How to Use Agentic AI in Business Workflows

Agentic AI won’t reward the teams with the cleverest agents. It’ll reward the teams with the tightest operating model. → Agents will fail. → They'll misunderstand goals. → Execute wrong steps. → Hallucinate early. This is normal. It's not a reason to avoid agents. It's a reason to build systems that handle failure gracefully. Here's the 5-layer framework I use with CXOs preparing for agentic AI: 𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 Not every workflow should be automated. Start with workflows that are: → Repetitive and predictable → High-volume → Low-risk if errors occur → Currently consuming significant human time These are your pilot candidates. Save the high-stakes workflows for later. 𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝗥𝗼𝗹𝗲𝘀 𝗔𝗿𝗼𝘂𝗻𝗱 𝗔𝗴𝗲𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 For each workflow you automate, ask: → Who sets goals for the agent? → Who reviews outputs? → Who handles exceptions? → Who provides feedback for improvement? These become the new job responsibilities. Define them before you deploy. 𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 Your workforce needs new capabilities: → Prompt engineering (communicating clearly with agents) → Output evaluation (knowing good from bad) → Workflow design (breaking goals into agent-executable steps) → Exception handling (knowing when to intervene) This isn't optional training. It's core job competency. 𝗟𝗮𝘆𝗲𝗿 𝟰: 𝗖𝗿𝗲𝗮𝘁𝗲 𝗙𝗮𝘂𝗹𝘁-𝗧𝗼𝗹𝗲𝗿𝗮𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Assume agents will fail. Build accordingly: → Human checkpoints at critical decision points → Audit trails so you can see what agents did and why → Feedback loops so agents improve over time → Kill switches for when things go wrong The bottleneck isn't agent capability. It's organizational readiness to manage agents effectively. 𝗟𝗮𝘆𝗲𝗿 𝟱: 𝗘𝘃𝗼𝗹𝘃𝗲 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀𝗹𝘆 Capabilities move fast. Your operating model has to move faster. Build muscle for continuous adaptation: → Regular workflow reviews (what else can agents handle now?) → Ongoing skill development (what new capabilities do people need?) → Technology evaluation cycles (what new agent features should we adopt?) The leaders who get this right treat agentic AI as an organizational transformation, not a technology project. The technology is ready. The question is whether your organization is. Save this framework for your next AI planning session.

Your business doesn’t need another chatbot. It needs an agent that owns a result. Most teams bought “answers.” Operators need outcomes. Agentic AI isn’t Q&A. It’s plan → act → check → escalate until done. Start where it pays back fast: one workflow with a clear finish line. Missed-call follow-up. Intake routing. Weekly ops recap. System (operator edition): ✅ Role & goal: one job, one KPI (ex: reduce exceptions to <15%) ✅ Tools: the 3–5 it must touch (CRM, docs, email/SMS, ledger, search) ✅ Guardrails: rate limits, retries, human stop, audit log ✅ Memory: retrieval from approved sources with permissions ✅ Loop: plan → act → verify → write the record ✅ Escalation: “can’t complete” triggers owner + context bundle Proof you can measure (beyond “time saved”): ✅ Reasoning accuracy (grounded & cited) ✅ Autonomy rate vs. human handoffs ✅ Cycle time per case, not per click ✅ CX deltas: fewer repeat questions, faster resolutions Build vs. buy vs. hybrid is a platform call, not a tool swipe. If your APIs, logging, and sandbox aren’t ready, pilot first small scope, real metric. New habits for managers: ✅ Assign an owner per flow ✅ Set a pass bar before go-live ✅ Review exceptions weekly, promote what works Bottom line: move from “answers in threads” to “outcomes in systems.” Artifact or it didn’t happen: if the agent didn’t write to the system of record, it didn’t ship.

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible. This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management. Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture : 1.🔸Decomposition Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator). 2.🔸Communication Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration. Support both single-agent and multi-agent setups for small or distributed workflows. 3.🔸Deployment Deploy agents in containerized or serverless environments using Docker or Modal. Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows. 4.🔸Data & Discovery Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access. Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning. 5.🔸Testing & Observability Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions. Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate. 6.🔸UI & Style Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes. Enable collaborative, proactive, and goal-driven reasoning among multiple agents. 7.🔸Security Protect access with token-based authorization and data encryption. Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution. 8.🔸Cross-Cutting Concerns Handle configuration, secrets, and environment management. Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design. Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns. Save this post to understand the architecture that powers the next generation of AI systems #AgenticAI

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?

AI agents are moving from pilot projects to production, and thus the balance sheet. Today's post by Zahra Bahrololoumi CBE, CEO of Salesforce UK/I demostrates that agentic-AI is entering the mainstream. Boards now ask two questions: - Where will they increase economic value? - How will we keep them inside our risk appetite? The answers start with process and governance, not with algorithms. Understand your core workflows at event log-level detail. Measure latency, hand-offs, rework, and exception paths. The data tells you which tasks an agent can take over, what the economic upside is, and how you will audit performance later. Create an AI Governance Council that owns: - A formal risk taxonomy aligned to UK Operational Resilience Act, GDPR, and the EU AI Act. - Stage-gating rules that move use cases from sandbox to production. - Policy-as-code libraries, version-controlled and automatically testable. For every use case, document data domains, transaction values, exception types, and human-in-the-loop triggers, and the infrastructure components. Clear boundaries limit exposure, quantify residual risk, and provide a roadmap for scaling autonomy with confidence. Discovery, analysis, optimisation, automation, and monitoring remain essential. These activities do not disappear with AI; they become a strategic imperative. Agents simply become new actors in an end-to-end workflow that already has ownership (RACI), service-level targets, and a continuous-improvement cadence. Latency, exception rates, and compliance risks must flow into a dashboard consumed by both operations and governance teams. If you cannot see it in near real time, you cannot defend it when auditors or regulators arrive. Autonomous agents amplify the processes they inhabit. Pairing them with disciplined Business Process Management (BPM) and data-driven process intelligence turns isolated wins into an auditable, enterprise-scale operating model. #BPM #Process #Intelligence #AI #Agents #Business

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

AI agents aren’t the future — they’re already changing how work gets done. Everyone’s racing to “add AI” into their products. But few stop to ask why — or how it should actually work once it’s there. A report from QuantumBlack, AI by McKinsey takes a bold stance: The real advantage isn’t in using AI tools. It’s in reimagining how work, decisions, and collaboration flow when software can act. Here are the biggest takeaways worth every UX and product team’s attention 👇 1️⃣ Rethink how AI fits in your business. Don’t bolt AI onto old workflows. Design for how decisions are made when software can think and act. 2️⃣ Reinvent your processes — don’t just automate. The biggest value comes when workflows are rebuilt for agentic AI — adaptive, personalized, and scalable. 3️⃣ Go beyond off-the-shelf solutions. Chatbots and copilots are quick wins but rarely strategic. Build AI agents around your unique logic, data, and culture. 4️⃣ Governance isn’t optional. As autonomy increases, so do risks. Trust, transparency, and clear control are the real enablers of scale. 5️⃣ Prepare your people and technology. AI transformation isn’t tech-first — it’s mindset-first. Upskill teams, build openness, and make systems ready for new workflows. This report is a must-read for anyone shaping the next generation of intelligent workflows. It’s not just about AI — it’s about rethinking how work happens. 📘 Full report: “Seizing the Agentic AI Advantage” by QuantumBlack, McKinsey. Follow me for more insights on the Empathic Web, AI, and design. #AI #UXDesign #UX #Design #AIAgents #ProductManagement #AgenticAI #DesignLeadership #Innovation #DigitalTransformation #McKinsey #FutureOfWork

Agentic AI is quietly reshaping UX research and human factors. These systems go beyond isolated tasks - they can reason, adapt, and make decisions, transforming how we collect data, interpret behavior, and design with real users in mind. Currently, most UX professionals experiment with chat-based AI tools. But few are learning to design, evaluate, and deploy actual agentic systems in research workflows. If you want to lead in this space, here’s a concise roadmap: Start with the core skills. Learn how LLMs work, structure prompts effectively, and apply Retrieval-Augmented Generation (RAG) to tie AI reasoning into your UX knowledge base: 1) Generative AI for Everyone (Andrew Ng) - broad introduction to generative AI, prompt engineering, and how generative tools feed autonomous agents. https://lnkd.in/eCSaJRW5 2) Preprocessing Unstructured Data for LLM Apps - shows how to structure data for AI-driven research. https://lnkd.in/e3AKw8ay 3)Introduction to RAG - explains retrieval-augmented generation, which makes AI agents more accurate, context-aware, and timely. https://lnkd.in/eeMSY3H2 Then you need to learn how agents remember past interactions, plan actions, use tools, and interact in adaptive UX workflows. 1) Fundamentals of AI Agents Using RAG and LangChain - teaches modular agent structures that can analyze documents and act on insights. This one has a free trial. https://lnkd.in/eu8bYdjh 2) Build Autonomous AI Agents from Scratch (Python) - hands-on guide for planning and prototyping AI research assistants. This one also has a free trial. https://lnkd.in/e8kF-Hm7 3) AI Agentic Design Patterns with AutoGen - reusable architectures for simulation, feedback analysis, and more. https://lnkd.in/eNgCHAss 3) LLMs as Operating Systems: Agent Memory - essential for longitudinal studies where memory of past behavior matters. https://lnkd.in/ejPiHGNe Finally, you need to learn how to evaluate, debug, and deploy agentic systems at scale in real-world research settings. 1) Building Intelligent Troubleshooting Agents - focuses on workflows where agents help researchers address complex research challenges. https://lnkd.in/eaCpHXEy 2) Building and Evaluating Advanced RAG Applications - crucial for high-stakes domains like healthcare, where performance and reliability matter most. https://lnkd.in/eetVDgyG

Here's what building AI agents for my business taught me. Most people are completely wrong about this. We've invested heavily in building internal AI agents at ColdIQ using n8n, and after months of testing, I need to cut through the hype. Here's what actually works (and what's just BS): 1️⃣ Non-Agentic Workflows (where you should start) This is basic AI usage. One input -> LLM Request -> One output. Done. Examples from our daily operations: - Prospect research: Input LinkedIn profile → Output enriched contact data - Email copywriting: Input campaign brief → Output personalized sequences - Call summarization: Input transcript → Output action items 2️⃣ Agentic Workflows (the sweet spot for revenue) This is where multiple tools work together in sequence, with some basic decision-making. Real example with a workflow we built: Goal: "Turn LinkedIn engagement into qualified pipeline" The workflow: → Monitors our LinkedIn posts for engagement → Enriches engaged users with Clay → Scores them against our ICP criteria → Routes qualified leads to different sequences → Triggers personalized outreach in Instantly → Updates CRM with context This runs 24/7 without human intervention. 3️⃣ True AI Agents (overhyped, underdeveloped) Here's the reality: Most "AI agents" are just fancy workflows with better marketing. Real agents should: - Understand context and make complex decisions - Learn from outcomes and improve over time - Handle unexpected situations without breaking We're building towards this, but the technology isn't quite there yet for most business use cases. The truth about AI agents: 99% of what people call "AI agents" are just well-designed workflows. And that's actually good news - workflows are reliable, predictable, and profitable. Don't get caught up in the hype. Focus on building solid automation that actually generates revenue. Start simple. Build workflows that solve real problems. Then gradually add complexity. Most companies wanna jump straight to "AI agents" and end up with broken, unreliable systems. What automation challenge are you trying to solve right now? Drop a comment - happy to share what's actually working.

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐢𝐧 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬: 𝐇𝐲𝐩𝐞 𝐯𝐬 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧𝐬 🤖 Most companies don’t need “autonomous agents.” They need reliable automations that use AI at the right moments, without breaking. Here’s what AI agents do well today 👇 ✔️ 𝗪𝗵𝗲𝗿𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝗻 (𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻𝘀) 𝗧𝗿𝗶𝗮𝗴𝗲: classify emails/tickets/leads and assign priority + owner 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗲: meetings, threads, docs → clear action items + owners 𝗥𝗼���𝘁𝗲: send the right task to the right tool/person (CRM, Slack, Jira) 𝗗𝗿𝗮𝗳𝘁: replies, SOPs, proposals, follow-ups (with human approval) 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 + 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: pull fields from messy text → clean CRM/Sheets updates ⚠️ 𝗪𝗵𝗲𝗿𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝗯𝗿𝗲𝗮𝗸 (𝘁𝗵𝗲 “h𝘆𝗽𝗲 𝘇𝗼𝗻𝗲”) 𝗨𝗻𝗰𝗹𝗲𝗮𝗿 𝗴𝗼𝗮𝗹𝘀: vague prompts = random outcomes 𝗕𝗮𝗱 𝗱𝗮𝘁𝗮: missing context, outdated SOPs, messy CRM = wrong decisions 𝗡𝗼 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: no validation rules, approvals, or fallback paths 𝗘𝗱𝗴𝗲 𝗰𝗮𝘀𝗲𝘀: exceptions and “special customers” cause cascading errors 𝗧𝗼𝗼𝗹 𝗰𝗵𝗮𝗼𝘀: too many integrations, no monitoring, silent failures ✅ 𝗧𝗵𝗲 𝘀𝗶𝗺𝗽𝗹𝗲 𝗿𝘂𝗹𝗲: 𝗟𝗲𝘁 𝗮𝗴𝗲𝗻𝘁𝘀 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 𝗮𝗻𝗱 𝗽𝗿𝗲𝗽𝗮𝗿𝗲. Don’t let them finalize and send without checks. 𝗛𝗼𝘄 𝗜 𝗯𝘂𝗶𝗹𝗱 𝗮𝗴𝗲𝗻𝘁 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗰𝗮𝗹𝗲: 1. Start with a single workflow (ex: inbound lead → qualify → route) 2. Add human-in-the-loop at the risky step (send / update / approve) 3. Add validation + logging (required fields, confidence threshold, audit trail) 4. Measure time saved + error rate weekly ⏳ 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗻𝗼𝘄: Teams that treat agents like “magic employees” get burned. Teams that treat agents like automation components get compounding ROI. 👉 Want my 𝗔𝗴𝗲𝗻𝘁 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 (10 questions to decide if a process is agent-ready)? Comment “AGENT” oe send a DM and I’ll drop it. #AutomationGuy #ScaleThroughAutomation #AIAutomation #BusinessAutomation #AIAgents Follow me for AI & Automation updates and resources: https://lnkd.in/gjG8gvRd