AI-Driven Task Scheduling

Scheduling in Kubernetes happens in various ways. Depending on the workload, you might need different algorithms like 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴. Volcano, a CNCF project, supports this and can optimize complex workflows such as AI training, inference pipelines, and distributed data processing.  🚀 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴? Gang scheduling ensures all pods in a group ("gang") start simultaneously or none do. This prevents partial execution, which is critical for interdependent tasks like distributed training or multi-stage AI pipelines. Without it, a single delayed pod could stall an entire workflow, wasting resources. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: In distributed AI training, if three worker pods are needed, Volcano’s gang scheduler waits until all 3 are available. If even one fails to schedule, the scheduler releases reserved resources to avoid cluster deadlocks. ⚡ 𝗪𝗵𝘆 𝗩𝗼𝗹𝗰𝗮𝗻𝗼? Volcano extends Kubernetes’ default scheduler to handle batch workloads and multi-pod dependencies. It’s ideal for: → AI/ML workflows (e.g., TensorFlow/PyTorch jobs). → Big Data processing (Spark, Flink). → High-performance computing (HPC). Key features: ✅ PodGroup orchestration: Treats multiple pods as a single schedulable unit. ✅ Fair-share resource allocation: Balances cluster resources across teams. ✅ Preemption/Reclaim: Prioritizes critical workloads without manual intervention. 🌟 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Imagine training a large language model (LLM) across 3 GPUs. With gang scheduling: → Volcano groups all worker pods into a PodGroup. → The scheduler reserves resources only when all 3 GPUs are available. → If a node fails, Volcano retries or releases resources instantly, avoiding idle clusters. This eliminates "resource hoarding" and ensures cost-efficient scaling for AI teams. #Kubernetes #mlops

I spend a huge part of my week just managing my calendar — finding free slots, rescheduling meetings, dealing with recurring events, and juggling multiple time zones. It’s tedious and eats into real work. That’s why I decided to build my own solution: a Google Calendar AI agent powered by Google’s Agent Development Kit. This agent can: 👉 Understand plain English commands like “Schedule a 1-hour call with Alex next Tuesday morning”. 👉 Suggest free time slots based on my existing calendar. 👉 Handle recurring events, cancellations, and attendees automatically. 👉 Work across time zones without any manual conversion. While building this, I learned something crucial: AI isn’t just about generating text — it can actually perform actions that solve real problems. Designing this agent taught me how to bridge natural language understanding with real-world API actions. I wrote a detailed step-by-step blog, including code snippets and logic, so anyone can replicate this setup or build their own AI productivity assistant: https://lnkd.in/dsDhtcMr #AIAgents #AgentDevelopmentKit Google Cloud #GoogleAI #GoogleCalendar #CalendarManagement #AgenticAI

I’ve heard hundreds of time management tips over the years, but 90% of them aren’t practical for daily use. Why? Because they’re:   🚫 too theoretical 🚫 too abstract 🚫 too rigid These 5️⃣ are the ones I actually use every day—plus how to boost each one with AI (and the exact prompts I use). 👇 1️⃣ Prioritize ruthlessly Not every task deserves your time. Ask: If I only do one thing today, what will matter most? 🤖 AI prompt: “Here’s my to-do list: [paste list]. Please organize these using the Eisenhower Matrix—urgent vs. important—and suggest which I should do, delegate, defer, or delete.” 2️⃣ Use AI on the $10 task so you can focus on the $10K task If it’s low-impact or repetitive, delegate it to AI. Free yourself up for meaningful work. 🤖 AI prompt: “Here’s a list of my current tasks: [paste list]. For each one, tell me if it’s a $10 task or a $10,000 task. Recommend which I should delegate to AI and which I should prioritize myself.” 3️⃣ Eat the frog Tackle your hardest or highest-impact task first—before distractions set in. 🤖 AI prompt: “Here’s my calendar and to-do list for the week: [paste or describe]. Identify which tasks are most critical and when I’m best positioned (energy-wise or schedule-wise) to tackle them first thing in the day.” 4️⃣ Time-block more than meetings Protect chunks of time for deep, focused work—not just calls. 🤖 AI prompt: “Here’s my weekly calendar: [paste or describe]. Help me find 3 time blocks for deep work. Optimize my schedule to reduce context switching and maximize focus.” 5️⃣ Every ‘yes’ to something trivial is a ‘no’ to something meaningful Practice saying “no” with intention—your time is your most valuable asset. 🤖 AI prompt: “Act as my personal scheduler and productivity coach. I’ll list recent tasks, meetings, or requests. For each one, ask: Does this align with my priorities? What am I giving up by saying yes? Is this the best use of my time? Then recommend whether I should accept, delegate, delay, or decline—and how to respond.” ✨ Real game-changer: I don’t treat AI as a shortcut—I use it as a force multiplier. What’s your go-to time management tip? Drop it below 👇

𝗦𝗺𝗮𝗹𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀—𝗮𝗻𝗱 𝗭𝗼𝗼𝗺 𝗷𝘂𝘀𝘁 𝘀𝗵𝗼𝘄𝗲𝗱 𝘂𝘀 𝗵𝗼𝘄. Zoom recently introduced a 𝗻𝗲𝘄 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗺𝗼𝗱𝗲l: a system of 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝗴𝗲𝗻𝘁𝘀 orchestrated to work together, rather than relying on a single, monolithic chatbot. The shift is subtle but powerful: instead of waiting for user prompts, these agents 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗲𝘅𝗲𝗰𝘂𝘁𝗲 𝘁𝗮𝘀𝗸𝘀—scheduling meetings, managing follow-ups, and even coordinating across calendars and email systems. One agent identifies a user's intent, another pulls in relevant contacts, a third finds open time slots, and so on—each powered by a Small Language Model (SLM) tuned for its specific task. Why are SLMs the real unlock? • SLMs (10M–3B parameters) are 𝟰–𝟭𝟬𝘅 𝗰𝗵𝗲𝗮𝗽𝗲𝗿 to run than LLMs • They’re faster, with 𝗹𝗼𝘄𝗲𝗿 𝗹𝗮𝘁𝗲𝗻𝗰𝘆—ideal for real-time agent workflows • They’re 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗮𝗯𝗹𝗲, enabling better accuracy on task-specific domains • They can run 𝗹𝗼𝗰𝗮𝗹𝗹𝘆 𝗼𝗿 𝗼𝗻-𝗽𝗿𝗲𝗺, solving for privacy and edge deployment This modularity enables multi-agent orchestration: rather than overloading a single model, we build systems that mirror real organizations—dividing responsibilities across agents. And Zoom isn’t alone: • 𝗔𝗱𝗲𝗽𝘁 uses multi-agent LLMs to 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗯𝗿𝗼𝘄𝘀𝗲𝗿 𝘁𝗮𝘀𝗸𝘀 autonomously • 𝗥𝗲𝘄𝗶𝗻𝗱.𝗮𝗶 runs an on-device agent that proactively surfaces relevant personal context • 𝗗𝘂𝘀𝘁 helps enterprises deploy custom agents inside Slack and Notion  • 𝗠𝗶𝘀𝘁𝗿𝗮𝗹 𝟳𝗕 and 𝗣𝗵𝗶-𝟮 are powering local agents in mobile and embedded applications The future isn't just smarter models—it's smaller ones, deployed strategically. What’s one high-frequency task in your product that a dedicated agent could own end-to-end?

The daily production meeting is one of manufacturing's most valuable rituals. It's also frequently one of our most expensive misallocations of management time...it's also about to drastically change. The gold standard: A short, focused conversation that allocates resources and sets priorities to win today In most cases, it's a debrief on yesterday. What did we make? How much did we miss the number by? What were our top losses? What actions are open? Let's review the action log... Let me be provocative: I don't care what you made yesterday. That product has been made. It's in the warehouse or on a truck. The work is done What I care about is what you're going to do today to hit your business objectives (+how the events of yesterday shape decisions today) Leaders are using real-time dashboards to make this conversation more targeted. But it takes real discipline to let go of the past and focus on the future How does — and how could — AI change this picture? Near term: AI can do something most teams struggle with manually. It will rank today's priorities by business impact. Not by who's loudest in the room. Not by what broke most recently. By the actions that move your numbers. The "next best action" across your full metric set, with the trade-offs made visible IDC predicts over 40% of manufacturers will have AI-driven autonomous scheduling in place in 2026 - THIS YEAR! The technology to run this meeting differently already exists. That alone changes the meeting Longer term: As our processes become increasingly automated, this meeting starts to look fundamentally different In a world where AI is calling real-time shots on scheduling, maintenance, quality interventions — the 8am meeting doesn't review outcomes. It governs decisions McKinsey frames it this way: Organizations will need to manage AI agents the way they manage people — with performance reviews, accountability, and the ability to retrain or retire underperformers The focuses shifts: - You're not discussing what broke...the maintenance order is raised, the part is ordered, the time is scheduled - You're not discussing what you'll make....the schedule is updated in minutes, materials ordered, resources assigned What the meeting will become is a review of the decisions your AI routines made in the last day. A challenge of the logic behind anything sub-optimal, and an action plan to improve the data and reasoning that drives action today In other-words: Your Daily Production Meeting will be the frontline of your AI Governance strategy Very different skills. Different questions. Different leaders at the front of the room The daily cadence isn't going away. Its center of gravity will shift — from forensics to orchestration BCG research suggests only 14% of frontline workers have received any AI upskilling. So when I ask — are your teams ready for that meeting? — I think we both know the honest answer #Manufacturing #AI #OperationalExcellence #DigitalTransformation

I asked my team a simple question last week: "What's still eating up your time every day?" The room got quiet. Then Sarah, one of our top AEs, spoke up. "Meeting prep. I spend 20-30 minutes before every call just trying to figure out who I'm talking to, what their company does, and what questions I should ask. Yes, we have a few tools that give me some fluff about the people I am talking to, but I still need context specific to us, our customer, and how I can add value when I step into the meeting...." She pulled up her screen and walked me through her process: - Check LinkedIn profiles for each attendee Research the company website - Look up recent news or funding Scan their tech stack for competitors - Draft discovery questions Block time for follow-up tasks "This is for ONE meeting," she said. "I have six today." Five minutes into her walkthrough, I stopped her. "Five minutes is five minutes too long. We're fixing this today." That afternoon, I built what my team now calls "the prompt to rule them all." Here's what our Daily Sales Agenda AI agent does automatically every morning: 1. Scans each rep's calendar for the day 2. Researches every non-company attendee 3. Pulls prospect insights and company context 4. Maps strategic connections to our solution 5. Generates tailored discovery questions for each meeting 6. Flags if competitors appear in their tech stack 7. Recommends optimal time blocks for deal management Schedules post-meeting follow-up windows 8. Creates a daily deal hygiene checklist 9. Suggests new prospect research windows The agent delivers this as a personalized briefing document before their first coffee. Sarah tested it the next day. Her reaction? "I feel like I have a research team working for me overnight." But I didn't stop there. Version 2.0 is already in development. It will pull data from Salesforce, analyze recent Gong calls, cross-reference email engagement, and even suggest which deals need attention based on last activity. But here's the real insight: This isn't about the tool I built. It's about changing how we think about sales operations. For years, we've accepted that "good sales reps do their homework." We've normalized 2-3 hours of daily admin work as "part of the job." That's insane. Your reps shouldn't be spending 30% of their day on tasks a computer can do in 30 seconds. The old growth equation was: more people = more revenue. The new equation: remove friction = sales superheroes. Every minute your team spends on manual research, data entry, or administrative tasks is a minute they're not solving problems for prospects. We don't need to buy every shiny new sales tool. We can build targeted solutions for our specific workflows. The question isn't "Can we afford to invest in automation?" The question is "Can we afford NOT to?"

What happens when you aim industrial AI at production scheduling but treat it like every other engineering problem? We built a multi-agent AI system that achieved a 21% increase in profit. Here’s how: 1. Make the goals explicit Production scheduling is a complex process with numerous trade-offs. Highest demand or most efficient run? Overtime or on-time delivery? We spelled out the real goals and KPIs so the agent system knew exactly which knot it had to untangle. 2. Capture expertise through machine teaching Machine teaching breaks the job into bite-size skills. An engineer shows the system why a decision works, not just what happened in the data. Rather than rely purely on data, machine teaching transfers deep human expertise into the system - digitizing decades of experience and knowledge, crucial as expert operators retire. 3. Structuring the Multi-Agent System The multi-agent system was designed to mimic human decision-making: Sensors: Gather real-time data on production status, resources, and external market conditions. Skills: Modular units responsible for specific actions, such as forecasting demand, optimizing scheduling, or adapting to sudden changes. Each skill can evolve on its own, giving the plant the same modular flexibility you expect from any well-engineered system. 4. Establishing a Performance Benchmark Good engineering demands clear benchmarks. We ran a standard optimization-based system as our baseline. This allowed us to objectively measure whether our AI agents delivered measurable improvements. 5. Rigorous Testing & Iteration Engineering thrives on iteration. We created and tested 13 agent system designs, continuously iterating based on performance data. Each iteration leveraged insights from the previous, systematically improving performance until we identified the optimal solution. --- By treating AI as an engineered system (modular, explainable, and configurable) it demonstrates significant potential results: ✅ 21% higher profit margins ✅ Improved adaptability to rapidly changing market conditions ✅ Preservation and amplification of valuable human expertise Full breakdown of the build and tests is below.👇 #ProductionScheduling #IndustrialAI #MachineTeaching #SmartManufacturing

Imagine you're a legal or compliance team, looking to use AI to help keep you abreast of recent legal developments and to horizon scan upcoming developments in data protection and AI — how can you do this? 🤖⚖️ Subject to all the usual warnings (beware of hallucinations, check outputs for accuracy etc.), those of you who have access to any of the mainstream LLMs (ChatGPT, Claude, Gemini, Copilot etc.) can do this easily as follows: 1. First, create a horizon scanning prompt that you can ask the AI to run. The challenge is how to create a good prompt if you're not an expert in prompt engineering. ✍️ 2. The answer is to have AI create it for you! For example, I asked ChatGPT: "Can you draft a prompt for an AI agent that will run on a weekly basis and endeavour to find: (i) major developments in EU/UK data protection, data regulation and AI laws and regulatory guidance (at both an UK and EU-wide and EU member state level), and (ii) do horizon scanning for any new or emerging issues relevant to these areas? The aim is to gather information that will be relevant to and keep up to date a legal team specialising in these areas in a leading law firm." 💡 3. ChatGPT understood the brief, and created an extremely long, fleshed-out prompt that you can see below. If you're looking for a short cut, you can cut and paste this into the LLM you use (and even ask your own LLM if it would suggest any improvements). 📋 4. If you then run this in your chosen LLM, it will provide you with an initial horizon scanning report. 🔍 5. LLMs like ChatGPT, Gemini and (I think) Copilot offer task scheduling functionality to business (i.e. paid) users — meaning you can give them this prompt and require them to execute it at predetermined intervals (e.g. once a week). For example, you can ask them to schedule these prompts to run at weekly intervals by saying: "Can you create a schedule to run the previous prompt once a week?" This then provides you with regular horizon scanning reports. 📅 6. If your chosen LLM doesn't have task scheduling capability, then you'll need to run this prompt manually at your chosen intervals. But that's what calendars and reminders are for! 🔔 The prompt created below has in-built source prioritisation, search strategies, and quality controls — but, like any AI, they won't be foolproof. Simply telling an AI not to hallucinate doesn't mean that it won't! So keep that in mind. However, if you're looking for a tool that can help make you aware of developing issues so you can check them out and validate them for yourself (you human-in-the-loop, you), then this can be a great place to start. 🚀

Redefining Productivity: AI Agents as Autonomous Team Members We are entering a new phase of digital productivity — and it’s not just about automation anymore. AI agents are evolving from tools into semi-autonomous collaborators capable of executing multi-step workflows, making context-aware decisions, and interfacing across systems. 🧠 What Is an AI Agent? Unlike basic AI assistants that respond to single prompts, AI agents are built to: Interpret a goal or objective Break it into actionable subtasks Execute those tasks autonomously across apps, APIs, and systems Adapt based on real-time inputs and outcomes Think of them as project interns — except they don’t sleep, forget, or burn out. 🧪 Case Study: I Gave an AI Agent 2 Business Goals Recently, I assigned an AI agent the following tasks: Analyze top competitors in our space → The agent pulled financials, summarized public data, and flagged emerging differentiators. Draft a weekly planning calendar → Based on my goals and upcoming meetings, it proposed a time-blocked schedule with task batching. Result? ✅ Saved ~7 hours of manual work ✅ Increased consistency in execution ✅ Identified insights I hadn’t considered 🎯 Why This Matters for Professionals AI agents aren’t here to replace strategic thinking — they free you to do more of it. They help with: Multi-step planning (not just single-task help) Workflow orchestration across tools like Notion, Slack, Calendly, HubSpot, etc. Acting as “Chief of Staff” for solopreneurs, execs, and project teams The Future of Work Is Not Solo — It’s Symbiotic To stay competitive, professionals must shift from: ❌ “How do I do this task?” ✅ To “How do I assign this task to AI and review the outcome?” 💬 Are you experimenting with autonomous agents yet? 📩 For deeper insights like this, subscribe to LinkedIn Today by Emma Shad https://lnkd.in/gCV3_Raw

The next leap in AI isn’t smarter chat. It’s AI that can finally do the work for you. That’s what “function calling” unlocks and it’s quietly transforming chatbots into true AI agents that plan, act, and deliver results.. Here’s why this matters (without the jargon): 1️⃣ One brain, unlimited reach AI can now plug directly into your CRM, calendar, databases, and APIs - not just give you text replies. 2️⃣ Decisions in real time You ask for an outcome, and the agent figures out the how. Choosing tools, sequencing steps, running actions instantly. 3️⃣ Parallel execution = speed Imagine 10 assistants working at once: leads enriched, emails drafted, reports generated Done in minutes, not hours. 4️⃣ Answers you can use, not just read The agent doesn’t hand you data, it delivers a ready-to-go solution. 5️⃣ It adapts under pressure Errors don’t stop it. It tries again, switches tools, finds another way forward. 💡Example: “Pull last week’s webinar attendees, find all CFOs, draft 2 tailored follow-ups for each, schedule them for tomorrow morning.” Old AI: “Here’s a list of prospects.”   Function-calling agent: Finds them → enriches data → writes emails → schedules sends → confirms it’s ready. With Model Context Protocol (MCP), this gets even more powerful: ✔ Any system can be plugged in within minutes ✔ Built-in security and local execution for speed ✔ No custom code needed We’re stepping into a world where you won’t just ask AI for ideas. You’ll give it instructions and watch it deliver results. 👉 If you could hand off one recurring task to an AI agent tomorrow, what would it be?