Why AI Agents Shouldn't Orchestrate Your Enterprise Systems

Salesforce’s much-hyped Agentforce promised to bring large language model (LLM) intelligence directly into the CRM. But as early adopters quickly learned, the execution has fallen short. Customers report limited flexibility, integration challenges, and performance gaps that don’t align with the complexity of enterprise use cases. The result? A wave of Salesforce customers are re-evaluating their AI strategy, looking beyond Salesforce-native tools to harness the true potential of LLMs. 🔎 Why Agentforce Fell Short While Agentforce had an ambitious vision, several factors hindered adoption: • Closed ecosystem – Rigid guardrails prevented enterprises from bringing their own fine-tuned models. • Scalability concerns – Struggled to handle large-scale enterprise data. • Vendor lock-in – AI tied tightly to Salesforce’s stack, leaving little room for multi-cloud or hybrid approaches. • Limited connectors – Many organizations rely on MCP servers and diverse AI platforms (OpenAI, Anthropic, Llama, Mistral, etc.) which Agentforce did not support natively. 🌐 The New Path: External AI Platforms + Salesforce Data Enterprises don’t want to be boxed in. They need AI that can: • Plug into Salesforce data easily (Cases, Accounts, Opportunities, Knowledge). • Leverage enterprise-grade AI platforms that already fit into their ecosystem. • Support MCP (Model Context Protocol) for interoperability across AI tools, clouds, and servers. • Enable fine-tuning on proprietary datasets without vendor restrictions. By decoupling Salesforce data from Agentforce and instead exposing it via APIs, Data Cloud, or Data Federation, organizations can run LLMs where it makes the most sense: on external AI platforms that support flexibility, compliance, and scaling. ⚡ Practical Approaches 1. API + Data Cloud Integration Export Salesforce objects (Case, MessagingSession, Knowledge) securely to an external AI pipeline for training and inference. 2. n8n / MuleSoft / Middleware Orchestration Use low-code automation platforms to route data between Salesforce and LLM servers. 3. MCP Servers for Standardization Adopt MCP to allow AI models to interoperate with Salesforce data and other enterprise systems without heavy customization. 4. Fine-Tuned Enterprise Models Train LLMs on your domain-specific Salesforce data (case logs, support chats, sales playbooks) while keeping the model infrastructure in your control. 🏆 The Benefits of Going External • Freedom of Choice: Use the right LLM for the right use case. • Enterprise Security: Keep sensitive data in compliance with company policies. • Future-Proofing: Avoid being tied to a single vendor’s AI limitations. • Faster Innovation: Experiment with open-source and commercial LLMs side by side. 📌 Conclusion The Agentforce experiment showed us what’s possible—but also what enterprises truly need: flexibility, interoperability, and ownership over their AI stack. Wait is over Salesforce customers jump …

Bold post about the hype—and the reality—of Salesforce Agentforce. No wonder forward-thinking customers who are serious about delighting their users with AI are turning to #DevRev. Our platform enables AI Agents and Workflows powered by a knowledge graph built from multiple enterprise sources, including SFDC, Service Cloud, Jira, and more—giving teams the flexibility and context they truly need.

This is worth a read. The challenge with AgentForce for someone who has been directly and indirectly involved in promoting it is that more flexibility exists in emerging open standards like MCP, A2A and a plethora of smaller AI tools. These provide a viable alternative to being locked in to a vendor with a rigid framework. AgentForce might be right for some, but for those already being squeezed - looking at a suite of existing, proven and cost-efficient tools offers a much better fit.

Salesforce’s much-hyped Agentforce promised to bring large language model (LLM) intelligence directly into the CRM. But as early adopters quickly learned, the execution has fallen short. Customers report limited flexibility, integration challenges, and performance gaps that don’t align with the complexity of enterprise use cases. The result? A wave of Salesforce customers are re-evaluating their AI strategy, looking beyond Salesforce-native tools to harness the true potential of LLMs. 🔎 Why Agentforce Fell Short While Agentforce had an ambitious vision, several factors hindered adoption: • Closed ecosystem – Rigid guardrails prevented enterprises from bringing their own fine-tuned models. • Scalability concerns – Struggled to handle large-scale enterprise data. • Vendor lock-in – AI tied tightly to Salesforce’s stack, leaving little room for multi-cloud or hybrid approaches. • Limited connectors – Many organizations rely on MCP servers and diverse AI platforms (OpenAI, Anthropic, Llama, Mistral, etc.) which Agentforce did not support natively. 🌐 The New Path: External AI Platforms + Salesforce Data Enterprises don’t want to be boxed in. They need AI that can: • Plug into Salesforce data easily (Cases, Accounts, Opportunities, Knowledge). • Leverage enterprise-grade AI platforms that already fit into their ecosystem. • Support MCP (Model Context Protocol) for interoperability across AI tools, clouds, and servers. • Enable fine-tuning on proprietary datasets without vendor restrictions. By decoupling Salesforce data from Agentforce and instead exposing it via APIs, Data Cloud, or Data Federation, organizations can run LLMs where it makes the most sense: on external AI platforms that support flexibility, compliance, and scaling. ⚡ Practical Approaches 1. API + Data Cloud Integration Export Salesforce objects (Case, MessagingSession, Knowledge) securely to an external AI pipeline for training and inference. 2. n8n / MuleSoft / Middleware Orchestration Use low-code automation platforms to route data between Salesforce and LLM servers. 3. MCP Servers for Standardization Adopt MCP to allow AI models to interoperate with Salesforce data and other enterprise systems without heavy customization. 4. Fine-Tuned Enterprise Models Train LLMs on your domain-specific Salesforce data (case logs, support chats, sales playbooks) while keeping the model infrastructure in your control. 🏆 The Benefits of Going External • Freedom of Choice: Use the right LLM for the right use case. • Enterprise Security: Keep sensitive data in compliance with company policies. • Future-Proofing: Avoid being tied to a single vendor’s AI limitations. • Faster Innovation: Experiment with open-source and commercial LLMs side by side. 📌 Conclusion The Agentforce experiment showed us what’s possible—but also what enterprises truly need: flexibility, interoperability, and ownership over their AI stack. Wait is over

Intriged by reading below post from SRINI Pusuluri . Well written ! 👏 As the landscape of AI-driven CRM evolves, enterprises are seeking platforms that offer more than just promises. At DevRev, we understand the critical need for flexibility, scalability, and seamless integration that modern businesses demand. Unlike some solutions that impose rigid vendor constraints and scalability issues, DevRev provides an open ecosystem where you can fine-tune your AI models, integrate effortlessly with enterprise-grade platforms, and maintain control over your data. Happy to discuss with anyone on the how behind DevRev

Salesforce’s Data Cloud may be the most under-discovered data tech yet. It’s not a data lake. It’s an entirely new layer in the stack, built for one purpose: activating your data. Data teams are finally spending more time on activation - executing the recommended decision in downstream systems. That’s what turns data from a cost center into a revenue accelerant. But in the old data stack, activation meant painful integrations between the lake and Slack, CRM, ERP, and every other business system. Most data teams don’t have the bandwidth or skills for that. We use Data Cloud to bring together hundreds of streams into a single data model, and activate it natively across marketing, sales, success, and analytics. Build once. Reuse everywhere. It complements, not replaces, your data lake and operational systems. If your company runs on Salesforce, Data Cloud could be your shortcut to ROI. More on Data Cloud from our engineering leader Muralidhar Krishnaprasad: https://lnkd.in/eQV3vUeb Data teams - what tools are helping you get past the last-mile activation problem?

#HostingJournalist #AI Ardoq, a software as a service provider that is redefining enterprise architecture, has announced the release of its Enterprise AI Management Solution, which is intended to assist businesses in gaining control, visibility, and compliance over their use of AI. The issue for executives is to close blind spots by getting insight about shadow AI usage, data flows, compliance requirements, and the influence on business outcomes as AI becomes more and more integrated into every part of the organization. The urgency is highlighted by impending deadlines from proposed US legislation like the Algorithmic Accountability Act and frameworks like the EU AI Act and the Colorado AI Act. Boards are expected to show that AI systems are ethical, transparent, and compliant, but many firms do not have the resources to provide that kind of evidence. "AI is bringing about both genuine uncertainties and hitherto unheard-of opportunities. The majority of businesses don't know what data AI affects or where it lives in their operations. In order to link AI use to strategy, compliance, and risk, they want integrated, enterprise-wide intelligence. "Ardoq provides leaders with the visibility to act and the confidence to move quickly and responsibly by integrating governance into the Enterprise Architecture knowledge graph," stated Erik Bakstad, CEO and co-founder of Ardoq. Governance in Context: Linking AI to Value, Risk, and Strategy Ardoq provides governance in context by relating the use of AI to risk, strategy, and quantifiable commercial value. Incorporating governance into the Enterprise Architecture knowledge graph gives leaders a comprehensive understanding of AI's applications, interactions with data and systems, and alignment with transformation goals and objectives and business capabilities. They are able to confidently and clearly respond to board-level queries as a result. There are four pillars that would provide impact:  * AI Visibility: Locate AI agents and systems, including shadow AI, throughout the company. Map the applications, owners, and data that AI depends on in order to identify blind spots before they become hazards. * AI Compliance Readiness: Keep an eye on changing AI rules and guidelines in addition to internal security and governance guidelines. Organizations may show regulators, auditors, stakeholders, and their own boards that AI is being used responsibly by centralizing controls, audit trails, and reporting readiness. * Aligning strategically: By linking AI use to strategy, capabilities, KPIs, and results, executives can prioritize investments, show value creation, and remove high-risk or low-value use cases. * Future‑Proofing Governance: Governance that is future-proof is scalable and vendor-neutral, adapting to changing models and ecosystems. Lock-in-free support for industry-specific tools, proprietary LLMs, and generative AI. In order to eliminate…

#AI Ardoq, a software as a service provider that is redefining enterprise architecture, has announced the release of its Enterprise AI Management Solution, which is intended to assist businesses in gaining control, visibility, and compliance over their use of AI. The issue for executives is to close blind spots by getting insight about shadow AI usage, data flows, compliance requirements, and the influence on business outcomes as AI becomes more and more integrated into every part of the organization. The urgency is highlighted by impending deadlines from proposed US legislation like the Algorithmic Accountability Act and frameworks like the EU AI Act and the Colorado AI Act. Boards are expected to show that AI systems are ethical, transparent, and compliant, but many firms do not have the resources to provide that kind of evidence. "AI is bringing about both genuine uncertainties and hitherto unheard-of opportunities. The majority of businesses don't know what data AI affects or where it lives in their operations. In order to link AI use to strategy, compliance, and risk, they want integrated, enterprise-wide intelligence. "Ardoq provides leaders with the visibility to act and the confidence to move quickly and responsibly by integrating governance into the Enterprise Architecture knowledge graph," stated Erik Bakstad, CEO and co-founder of Ardoq. Governance in Context: Linking AI to Value, Risk, and Strategy Ardoq provides governance in context by relating the use of AI to risk, strategy, and quantifiable commercial value. Incorporating governance into the Enterprise Architecture knowledge graph gives leaders a comprehensive understanding of AI's applications, interactions with data and systems, and alignment with transformation goals and objectives and business capabilities. They are able to confidently and clearly respond to board-level queries as a result. There are four pillars that would provide impact:  * AI Visibility: Locate AI agents and systems, including shadow AI, throughout the company. Map the applications, owners, and data that AI depends on in order to identify blind spots before they become hazards. * AI Compliance Readiness: Keep an eye on changing AI rules and guidelines in addition to internal security and governance guidelines. Organizations may show regulators, auditors, stakeholders, and their own boards that AI is being used responsibly by centralizing controls, audit trails, and reporting readiness. * Aligning strategically: By linking AI use to strategy, capabilities, KPIs, and results, executives can prioritize investments, show value creation, and remove high-risk or low-value use cases. * Future‑Proofing Governance: Governance that is future-proof is scalable and vendor-neutral, adapting to changing models and ecosystems. Lock-in-free support for industry-specific tools, proprietary LLMs, and generative AI. In order to eliminate blind spots and…

Enterprise Agent Memory: From Chatbot to Business Assistant Traditional LLMs are stateless, but for enterprise agents to be truly effective and reliable, they require a robust, stateful memory architecture. This shift from a simple "conversation in a box" to a functional business assistant is fundamentally a problem of strategic memory management. Enterprise agent memory is more than just a feature; it's a core piece of your infrastructure. A well-designed memory system is built around four key strategies: 1. Persistence (Write Context): This involves creating a durable record of agent operations. This can be achieved through memory blocks with size limits, external file systems, and structured state objects that manage complex workflows. 2. Retrieval (Select Context): To be useful, memories must be easily accessible and relevant. This is accomplished using vector similarity search, weighting based on business priorities, and orchestrating retrieval from multiple data sources. 3. Optimization (Compress Context): To prevent an ever-growing memory footprint, systems need to efficiently manage and compress context. Techniques like recursive summarization and intelligent message eviction keep memory lean while preserving critical information and continuity. 4. Separation (Isolate Context): For multi-agent systems and complex tasks, it's crucial to segregate memory. This is done by establishing specialized memory domains, session boundaries, and agent-specific memory stores with controlled sharing. The Memory Lifecycle & Types: A full memory lifecycle in production includes generation, storage, retrieval, integration, updating, and deletion. To support this cycle, enterprise agents utilize six distinct types of memory, each serving a specific purpose: a. Short-Term Memory: For real-time task status and immediate agent coordination. b. Long-Term Memory: A knowledge repository of accumulated expertise and successful patterns. c. Episodic Memory: An audit trail of complete interaction history for compliance and debugging. d. Consensus Memory: A source of canonical knowledge like official procedures and validated standards. e. Persona Libraries: Used for managing roles, behaviors, and permissions. f. The Whiteboard: A collaborative space for shared artifacts and cross-team coordination. The Architecture Mindset: Given that agents often make over 50 tool calls per task, their ability to be reliable hinges on their memory architecture. As such, memory is not a secondary feature; it's infrastructure. Design it with the same rigor you would apply to your database architecture, ensuring to: - Always include a memory provider in your system design. - Never rely solely on the context window. - Plan for multiple memory types from the outset. - Implement a full memory lifecycle management plan.

𝑨𝒛𝒖𝒓𝒆 𝑫𝒂𝒕𝒂 𝑭𝒂𝒄𝒕𝒐𝒓𝒚 𝒗𝒔 𝑴𝒊𝒄𝒓𝒐𝒔𝒐𝒇𝒕 𝑭𝒂𝒃𝒓𝒊𝒄: 𝑵𝒂𝒗𝒊𝒈𝒂𝒕𝒊𝒏𝒈 𝒕𝒉𝒆 𝑬𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏 In enterprise boardrooms this year, one question keeps surfacing: "Should we modernize our data integration to Microsoft Fabric, or continue investing in Azure Data Factory?" After implementing both platforms across various enterprise environments, here's the reality check every data engineer needs. 𝐖𝐡𝐞𝐧 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐑𝐞𝐦𝐚𝐢𝐧𝐬 𝐊𝐢𝐧𝐠 ADF continues to dominate in complex enterprise scenarios where control and maturity matter most. • Enterprise-grade orchestration with proven reliability at scale • Comprehensive connectivity — 100+ connectors with mature hybrid support • Advanced control flows and sophisticated error handling mechanisms • Battle-tested CI/CD with established deployment patterns • Multi-cloud flexibility without vendor lock-in constraints It's the reliable workhorse — mature, feature-complete, and trusted by Fortune 500 companies for mission-critical workloads. 𝐖𝐡𝐞𝐫𝐞 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜 𝐢𝐬 𝐖𝐢𝐧𝐧𝐢𝐧𝐠 Fabric excels when unified analytics and modern user experience are priorities. • OneLake integration — single source of truth across all workloads • AI-powered development with Copilot for accelerated pipeline creation • Unified platform eliminating data movement between analytics services • Capacity-based pricing offering predictable cost models • Modern collaboration with Power BI, real-time analytics, and data science It's the Tesla of data platforms — innovative, integrated, and designed for the AI-first future. 𝐓𝐡𝐞 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 (𝐀𝐮𝐠𝐮𝐬𝐭 𝟐𝟎𝟐𝟓) Recent updates show Microsoft's clear direction: • Feature parity acceleration — 79% of ADF activities now supported in Fabric • Enhanced migration tools with AI-powered assistance for modernization • Direct ADF linking in Fabric (now in preview) for seamless transition • No ADF deprecation plans — both platforms will coexist strategically The convergence is happening, but it's evolutionary, not revolutionary. 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐚𝐭𝐫𝐢𝐱 Choose ADF when: - Complex hybrid/multi-cloud environments - Mature DevOps processes with existing investments - Advanced orchestration requirements - Regulatory compliance demands maximum control Choose Fabric when: - Microsoft ecosystem standardization - Power BI-centric analytics strategy - Team collaboration and low-code development priorities - Green-field projects with AI/ML requirements Hybrid approach when: - Large enterprises with diverse use cases   - Gradual modernization strategies   - Risk mitigation during transformation 𝐅𝐢𝐧𝐚𝐥 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 ADF vs Fabric isn't a competition — it's an evolution, and your data strategy determines which path fits your organization's journey. #AzureDataFactory #MicrosoftFabric #DataEngineering #CloudData #DataStrategy