How Organizations can Utilize Agentic AI

Missing the Agentic AI Revolution? Here's Your Roadmap to Get Started If you're not exploring Agentic AI yet, you're missing the biggest paradigm shift since the emergence of LLMs themselves. While others are still perfecting prompts, forward-thinking teams are building systems that can autonomously plan, reason, and execute complex workflows with minimal supervision. The gap between organizations leveraging truly autonomous AI and those using basic prompt-response systems is widening daily. But don't worry—getting started is more accessible than you might think. Here's a practical roadmap to implementing your first agentic AI system: 1. 𝗕𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝗮 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 – Choose a specific task with clear boundaries where automation would provide immediate value. Document research, competitive analysis, or data processing workflows are excellent starting points. 2. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁'𝘀 𝘁𝗼𝗼𝗹 𝗯𝗲𝗹𝘁 – An agent's power comes from the tools it can access. Start with simple tools like web search, calculator functions, and data retrieval capabilities before adding more complex integrations. 3. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 – The ReAct (Reasoning + Acting) pattern dramatically improves reliability by having your agent think explicitly before acting. This simple structure of Thought → Action → Observation → Thought will transform your results. 4. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗺𝗼𝗿𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝗮𝗿𝗹𝘆 – Don't overlook this critical component. Even a simple vector store to maintain context and retrieve relevant information will significantly enhance your agent's capabilities. 5. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 – LangGraph, LlamaIndex, and CrewAI provide solid foundations without reinventing the wheel. They offer battle-tested patterns for orchestration, memory management, and tool integration. The most important step? Just start building. Your first implementation doesn't need to be perfect. Begin with a minimal viable agent, collect feedback, and iterate rapidly. What specific use case would you tackle first with an autonomous agent? What's holding you back from getting started?

Agentic AI: Your GCC's New Engine for Enterprise Transformation and Innovation at Scale The era of merely automating tasks is behind us. In the age of Agentic AI, your Global Capability Center (GCC) transforms into a true engine of enterprise transformation. Today's AI excels at automating repetitive tasks. But tomorrow's, and indeed, Tholons' vision for GCCs, involves Agentic AI — intelligent systems capable of thinking, planning, and acting autonomously. They will reimagine your operations, optimizing workflows, drastically reducing costs, and unlocking unprecedented value across your entire enterprise. This isn't just innovation; it's a strategic shift that redefines how your business functions. Your Roadmap to Deploying Business-Ready AI Agents from Your GCC: At Tholons Inc., we've developed a comprehensive, step-by-step guide to transforming your operations – from laying robust foundations to achieving scaled impact with Agentic AI. Tholons Inc. Partnership delivers: ✔️ Beyond Chatbots & LLMs: Understand why Agentic AI uniquely redesigns end-to-end processes like supply chain, customer service, and finance, rather than just augmenting specific interactions. This is about systemic change. ✔️ Core Foundations for Enterprise Reliability: We'll embed advanced AI/ML, LLMs, and prompt engineering techniques to ensure your AI agents operate with the precision and reliability demanded by enterprise-grade applications. ✔️ Frameworks That Deliver Complex Business Logic: Leverage industry-leading frameworks like LangChain, CrewAI, and AutoGen, expertly tailored by Tholons to handle the intricate logic of your most complex business processes. ✔️ Operational Intelligence for Context-Aware Actions: Equip your agents with advanced capabilities for Memory, Retrieval-Augmented Generation (RAG), and sophisticated decision-making, enabling truly context-aware business actions that drive efficiency and accuracy. ✔️ Self-Optimizing Agents for Continuous Improvement: Implement reinforcement learning strategies that allow your AI agents to continuously learn, adapt, and improve their performance, ensuring perpetual process optimization. ✔️ Scaling Trust for Mission-Critical Workflows: We prioritize robust deployment strategies, stringent security protocols, and continuous monitoring to build confidence and ensure the reliability of your mission-critical Agentic AI deployments. ✔️ Real-World Impact Across Your Enterprise: See tangible results through case studies and direct application in areas like procurement automation, intelligent HR operations, optimized logistics networks, and truly transformed customer experiences. → Transform Reactive Workflows into Strategic, Self-Directed Assets. Partner with Tholons to turn your GCC into the epicenter of Agentic AI-driven enterprise transformation. Contact Us: 📧 inquiry@tholons.com 🌐 www.tholons.com #GCC #GCCIndia #AIAgents #AI

I recently sat down with Andreas Welsch for a live conversation about how 𝘀𝗺𝗮𝗹𝗹 𝗮𝗻𝗱 𝗺𝗶𝗱𝘀𝗶𝘇𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 can get started with #AgenticAI - and do it in a way that’s p͟r͟a͟c͟t͟i͟c͟a͟l͟, s͟t͟r͟a͟t͟e͟g͟i͟c͟, and grounded in r͟e͟a͟l͟ ͟o͟u͟t͟c͟o͟m͟e͟s͟. Check it out here: https://lnkd.in/eTcWzasH  𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗮 𝗳𝗲𝘄 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝗼𝘂𝗿 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻: • 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀. They’re goal-driven systems that can break down complex tasks, make decisions, and deliver results. The goal isn’t to replace humans, but to remove repetitive work so people can focus on higher-value activities. • 𝗧𝗵𝗲𝗿𝗲’𝘀 𝗮 𝗯𝗶𝗴 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗮𝗻𝗱 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻. While older RPA tools follow strict rules, AI agents can adapt, reason, and solve problems. Both have value - it’s about choosing the right tool for the job. • 𝗕𝗲 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗳 𝘁𝗵𝗲 𝗵𝘆𝗽𝗲. Not every product labeled “agentic” actually is. Ask vendors tough questions. What does the solution really do? How flexible is it? How well does it connect to your systems? • 𝗣𝗲𝗼𝗽𝗹𝗲 𝘀𝘁𝗶𝗹𝗹 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝘀𝘁. AI is a tool, not a strategy. The real value comes when you equip your teams, encourage innovation, and focus on how humans and machines can work better together. • 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. Don’t shop for tools. Identify bottlenecks in marketing, sales, support, or internal operations. Look for tasks that are slow, repetitive, or decision-heavy - and explore how AI agents can help. If you’re not sure where to begin: • Review your existing software stack. Many platforms already offer AI features. • Focus on pain points, not just new tech. • Prioritize use cases where speed and smart decision-making make a difference. • Agentic AI isn’t about replacing people. It’s about amplifying what your team can achieve by automating what holds them back. Thanks again to Andreas for the great conversation. We’ll definitely be hosting more sessions like this soon. If you missed it, 𝗳𝗼𝗹𝗹𝗼𝘄 𝘂𝘀 for updates. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝘆𝗼𝘂: Where do you see the biggest opportunity for agentic AI in your business? #LinkedInOfficeHours LinkedIn for Learning #SMB  #AIAdoption  #Automation  #Leadership

You don’t need to be an AI agent to be agentic. No, that’s not an inspirational poster. It’s my research takeaway for how companies should build AI into their business. Agents are the equivalent of a self-driving Ferrari that keeps driving itself into the wall. It looks and sounds cool, but there is a better use for your money. AI workflows offer a more predictable and reliable way to sound super cool while also yielding practical results. Anthropic defines both agents and workflows as agentic systems, specifically in this way: 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: systems where predefined code paths orchestrate the use of LLMs and tools 𝗔𝗴𝗲𝗻𝘁𝘀: systems where LLMs dynamically decide their own path and tool uses For any organization leaning into Agentic AI, don’t start with agents. You will just overcomplicate the solution. Instead, try these workflows from Anthropic’s guide to effectively building AI agents: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁-𝗰𝗵𝗮𝗶𝗻𝗶𝗻𝗴:  The type A of workflows, this breaks a task down into sequential tasks organized and logical steps, with each step building on the last. It can include gates where you can verify the information before going through the entire process. 𝟮. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: The multi-tasker workflow, this separates tasks across multiple LLMs and then combines the outputs. This is great for speed, but also collects multiple perspectives from different LLMs to increase confidence in the results. 𝟯. 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: The task master of workflows, this breaks down complex tasks into different categories and assigns those to specialized LLMs that are best suited for the task. Just like you don’t want to give an advanced task to an intern or a basic task to a senior employee, this find the right LLM for the right job. 𝟰. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿-𝘄𝗼𝗿𝗸𝗲𝗿𝘀: The middle manager of the workflows, this has an LLM that breaks down the tasks and delegates them to other LLMs, then synthesizes their results. This is best suited for complex tasks where you don’t quite know what subtasks are going to be needed. 𝟱. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗿: The peer review of workflows, this uses an LLM to generate a response while another LLM evaluates and provides feedback in a loop until it passes muster. View my full write-up here: https://lnkd.in/eZXdRrxz

Agentic AI is evolving and we are seeing four emerging patterns. Agentic AI systems don’t just answer questions, but actively do, decide, and drive business outcomes. If you’re mapping your organization’s AI journey, understanding the levels of agentic capability is crucial for unlocking both monetization and margin potential. Most of our customers are using Level 1, some are using Level 2 and Level 3. Level 4 has multiple challenges with sandboxing, security and governance. Mainly startups that are innovating in this space. Enterprises mostly are sitting this one out, for now. The Four Levels of Agentic AI: From Queries to Autonomy 1. Query Agents: The Generative Foundation These are your classic AI assistants with a plus: users ask questions, get answers. They support employees by surfacing information fast but don’t act on it. Think: knowledge retrieval, basic chatbots, or AI-powered search. 2. Task Agents: Getting Things Done Agents now complete discrete tasks—like scheduling meetings, drafting emails, or pulling reports. They access corporate knowledge and integrate with existing workflows, but still need human oversight. The payoff? Significant time savings and reduced manual effort, though boundaries and data quality remain key. 3. Workflow Agents: Orchestrating Complexity Here, agents handle multi-step workflows, integrating deeply into tech stacks and collaborating with other agents or systems. They plan, sequence, and adapt actions dynamically—think troubleshooting IT issues, automating onboarding, or managing campaigns. These agents leverage proprietary data and can iterate based on results, reducing manual intervention and boosting efficiency. 4. Autonomous Agents: The Future, Now The pinnacle: agents that understand entire business processes, access multiple systems, and operate with minimal human oversight. They don’t just follow instructions—they set goals, adapt to new scenarios, and optimize for outcomes in real time. Why This Matters As you move up the agentic ladder, both the value and margin potential increase dramatically. Query agents save time; autonomous agents can reinvent entire workflows, drive innovation, and open new business models. According to Gartner, Agentic AI will make 15% of all organizational decisions autonomously by 2028. Key Takeaways for Leaders a. Start with the basics: Ensure your data is organized and accessible to enable higher levels of agentic automation. b. Define governance and boundaries: Set clear rules for agent autonomy to balance efficiency with oversight. c. Invest in integration: The real value comes when agents orchestrate across systems, not just within silos. d. Prepare for autonomy: As agents become more capable, they’ll need less human intervention—freeing your teams for higher-value work. Agentic AI isn’t just a technology trend—it’s the new foundation for digital business. What are your thoughts about evolution of Agentic AI?

After 2 months deep in #AgenticAI workspace, here's what I've discovered: Your agents are only as good as your business process documentation. Building #AgenticAI without process documentation is like giving Thor's hammer to someone who doesn't know they need to be worthy The power is there, but without the right foundation, it's just an expensive paperweight. Think of documentation as the "worthiness" factor for AI agents. Just like Mjolnir responds only to those who understand its true nature, Agentic AI systems need exhaustive business rules to function effectively. Think detailed training manuals that capture every decision point, exception, and nuance your teams navigate daily. The plot twist: Most organizations lack comprehensive process documentation. What exists is often outdated, incomplete, or trapped in tribal knowledge. Here's your Avengers team for Agentic AI success: 1. Business process experts who map the real workflows (not the theoretical ones) 2. Technology architects who understand system integrations 3. AI specialists who translate human decisions into agent behaviors To AI startups and product managers: Stop leading with the hammer's power. Start by helping organizations become "worthy" through comprehensive process mapping. The companies that dominate B2B Agentic AI will be those who obsess over understanding what people actually do, not what they say they do. The real superpower isn't having the most advanced AI models. It's having the clearest blueprint of how your business actually operates. Only then will your Agentic AI lift off. #AgenticAI #DigitalTransformation