 1️⃣ Baseline LLM pipeline (used by most teams) 2️⃣ MuleSoft’s AI Quality Pipeline (engineered for trust) The results? Baseline pipeline: • ~20% valid • ~17% correct MuleSoft pipeline: • 90–95% valid • 80–90% correct Same model. 5x better outcome. The difference? What happens after the prompt is sent. Here’s what MuleSoft’s AI Quality Pipeline adds: ✅ Prompts grounded in 7,000+ real connector operations ✅ IDE-integrated validator that checks syntax + compatibility ✅ Automated error correction before the user ever sees it ✅ An LLM Judge to evaluate real business logic and intent This pipeline doesn’t just generate flows, it guarantees that   they’re usable, trustworthy, and aligned with real business intent. Flashy demos are easy. But production-grade, trusted AI takes real engineering. So the next time your flows break, don’t ask: “Is this the right LLM?” Ask: “Is this the right pipeline?” If your AI outputs look good but collapse in the real world… It’s time to stop testing models. And start fixing the system around them. 📩 Are you ready to build a generative AI pipeline that actually works? Let’s talk: https://lnkd.in/gX97Mgrh #MuleSoft #EnterpriseAI #AIEngineering #Salesforce #AgenticEnterprise #AIPipeline MuleSoft Community

Enterprises don’t need more prompts.  They need orchestration for completion.   Here’s how AI Agents actually execute enterprise workflows 👇 Most think LLMs = intelligence.  But in reality? Intelligence without structure gives improper results. The image shows what it really takes to turn a natural language request like  “Onboard an employee” into secure & auditable business actions across systems. Let’s walk through it: 𝗦𝘁𝗲𝗽 𝟭: 𝗟𝗟𝗠 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝘀 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗻𝘁  The prompt is received not from an app, but by an Agent Broker. This Broker uses an LLM to:  → Break down the request  → Generate a sequence of sub-tasks  → Assign those to the right agents (HR, IT, Finance, etc.) 𝗦𝘁𝗲𝗽 𝟮: 𝗔𝗴𝗲𝗻𝘁 𝗕𝗿𝗼𝗸𝗲𝗿 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲𝘀 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻  The agents live inside CloudHub 2.0, each paired with an MCP Server. They act:  → Calling APIs  → Moving data  → Triggering downstream automation All orchestrated through a dynamic loop. 𝗦𝘁𝗲𝗽 𝟯: 𝗙𝗹𝗲𝘅 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝗴𝗼𝘃𝗲𝗿𝗻𝘀 𝗲𝘃𝗲𝗿𝘆 𝘀𝘁𝗲𝗽  All communication, in or out, flows through Flex Gateway with enterprise-grade policies:  ✅ Access control  ✅ PII detection  ✅ Prompt decorators  ✅ Rate limiting  ✅ Full observability and logging Nothing runs without being inspected, validated, and governed. 𝗦𝘁𝗲𝗽 𝟰: 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 + 𝗧𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆  Every step is tracked.  Every decision is visible.  Every interaction is logged.  So you don’t just deploy AI, you trust it. This architecture is how you move from: ❌ Chat interfaces and pilot projects  ✅ To enterprise-grade autonomous execution Natural language in.  Governed & secure workflows out. 👇 Are you building an agentic architecture like this?  Tag your integration, platform, or AI lead, this is the new operating model. #AgenticEnterprise #MuleSoft #EnterpriseAI #APIArchitecture #Salesforce #AnypointPlatform MuleSoft Community

𝟮 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝗮 𝗺𝗼𝗻𝘁𝗵.  But this isn’t “ChatGPT for CRM.”  It’s Agentforce and it’s quietly rewriting how enterprise AI actually works.   This is the moment AI stops answering… and starts executing.  At scale. In real workflows. With real trust. Here’s how Salesforce did it and what your stack can learn 👇 Most teams are still prompting LLMs and crossing their fingers.  Salesforce took a different route:  → Agents that reason, decide, and act  → Grounded in real-time enterprise data  → Governed like employees, not scripts That’s Agentforce, which doesn’t just generate but gets things done. So, here’s how you scale across 1,000+ customers in 90 days? 𝟭. 𝗧𝗿𝘂𝘀𝘁𝘄𝗼𝗿𝘁𝗵𝘆 𝗔𝗜 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮  Agentforce pulls from Salesforce Data Cloud, APIs, and external sources all in real time.  → Multi-source indexing  → Hybrid semantic + keyword search  → Context-aware ranking to prioritize trusted content Everything is grounded, governed, and retrieval-optimized.  No guesswork. Just precision. 𝟮. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗶𝘀 𝗲𝗮𝘀𝘆. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗶𝘀 𝗵𝗮𝗿𝗱.  That’s where Atlas comes in.  It’s not just routing tasks, it’s understanding goals and sequencing actions across:  → Salesforce workflows  → MuleSoft integrations  → External APIs LLMs respond.  Agents decide.  Atlas reasons. 𝟯. 𝗕𝘂𝗶𝗹𝘁 𝗹𝗶𝗸𝗲 𝗿𝗲𝗮𝗹 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲  This isn’t a sandbox prototype.  Agentforce runs live, at massive scale with:  → Dynamic load-balancing across inference pipelines  → Fault-tolerant design (retries, fallback flows, failover agents)  → End-to-end execution tracing for every AI decision This is AI with SRE discipline not dev tools duct-taped to a model. 𝟰. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁  → Agents inherit enterprise access policies  → Every step is logged and auditable  → Built-in approval paths and escalation logic This isn’t “move fast and break things.”  This is move fast without breaking trust. Takeaway?  Agentic AI doesn’t scale because the model is smart.  It scales because the architecture is solid.  Orchestration. Observability. Recovery. Governance. This is how AI evolves:  𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 → 𝗔𝗴𝗲𝗻𝘁 → 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 👇 If you had to scale 2B autonomous actions tomorrow…  What part of your stack breaks first? #Agentforce #Salesforce #AgenticAI #EnterpriseAI #MuleSoft #AIArchitecture #Observability MuleSoft Community

AI agents don’t fail loudly.  They fail silently.  And that’s way more dangerous. This is the blind spot in enterprise AI adoption:  → You deploy an agent.  → It runs on its own.  → Something breaks.  → And… nobody knows. No alert. No log. No explanation.  Just degraded performance or worse,   a wrong outcome and no way to trace why. Salesforce just fixed that.  They’ve added Agent Observability to Agentforce 360.  And honestly, it’s the most important update they’ve made yet. Here’s why 👇 The Black Box Problem Agentic AI moves fast.  But enterprises don’t scale what they can’t see. And until now, most teams were forced to trust agents without real visibility:  → No way to track decision paths  → No way to prove compliance  → No way to optimize cost or behavior Agent Observability changes that. 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗴𝗶𝘃𝗲𝘀 𝘆𝗼𝘂: 𝗟𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Track real agent performance. Spot broken flows. Surface weak logic.  𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗖𝗵𝗮𝗶𝗻𝘀:  Watch how agents make decisions step-by-step.  𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴:  Group similar sessions to detect patterns and friction.  𝗦𝗶𝗹𝗲𝗻𝘁 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗔𝗹𝗲𝗿𝘁𝘀:  Get notified before a mistake becomes a mess. This isn’t just helpful.  It’s foundational. Why this hits home for me: I’ve talked to too many teams excited about AI…  ...but terrified of what happens after deployment. Not because the tech doesn’t work  But because they can’t see inside it. You can’t govern what you can’t observe.  You can’t trust what you can’t explain.  And you definitely can’t scale what you can’t debug. Agent Observability closes that gap.  It turns agentic AI from “invisible” to accountable.  And that’s what makes it enterprise-ready. If you’re building or buying agents right now, ask this: What happens when your agent makes a bad decision?  Who gets alerted? Who can trace it? Who can fix it? If you don’t know… you’re not ready to scale.  But now, with Agentforce 360, you can be. 👇 Curious who’s already building observability into their AI stack? #Agentforce360 #Salesforce #EnterpriseAI #Observability #AIAgents #AgenticEnterprise #DigitalTransformation MuleSoft Community

❌ Most CIOs aren’t failing to scale AI. They’re failing because of 5 dangerous myths they still believe. 👇 This is a must-read if you want to move AI from pilot to platform. From initiative to infrastructure. #AIAdoption #AgenticEnterprise #DigitalTransformation MuleSoft Community

Deploying APIs is no longer the goal.  Running them like resilient, governed services is. Most MuleSoft architectures are stuck at “integration.”  But today’s demands are different: Hybrid cloud.  Real-time orchestration.  Zero-trust runtime.  And apps that run like platforms. That’s where Anypoint Runtime Fabric (RTF) steps in 👇 RTF is not just a deployment mechanism.  It’s an operational foundation built for scale, hybrid control, and security. MuleSoft handles: • Runtime engine lifecycle  • RTF orchestration agents  • Anypoint platform governance You manage: • Cloud or on-prem infrastructure  • Ingress, load balancing, and scaling  • Telemetry, proxies, and policies  • Your app deployments and upgrades Teams are adopting it fast because of: • Zero-trust communication with secure traffic control  • Isolated runtimes per app for fault containment  • Clustered deployment across any environment  • Built-in observability with metrics and logs  • Deploy anywhere: AWS, Azure, GKE, OpenShift, or datacenter It’s a platform shift With RTF, you move from:  → Shipping APIs  → To operating integration services with true infrastructure parity You unlock:  ✅ Policy-driven security  ✅ Infrastructure consistency  ✅ Real-time visibility  ✅ Cloud-native flexibility If you’re scaling integrations, enabling AI agents, or bridging on-prem with multi-cloud RTF isn’t optional.  It’s the only way MuleSoft becomes infra-native. 👇 What’s holding back your runtime strategy right now? #RuntimeFabric #HybridIntegration #APIArchitecture #ZeroTrust MuleSoft Community

If your products aren’t showing up inside ChatGPT...  They don’t exist to a growing slice of shoppers. Why?  Because AI-driven shopping isn’t a theory anymore, it’s a channel.  And it’s exploding. In just the past year, traffic from AI assistants surged 119%.  Not just for product discovery but for actual buying behavior.  And now, AI agents are projected to drive up to 22% of Cyber Week orders. Salesforce just launched Agentforce Commerce to meet this moment  A full-stack platform to help brands sell directly inside AI agents like ChatGPT while managing fulfillment, payments, and routing across traditional channels. Here’s why it matters: AI agents are projected to drive up to 22% of Cyber Week orders this year.  Not websites. Not social. Not apps.  → Agents. With Agentforce Commerce, retailers can now:  ✅ Distribute product catalogs & pricing into AI tools via ACP  ✅ Enable agent-led shopping on their own sites, apps & POS  ✅ Automate support, recommendations, and checkout flows  ✅ Connect to Stripe and Google AP2 for in-AI transactions And it’s already live:  Pandora boosted NPS & AOV using automated FAQs + AI recs  Pacsun is targeting Gen Z where they live inside AI  Retail associates are using Agentforce at POS to serve smarter Now the message is clear:  AI isn’t just for chat. It’s for conversion. And if your stack isn’t agent-ready?  You’re already behind. Because when AI becomes the new storefront,  visibility = viability. 👉 Are you building for where your customers are already shopping?  Let’s discuss.  #AgentforceCommerce #Salesforce #AIShopping #DigitalCommerce #AIAgents #CyberWeek2025 #EcommerceInnovation #DigitalTransformation MuleSoft Community

Everyone says they’ve implemented Zero Trust. But introduce AI agents and it all falls apart. This post nails the problem few are talking about: Zero Trust wasn’t built for autonomous systems. If you're rolling out agents without simulating trust boundaries first… then you’re taking a huge risk.

💥 𝟵𝟱% 𝗼𝗳 𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀 𝗳𝗮𝗶𝗹.  Not because the models are bad  but because the approach is. So what do the top 5% do differently? Here’s the pattern we’ve seen 👇 𝗪𝗵𝘆 𝗺𝗼𝘀𝘁 𝗽𝗶𝗹𝗼𝘁𝘀 𝘀𝘁𝗮𝗹𝗹:  • No clear goals or KPIs  • Agents live outside the tools people use  • Missing enterprise context agents rely on prompts, not data  • No governance, audit trails, or access controls  • Infrastructure can't support scale ✅ 𝗪𝗵𝗮𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼:  • Agents are embedded where work happens Salesforce, Slack, Service Cloud  • Context comes from CRM, logs, contracts, and history   • Role-based access + audit logs baked in from day one  • Clear metrics, training loops, escalation paths 📈 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲'𝘀 𝗿𝗲𝘀𝘂𝗹𝘁𝘀:  • 2M+ support conversations handled by Agentforce  • 91% of incidents detected in under 8 min  • $60M pipeline from AI-powered SDR The takeaway:  Agents only work when they’re built into how your business works.  No context → No trust.  No governance → No scale.  No integration → No adoption. Is your AI pilot built to scale or just built for demo? Let’s discuss. #AIArchitecture #AgenticEnterprise #MuleSoft #Salesforce #DigitalTransformation #APIAutomation #Governance MuleSoft Community