Online Inventory Management Systems

To what extent should every AI agent deployed in your organisation be risk assessed and catalogued via formal processes? This is one of the most challenging questions for AI governance today. Today's enterprise AI governance frameworks were built for traditional machine learning and generative AI . The underpinning logic is that AI systems and applications should be risk assessed by governance teams, prior to deployment in production. The purpose of this is to identify risks and corresponding mitigations that, if implemented, would lower the risks to an acceptable level. Agentic AI poses fundamental challenges to the way in which these processes operate in most enterprises. Consider the following four archetypes and patterns of agentic AI development: ➡️ Citizen developers using no- and low-code tools to build agents to support their personal or team productivity ➡️ An approved vendor enabling the deployment of out-of-the-box or custom agents in their platform ➡️ Building custom agents that operate within a specific platform or that connect to other platforms, to retrieve data and execute actions ➡️ Building an agentic platform, consisting of multiple agents, to enhance a specific business process The fundamental challenge is that, no matter the archetype or development pattern, agents can use data, invoke tools, and execute actions in a manner that constitutes a high-risk activity. Therefore, the core unit of risk is the action taken or decision supported. However, the speed and scale at which agents are being developed and deployed means that manual assessment, review, and approval of each one will not scale. Although well-intentioned, such an approach will collapse on itself. Furthermore, it would be disproportionate, as many agents will be performing trivial tasks, given the ease with which they can be developed. To address this challenge, organisations need to re-evaluate precisely what type of agents or agentic systems should remain in scope of their AI risk assessment process, and how to govern everything that isn't. Platform-level controls are essential. Whether agent registration, observability, and cataloguing, or action scope restriction, reversibility, and bounded autonomy, such controls should be enabled at the platform level. Therefore, ensure agents are only being deployed on approved platforms that support governance by design. Then, leverage telemetry to auto-populate AI governance inventories and implement guardrails to detect and prevent non-compliant actions before they can be executed. In doing so, you govern agentic actions without manually reviewing each one. Most importantly, educate the workforce on responsible development and use of agents. In particular, raise awareness of what constitutes a high-risk or prohibited use case, provide playbooks and resources that enable the business to manage risks, and be clear where responsibility and accountability lies when agents go rogue.

Shipping AI agents into production without governance is like deploying software without security, logs, or controls. It might work at first. But sooner or later, something breaks - silently. As AI agents move from experiments to real decision-makers, governance becomes infrastructure. This framework breaks AI Governance into the core functions every production-grade agent system needs: - Policy Rules Turn business and regulatory expectations into enforceable agent behavior - defining what agents can do, must avoid, and how they respond in restricted scenarios. - Access Control Limits agents to approved tools, datasets, and systems using identity verification, RBAC, and permission boundaries — preventing accidental or malicious misuse. - Audit Logs Create a full activity trail of agent decisions: what data was accessed, which tools were called, and why actions were taken — making every outcome traceable. - Risk Scoring Evaluates agent actions before execution, assigns risk levels, detects sensitive operations, and blocks unsafe decisions through thresholds and safety scoring. - Data Privacy Protects confidential information using PII detection, encryption, consent management, and retention policies — ensuring agents don’t leak regulated data. - Model Monitoring Tracks real-world agent performance: accuracy, drift, hallucinations, latency, and cost - keeping systems reliable after deployment. - Human Approvals Adds human-in-the-loop controls for high-impact actions, enabling escalation, overrides, and sign-offs when automation alone isn’t enough. - Incident Response Detects failures early and enables rapid containment through alerts, rollbacks, kill switches, and post-incident reporting to prevent repeat issues. The takeaway: AI agents don’t just need intelligence. They need guardrails. Without governance, agents become unpredictable. With governance, they become enterprise-ready. This is how organizations move from experimental AI to trustworthy, compliant, production systems. Save this if you’re building agentic systems. Share it with your platform or ML teams.

I've reviewed Anthropic's Risk Report for Claude Opus 4.6 because many of our enterprise customers are actively deploying AI agents into production environments. When those systems fail, the consequences are operational, financial and reputational. Most of the reaction centers on the headline that catastrophic risk is very low but not negligible. What matters more for customers and future customers is how risk actually manifests inside live enterprise systems and what that means for uptime, data integrity and compliance. It does not look like a breach. It looks like business as usual. An agent subtly influencing procurement decisions. A finance workflow that starts omitting inconvenient data. Permissions that expand over time without clear oversight. Anthropic describes a scenario called Persistent Rogue Internal Deployment, where an AI system with privileged access creates a less monitored instance of itself and continues operating inside production systems. In a real enterprise environment, that translates into downtime, data exposure or regulatory impact. The organizations at greatest risk are not the ones moving cautiously. They are the ones who pushed agents into production without adding an operational governance layer. We have seen this pattern before in cloud adoption. Technology advances quickly, and controls often lag behind. That gap is where exposure grows. So what should enterprise IT and security teams do now? 1. Constrain actions, not just access. Define what an agent can set in motion and enforce least privilege at the identity level, just as you have done for human users for decades. 2. Log actions, not just outcomes. Maintain an auditable trail of what the agent did, where and what triggered it, the same standard applies to human operators in regulated environments. 3. Automate your tripwires. Do not rely on people to catch machine speed behavior. Build policy enforcement and anomaly response into the loop. 4. Audit your agent footprint. Inventory every agent, its owner, permissions and kill path. Governance starts with visibility and most enterprises are still building it. The window to build these guardrails is now, before the agent workforce scales. At Rackspace, 25 years of running mission-critical systems have taught us that trust without controls creates exposure. We build and operate AI infrastructure with governance embedded from day one because customers need speed, resilience and measurable outcomes, not experiments in production. What this means for you is simple. Move forward on AI with confidence, but make operational governance part of the foundation so scale strengthens your business instead of introducing risk.

Inventory is the silent killer of consumer brands. Too much stock? Your cash is stuck. Too little? Customers walk away. There’s no perfect forecast — you’ll either overstock or run out of something critical. Last year we had a horrid quarter with overstocking on all the slow moving and OOS on all fast moving walking into festive with very less fuel.  We have been building this first off excel sheets and now in what looks like a system (built off Replit). Here’s what worked for us at Koparo: 1. Move Beyond Gut Feel For a long time, reorder decisions were instinct-based or working off plain averages. That stopped working as we scaled. We introduced formulas: ReorderPoint=(AverageDailyDemand×LeadTime)+SafetyStockReorder Point = (Average Daily Demand × Lead Time) + Safety StockReorderPoint=(AverageDailyDemand×LeadTime)+SafetyStock This one change helped us avoid both empty shelves and excess stock. 2. Get the Order Size Right Knowing when to reorder isn’t enough. You need to know how much: To be honest this is still hard but if your unit costs don’t fall too much based on order volume then just be conservative on this with a very accurate handle on actual vendor lead times and not just average but in season time. This helped us strike a balance between ordering frequently and locking cash in inventory. 3. Safety Stock That Makes Sense Earlier, we’d just add 20% “for safety.” Now, buffers are calculated based on actual demand variability and service levels. No more guesswork. 4. Lead Times Aren’t Assumptions We learned the hard way that vendor timelines on paper don’t match reality. Our system now tracks actual lead times — which changed planning dramatically and yes also our vendors. 5. Automate the Triggers We built an in-house system (on Replit) with auto-replenishment triggers. When stock hits ROP, it suggests orders. No manual chasing, no panic buying. What’s the impact? ✔ Fewer stock-outs ✔ Lower working capital ✔ Predictable operations We’re still evolving this — and have built a simple system on Replit. It’s far from sophisticated, but it has improved our decision-making, forced us to make assumptions real, and saved at least 10 hours per week. Curious: How are you managing inventory? DIY system, off-the-shelf software, or still spreadsheets? #InventoryManagement #SupplyChain #D2C #Koparo Kshitij Ranjan Vishal Singh Saurabh Nidar Abhishek Sharma Rahul Gaur

AI Agent Studio Inspiration Series Next up: transforming B2B commerce with an end-to-end SmartQuote Navigator agent, built using Oracle Fusion AI Agent Studio. Always Built in, Not Bolted on! Today’s B2B buyers expect a fast, intuitive, self-service experience yet complex product configurations, pricing rules, and manual approvals often slow the process to a crawl. This agent changes that. What it does: - Smart Config Validation Buyers submit custom product needs, and the agent instantly validates configuration feasibility. - Real-Time Pricing + Margin Checks Instantly retrieves pricing via CPQ and validates margin thresholds. - Automated Quote Generation Delivers a CPQ-backed quote within seconds with no human intervention required. - AI-Driven Approvals Compliant quotes auto-approve; edge cases are escalated to managers as needed. - Order Conversion + Sync Seamlessly converts approved quotes into sales orders in Fusion, with real-time updates sent to the buyer’s dashboard. This is a real AI Agent working across Oracle Commerce, CPQ, and Fusion Sales delivering a fully autonomous quote-to-order experience. Built by these Hackathon Champions!: Ramesh Vangala Yashwanth Reddy Vikas Jain This is how AI can elevate customer experience while removing manual bottlenecks from the sales cycle. #OracleAI #FusionAI #CX #AIStudio #AIAgentStudio #SmartQuote #CPQ #QuoteToOrder #GenAI #AIInspiration #CustomerExperience

𝐌𝐨𝐬𝐭 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐬𝐭𝐮𝐜𝐤 𝐨𝐧 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧. Which LLM. Which framework. Which prompt structure. Microsoft just made it clear:  The model isn't the strategic decision.  The data architecture is. Your AI agent doesn't fail because GPT or Claude is wrong.  It fails because your data sits in 9 silos, governance is split across 4 teams, and agents have no coherent way to reach any of it. 𝟏. 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐋𝐚𝐲𝐞𝐫 (𝐂𝐫𝐨𝐬𝐬-𝐂𝐮𝐭𝐭𝐢𝐧𝐠) • Entra for identity and access control. • Agent 365 as the agent control plane. • Purview for data governance. • Defender for security. Every agent operates inside this layer. No exceptions. 𝟐. 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐋𝐚𝐧𝐝𝐢𝐧𝐠 𝐙𝐨𝐧𝐞 • Azure-native foundation policy, monitoring, management groups, identity, connectivity. • The boring infrastructure that makes everything else compliant. 𝟑. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐧𝐝𝐢𝐧𝐠 𝐙𝐨𝐧𝐞𝐬 • One per data domain. • Azure Databricks for data products. • Microsoft Foundry for agents. • Databases mirror to Fabric OneLake automatically. 𝟒. 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐠𝐞𝐧𝐭 𝟑𝟔𝟓 • The control plane sitting above everything. • Dashboards, AI model training, oversight of every agent in the org. 𝟓. 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜 𝐚𝐧𝐝 𝐎𝐧𝐞𝐋𝐚𝐤𝐞 • Single data lake for the organization. • One workspace per data domain. • Power BI and Data Science attach directly. • Foundry IQ feeds agents from here. This is the layer most teams underestimate and the one that determines whether agents actually work. 𝟔. 𝐏𝐮𝐫𝐯𝐢𝐞𝐰 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐂𝐚𝐭𝐚𝐥𝐨𝐠 • Governance domains mirror data domains. • Single source of truth for what data exists, who owns it, and what agents can touch. 𝟕. 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞𝐬 • On-premises, Dataverse, M365, Azure, Google Cloud, Amazon S3. • Multi-cloud by design. Microsoft's bet isn't on having the best model. It's that the company with the cleanest data layer, strictest governance, and deepest enterprise integration wins the agent era regardless of which model sits on top. Which layer in your Microsoft stack is the bottleneck right now? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: Found this useful? Join 2,300+ AI architects and engineering leaders from Microsoft, Google, IBM, PwC and others reading my weekly newsletter 𝗗𝗶𝗮𝗿𝘆 𝗼𝗳 𝗮𝗻 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁. I break down real enterprise AI systems, agentic patterns, and what actually works in production. ✉️ Free subscription: https://lnkd.in/exc4upeq #Azure #MicrosoftAI #EnterpriseAI

An upstream table fails at 2am. Your agent detects it in minutes, identifies root cause, generates a remediation plan. Then it asks: who do I tell? No declared owner. The table feeds finance dashboards, growth pipelines, and three data science pods. The agent has two choices: notify everyone downstream and create exactly the conditions for alert fatigue, or resolve what it can and tell no one. Silent failure. The agent did everything right. Your org chart doesn't exist in a form it can read. For years, data teams navigated this through tribal knowledge. Someone always knew who really owned which tables. It was fragile, degraded every time someone left, produced inconsistent outcomes. But it mostly worked. So no one formalized it. Agents can't operate on tribal memory. They work on metadata, lineage graphs, and whatever ownership model you've explicitly encoded. The org chart has to exist before the incident happens, in a structured, queryable form. What organizations actually need is an escalation graph. Not a Confluence page. Not a RACI matrix someone updates quarterly when they remember. A version-controlled, queryable data model that maps assets to owners to escalation paths to notification channels, treated with the same rigor as a lineage graph or schema registry. Four properties that matter: 1. Narrow primary ownership. One team per asset, tiebreaker declared if ownership is genuinely shared. 2. Dependency-aware routing. The agent distinguishes who needs to fix it from who needs to know about the impact. Conflating those two is precisely how alert fatigue is born. 3. Severity-tiered routing. P1 failures route differently than P3 quality degradations. Agents execute policy humans designed in advance, not triage from first principles. 4. Feedback loops. When the escalation path produces wrong results, that's your signal the ownership graph has gone stale. Build the correction mechanism before you need it. Agentic systems are forcing organizations to build the ownership models they should have built years ago. Unglamorous. Absolutely necessary. Has your team started building this? And if you have, what broke first? #dataquality #dataobservability #AIagents

🚀 Excited to share my latest project: a fully autonomous Smart Warehouse Management System built using the Agent Communication Protocol (ACP)! This innovative system features four intelligent agents InventoryBot, OrderProcessor, LogisticsBot, and WarehouseManager working seamlessly together to manage stock, schedule deliveries, and handle reorders, all through standardized, real-time communication. 🌟 What is ACP?   ACP is a framework that enables autonomous agents to communicate effectively using structured messages with defined performatives (e.g., ASK, REQUEST_ACTION, TELL, CONFIRM). It ensures clear, reliable interactions, making it ideal for complex systems like smart warehouses where coordination is key. 🌟 How It Works:   Scenario 1: Stock Alert & Reorder - The OrderProcessor checks stock levels with InventoryBot and triggers reorders to maintain minimum availability (e.g., reordering to fill low laptop stock).  Scenario 2: Delivery Scheduling - The WarehouseManager directs LogisticsBot to schedule deliveries of goods, with LogisticsBot confirming the schedule including a tracking ID for transparency.  Scenario 3: Low Stock Management - InventoryBot alerts the WarehouseManager of low stock (e.g., 5 tablets), prompting a confirmation that 15 tablets are needed; the WarehouseManager then requests OrderProcessor to place an order for 15 tablets, with OrderProcessor confirming via a PO number.  The interactive frontend visualizes these interactions, complete with a Statistics dashboard (e.g., total messages: 6, active conversations: 3, registered agents: 4) to monitor performance, making it perfect for real-world adoption. 🏭Impact on Logistics: This solution transforms the logistics industry by reducing manual oversight, optimizing stock levels, and streamlining delivery schedules. With real-time data and automated reordering, warehouses can operate 24/7, cut costs, and improve customer satisfaction key drivers in today’s fast-paced supply chain. This showcase how AI and ACP can revolutionize warehouse management. Check out the demo video to see it in action!

World Economic Forum 𝗷𝘂𝘀𝘁 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗼𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 I've seen in December. In my two recent roles, we've deployed agents that optimize digital content on marketplaces, run retail media campaigns on platforms, create replenishment POs to prevent OOS, and identify opportunities for promotions or price increases. But here's my candid observation: most of us are moving faster than our governance frameworks can handle. This report adds a new perspective to the conversation. 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: ⬇️ 1. Technical architecture breakdown: application, orchestration, and reasoning layers—plus protocols like MCP and A2A that enable agent interoperability across enterprise systems. 2. 7-dimensional classification system: role, autonomy, authority, predictability, function, use case, and environment. This helps you understand exactly what level of risk you're dealing with. 3. Real-world evaluation framework: task success rates, completion time, tool-use accuracy, edge case robustness, and trust indicators. Finally, practical metrics for production deployment. 4. Risk assessment lifecycle: a 5-step process from defining context to managing residual risk—mapped directly to agent capabilities and deployment scenarios. 5. Progressive governance model: baseline controls for every agent (access, monitoring, testing, human oversight), with safeguards that scale as autonomy and authority increase. 6. Multi-agent ecosystems: the future isn't single agents—it's networks of agents that negotiate, transact, and collaborate. The report covers emerging risks like drift, misalignment, and cascading failures. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗖𝗣𝗚: ➜ Don't underestimate agents, they're not glorified chatbots; they are powerful and act on a much higher decision-making efficiency. They're making decisions on inventory, pricing, promotions, and customer data. ➜ Without classification, you can't assess risk. Without evaluation, you can't validate performance. Without governance, you're flying blind. Time to learn what's running under the hood. ➜ The framework gives you a playbook: start with low-autonomy agents, test rigorously, scale governance as capabilities grow. And don't rely on your IT and data science teams, get your hands dirty, please, even by watching and getting involved only. ➜ This isn't academic, from what I can tell, it's designed for practitioners who need to deploy safely today while preparing for multi-agent ecosystems tomorrow. The bottom line: adoption without governance is reckless. Governance without practical frameworks is paralysis. This report gives us both. Full paper is here: https://lnkd.in/eVuBJWps #AI #AIAgents #CPG #FMCG #Enterprise #Governance #Innovation

🚀 🌍 Movement Types (MM) Transaction/Event Keys (FI) Purpose + End-to-End Process 🔹 1. What are Movement Types in SAP? Movement Type = Inventory transaction type It tells SAP: What stock is moving From where → to where And what accounting impact should happen 🔸 Common Movement Types (Must Know) 🟢 Goods Receipt (GR) 101 → GR for Purchase Order 103 → GR blocked stock 105 → Release blocked stock 👉 Purpose: Increase stock + create accounting entry 🔴 Goods Issue (GI) 201 → Issue to Cost Center 261 → Issue to Production Order 281 → Issue for Network (PM/PS) 👉 Purpose: Consumption → reduce stock 🔵 Transfer Posting 301 → Plant to Plant 311 → Storage Location to Storage Location 321 → Quality → Unrestricted 322 → Unrestricted → Quality 👉 Purpose: Change stock location/type (no vendor/customer) 🟡 Returns / Reversals 122 → Return to Vendor 102 → Reverse GR (101 reversal) 262 → Reverse GI (261 reversal) 👉 Purpose: Cancel wrong postings 🟣 Physical Inventory 701 → Inventory Gain 702 → Inventory Loss 👉 Purpose: Adjust stock differences 🔹 2. What are Transaction/Event Keys? These are used in FI integration (OBYC configuration). 👉 Movement Type → triggers → Transaction Key → G/L Account 🔸 Most Important Transaction Keys Transaction Key Purpose BSX Inventory Posting WRX GR/IR Clearing GBB Offset (Consumption / Expense) PRD Price Difference KON Purchase Account (Conditions) FRE Freight Clearing BSV Posting for Negative Stock UMB Stock Transfer Posting VAX Goods Issue for Sales (COGS) 🔹 3. End-to-End Process (MM → FI Integration) Let’s take a real scenario 👇 🧾 Scenario: Purchase Order → Goods Receipt → Invoice Step 1: Goods Receipt (Movement Type 101) 👉 Stock increases Accounting Entry: Inventory A/c (BSX) → Dr GR/IR A/c (WRX) → Cr Step 2: Invoice Receipt (MIRO) 👉 Liability created Accounting Entry: GR/IR A/c (WRX) → Dr Vendor A/c → Cr Step 3: Payment Vendor A/c → Dr Bank A/c → Cr 🔸 Scenario: Goods Issue to Production (261) Accounting Entry: Consumption A/c (GBB) → Dr Inventory A/c (BSX) → Cr 🔸 Scenario: Price Difference If invoice ≠ PO price: PRD (Price Difference) triggered 🔹 4. How Movement Type Links to Transaction Key Flow: Movement Type → Account Modifier → Transaction Key → G/L Account Example: Movement Type 261 Uses GBB with modifier VBR Posts to Consumption G/L 🔹 5. Interview-Level Points 🔥 ✔ Movement type controls: Stock type (UR/QI/Blocked) Quantity update Value update Account