AI agents aren't chatbots... they take action. And that changes everything. They update records, sync systems, route requests, send communications—often without human review. When AI moves from answering questions to executing workflows, trust becomes the most important feature. Great breakdown of how Workato Enterprise MCP enables governance without slowing agents down: deterministic vs. stochastic behavior, modular architecture, zone-based autonomy controls, and orchestration that ensures agents follow approved processes. Definitely worth reading if you're thinking about agentic AI: https://lnkd.in/g2d96qUi #EnterpriseMCP #AgenticAI
AI Agents: From Answering Questions to Executing Workflows
This title was summarized by AI from the post below.
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I have published the full white paper on Medium, covering Agentic AI, Multi-Agent Systems, AI Governance Frameworks in detail: → The 5 characteristics that define a true AI agent → The 4-layer memory architecture every enterprise agent needs → Multi-agent orchestration patterns — hierarchical, peer-to-peer, market-based, and debate architectures → Sector-specific playbooks for BFSI, Healthcare, and GCCs → The RACE Governance Framework for responsible deployment → A 4-stage adoption roadmap with risk classification Please feel free to read. https://lnkd.in/gWftKQMj
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As AI Agents expand to the enterprise it is clear that the current operating models that were suitable more for a deterministic world of enterprise are no longer applicable to an agentic enterprise. The solution is to think about an operating model that focuses on a layered architecture 1. A cognitive / intelligence layer - Dealing with what models to deploy for various purposes given there could be multiple models in consideration 2. A coordination layer - Dealing with how agents interact with each other to achieve goals 3. The control layer or supervisor layer - Which manages guardrails and boundaries for agents 4. The governance layer - Interestingly, governance is often unified with control layer but separating this layer assigns specific responsibilities like accountability (who is responsible for the success/failure/operations of an agent or set of agents).
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Enterprise software was built for humans to run systems. That model is breaking. In an agentic enterprise, AI runs workflows end to end, reading data, making decisions, executing actions, and learning from outcomes. Humans shift from operators to governors, defining policies and outcomes. In the AI-VOLUTION Series by SCB 10X, Souvik Sen, CTO Ema breaks down what it takes to make this shift real: a Generative Workflow Engine™ to orchestrate agents dynamically, multi-model intelligence to optimize cost and performance, and a context graph that turns every action into institutional memory. This is not a better interface. It is AI Employees operating the business. Read the full blog to learn more: https://lnkd.in/gAnpWySq
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Lucidworks Launches MCP to Reduce AI Agent Integration Timelines by Up to 10x Why this matters: - Enterprises often struggle with AI adoption due to complex, time-consuming integrations, MCP standardizes connections between AI agents and enterprise systems, eliminating the need for custom builds - By reducing integration timelines and costs (up to $150K per integration), MCP accelerates the move from AI experimentation to real-world deployment Our take: This highlights a critical shift in enterprise AI, success is no longer about better models, but better infrastructure, and protocols like MCP could become the backbone that makes scalable, production-ready AI actually feasible What do you think? Will standardization layers like MCP become as essential to AI as APIs were to the internet? Meychele (Elle) Reis Read More:- https://lnkd.in/dBG2PY9W #AI #EnterpriseAI #AgenticAI #DigitalTransformation #MarTech #Automation #DataIntegration #news #latestnews #martechcubenews #mtc #marketingtechnology #martechcube
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Most enterprise AI deployments stop at generating text. The real value is in systems that can actually do things. AI agents don't just answer questions. They plan, take action, and adapt when things don't go as expected. The same way a capable employee would. Key takeaways: → Agents handle the 80% of decisions that don't need human judgment → Real deployments are cutting response times from days to minutes → The right architecture includes guardrails, not just capabilities → Start with one workflow, not a full transformation Full breakdown here: https://lnkd.in/dTjqBm2U #AIAgents #EnterpriseAI #DigitalTransformation #ArtificialIntelligence #BusinessLeadership
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openJiuwen: The AgentOS Revolutionizing Enterprise AI Agents with Reliability and Self-Evolution AI Agents are swiftly transitioning from proof-of-concept demos to large-scale deployment, yet they face critical challenges in scaling—particularly in compute efficiency, stability, security, and multi-agent coordination. openJiuwen addresses these pain points by introducing an enterprise-grade Agent Operating System (AgentOS) that boasts high reliability, native multi-agent support, and close hardware affinity to optimize performance and reduce operational costs. This platform is gaining notable international recognition, highlighting its cutting-edge architecture and autonomous evolution capabilities exemplified by its flagship intelligent agent, JiuwenClaw. openJiuwen's architecture redefines how AI agents operate, featuring a POSIX-inspired system service, a distributed runtime supporting semantic routing and orchestration, and a robust agent framework that treats skills as reusable libraries. The platform enhances long-term, deterministic operation through hierarchical memory and context management, continuous self-reflection, and closed-loop error correction. Its design tackles fundamental issues like high token consumption during long-running tasks and ensures state consistency despite the stateless nature of LLM inference, meeting enterprise-grade demands for robust, scalable AI workflows. Security and scalability are core to openJiuwen's design philosophy. The platform enforces multi-layered defenses, including fine-grained permissions, behavior tracking, cryptographic verification, and sandboxing for safe tool execution within agents. This comprehensive approach ensures the platform’s suitability for sensitive enterprise environments. Coupled with openJiuwen’s open-source spirit and ecosystem for turbo-charging agent skills, it promises an evolutive, composable future for AI agents acting as critical services in diverse sectors. This initiative is an exciting beacon for those eager to harness AI agents beyond the hype, into reliable, high-impact solutions. #AIAgents #AgentOS #openJiuwen
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If you've ever stayed late trying to integrate siloed systems, you know AI isn't a magic wand. It requires a rock-solid foundation. Check out Abhijit Kakhandiki's latest article on why trusted orchestration is the true operating fabric every IT leader needs to maximize ROI.
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If you've ever stayed late trying to integrate siloed systems, you know AI isn't a magic wand. It requires a rock-solid foundation. Check out Abhijit Kakhandiki's latest article on why trusted orchestration is the true operating fabric every IT leader needs to maximize ROI.
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If you've ever stayed late trying to integrate siloed systems, you know AI isn't a magic wand. It requires a rock-solid foundation. Check out Abhijit Kakhandiki's latest article on why trusted orchestration is the true operating fabric every IT leader needs to maximize ROI.
