User Task Analysis Techniques

One of the first Instructional Design projects I worked on still sticks with me. The client handed me eight separate slide decks and said: “Can you turn all of this into one training?” 😑 I remember opening the files: hundreds of slides, all important in their own way, but something didn’t sit right. So, I asked a question that’s become my go-to ever since: “What’s the actual moment in the job where someone gets stuck, messes up, or hesitates, and needs this?” That question changed everything. Instead of cramming it all into a mega-course, we: ✅ Cut 70% of the content ✅ Turned the rest into two scenario-based simulations ✅ Built a one-page job aid that’s still in use today And the best part? People applied it, and didn't complain about taking it. The motivation to complete the course increased. The behavior changed. The feedback improved. I learned early on that we’re not here to cover content. We’re here to solve real work problems. So now, every time I hear, “Can you just turn this into a course?” I slow it down and ask: “What do people need to do, and where do they get stuck?” If you’ve been in that spot, I’d love to hear how you handled it👇. It can be a tough discussion, and pushing back respectfully can be daunting sometimes. #InstructionalDesign #LearningAndDevelopment #LXD #CorporateTraining #RealWorldLearning #JobRelevance #EarlyCareerLessons

Treating AI like a chatbot, AKA you ask a question → it gives an answer is only scraching the surface. Underneath, modern AI agents are running continuous feedback loops - constantly perceiving, reasoning, acting, and learning to get smarter with every cycle. Here’s a simple way to visualize what’s really happening 👇 1. Perception Loop – The agent collects data from its environment, filters noise, and builds real-time situational awareness. 2. Reasoning Loop – It processes context, forms logical hypotheses, and decides what needs to be done. 3. Action Loop – It executes those plans using tools, APIs, or other agents, then validates outcomes. 4. Reflection Loop – After every action, it reviews what worked (and what didn’t) to improve future reasoning. 5. Learning Loop – This is where it gets powerful, the model retrains itself based on new knowledge, feedback, and data patterns. 6. Feedback Loop – It uses human and system feedback to refine outputs and improve alignment with goals. 7. Memory Loop – Stores and retrieves both short-term and long-term context to maintain continuity. 8. Collaboration Loop – Multiple agents coordinate, negotiate, and execute tasks together, almost like a digital team. These loops are what make AI agents more human-like while reasoning and self-improveming. Leveraging these loops moves AI systems from “prompt and reply” to “observe, reason, act, reflect, and learn.” #AIAgents

Parallel agents are emerging as an important new direction for scaling up AI. AI capabilities have scaled with more training data, training-time compute, and test-time compute. Having multiple agents run in parallel is growing as a technique to further scale and improve performance. We know from work at Baidu by my former team, and later OpenAI, that AI models’ performance scales predictably with the amount of data and training computation. Performance rises further with test-time compute such as in agentic workflows and in reasoning models that think, reflect, and iterate on an answer. But these methods take longer to produce output. Agents working in parallel offer another path to improve results, without making users wait. Reasoning models generate tokens sequentially and can take a long time to run. Similarly, most agentic workflows are initially implemented in a sequential way. But as LLM prices per token continue to fall — thus making these techniques practical — and product teams want to deliver results to users faster, more and more agentic workflows are being parallelized. Some examples: - Many research agents now fetch multiple web pages and examine their texts in parallel to try to synthesize deeply thoughtful research reports more quickly. - Some agentic coding frameworks allow users to orchestrate many agents working simultaneously on different parts of a code base. Our short course on Claude Code shows how to do this using git worktrees. - A rapidly growing design pattern for agentic workflows is to have a compute-heavy agent work for minutes or longer to accomplish a task, while another agent monitors the first and gives brief updates to the user to keep them informed. From here, it’s a short hop to parallel agents that work in the background while the UI agent keeps users informed and perhaps also routes asynchronous user feedback to the other agents. It is difficult for a human manager to take a complex task (like building a complex software application) and break it down into smaller tasks for human engineers to work on in parallel; scaling to huge numbers of engineers is especially challenging. Similarly, it is also challenging to decompose tasks for parallel agents to carry out. But the falling cost of LLM inference makes it worthwhile to use a lot more tokens, and using them in parallel allows this to be done without significantly increasing the user’s waiting time. I am also encouraged by the growing body of research on parallel agents. For example, I enjoyed reading “CodeMonkeys: Scaling Test-Time Compute for Software Engineering” by Ryan Ehrlich and others, which shows how parallel code generation helps you to explore the solution space. The mixture-of-agents architecture by Junlin Wang is a surprisingly simple way to organize parallel agents. [Truncated for length. Full text, with links: https://lnkd.in/gQ98HMci ]

🧠 How to Build AI Agents the Right Way A Holistic Lifecycle Approach: From Requirements to Responsible Operations 1️⃣ Define Purpose & Requirements - Problem Framing: What real-world task will the agent solve? - Stakeholder Mapping: Who are the users? What are their expectations? - Success Metrics: Define efficiency, accuracy, cost, and sustainability targets. 2️⃣ Design Agentic Blueprint - Roles & Goals: Define each agent’s specialization, responsibilities, and autonomy level. - Decomposition Strategy: Break down the task into subtasks mapped to agents. - Interaction Model: Self, collaborative, or autonomous workflows. 3️⃣ Choose the Right Models & Tools - LLM Selection: Pick SLMs or LLMs based on task, cost, and emission profile. - Toolchain Design: APIs, webhooks, data access tools, planning libraries. - Agent Orchestration Framework: CrewAI, LangGraph, ADK, Autogen, or custom. 4️⃣ Enable Contextual Memory - Episodic Memory: Track short-term interactions and loops. - Long-Term Memory: Use vector DBs, SQL/NoSQL for history. - Shared State: Enable inter-agent memory and cross-task coordination. 5️⃣ Incorporate Reasoning & Planning - Reflection Loops: Evaluate and refine actions mid-task. - Planning Depth Control: Avoid hallucinations and inefficiencies. - Prompt Engineering: Optimize for compression, clarity, and chain-of-thought. 6️⃣ Validate & Simulate Behavior - Scenario Testing: Use synthetic and real-world test cases. - Edge Case Simulation: Identify failure paths, looping, and over-execution. - Agentic Evaluations: Use auto-evals for robustness, explainability, and efficiency. 7️⃣ Optimize for Cost, Carbon, and Complexity - Model Routing: Dynamically select models based on input. - Token Efficiency: Compress prompts, prune outputs. - Green Execution: Schedule in low-carbon zones, use idle-aware agents. 8️⃣ Deploy in Controlled Environments - Secure Interfaces: REST, MCP, or stream-based calls with scoped access. - Version Control & Rollbacks: For agents, tools, and workflows. - Fallback Models: Define what happens when something fails. 9️⃣ Continuous Monitoring & Feedback - Telemetry Collection: Latency, model cost, emissions, task success rate. - Behavioral Logging: Track decision paths and agent communication. - Drift Detection: Trigger retraining or prompt updates as needed. 🔟 Governance, Risk & Compliance - Auditability: Log decisions, tool usage, model selections. - Privacy Controls: Mask PII, restrict memory scope. - Sustainability Standards: Integrate SCI for AI, emission budgets, and green compliance. Building AI agents isn’t about chaining tools — it’s about designing a living system that thinks, adapts, collaborates, and respects boundaries of compute, cost, and conscience. #agenticai #lifecycle

🔍 𝗠𝗼𝘀𝘁 𝗠𝗜𝗦 𝗿𝗲𝗽𝗼𝗿𝘁𝘀 𝗮𝗿𝗲 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 '𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝘀 𝗨𝘀𝘂𝗮𝗹'... 𝗯𝘂𝘁 𝘄𝗵𝗮𝘁 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗻 𝗶𝘁'𝘀 𝗻𝗼𝘁? In times of volatility—like sudden demand drops, cost spikes, or supplier disruptions—traditional reporting systems often fall short. 𝗪𝗵𝘆? 𝘉𝘦𝘤𝘢𝘶𝘴𝘦 𝘵𝘩𝘦𝘺’𝘳𝘦 𝘥𝘦𝘴𝘪𝘨𝘯𝘦𝘥 𝘧𝘰𝘳 𝘳𝘰𝘶𝘵𝘪𝘯𝘦 𝘮𝘰𝘯𝘪𝘵𝘰𝘳𝘪𝘯𝘨, 𝘯𝘰𝘵 𝘳𝘢𝘱𝘪𝘥 𝘳𝘦𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨. This is where 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼-𝗯𝗮𝘀𝗲𝗱 𝗠𝗜𝗦 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀 𝗰𝗮𝗻 𝗯𝗲 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿𝘀. 🧠 During a high-pressure moment in my past work, we didn’t have the luxury of waiting for weekly dashboards. We 𝗯𝘂𝗶𝗹𝘁 𝗮 𝗿𝗮𝗽𝗶𝗱-𝘁𝘂𝗿𝗻𝗮𝗿𝗼𝘂𝗻𝗱 𝗘𝘅𝗰𝗲𝗹-𝗯𝗮𝘀𝗲𝗱 𝗠𝗜𝗦 tailored to 𝘵𝘳𝘢𝘤𝘬 3 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘸𝘩𝘢𝘵-𝘪𝘧 𝘥𝘦𝘮𝘢𝘯𝘥 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰𝘴 per outlet, per day. That agility allowed leadership to adjust procurement, staffing, and marketing—almost in real time. In such moments, your ability to 𝗽𝗶𝘃𝗼𝘁 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 = 𝗽𝗶𝘃𝗼𝘁 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀. 💡 MIS should not only tell you what is happening—it should help you plan for 𝘸𝘩𝘢𝘵 𝘤𝘰𝘶𝘭𝘥 𝘩𝘢𝘱𝘱𝘦𝘯. 🔄 𝙒𝙝𝙖𝙩’𝙨 𝙩𝙝𝙚 𝙛𝙖𝙨𝙩𝙚𝙨𝙩 𝙧𝙚𝙥𝙤𝙧𝙩𝙞𝙣𝙜 𝙥𝙞𝙫𝙤𝙩 𝙮𝙤𝙪’𝙫𝙚 𝙚𝙫𝙚𝙧 𝙝𝙖𝙙 𝙩𝙤 𝙢𝙖𝙠𝙚—𝙖𝙣𝙙 𝙬𝙝𝙖𝙩 𝙙𝙞𝙙 𝙞𝙩 𝙩𝙚𝙖𝙘𝙝 𝙮𝙤𝙪? 👇 Drop your story. Let's learn from each other. #MISReporting #DataDrivenDecisionMaking #ScenarioPlanning #BusinessContinuity #ExcelForBusiness

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

Skill Development is Broken. Here's How to Fix It. A client once ran a leadership training for a mid-sized manufacturing company. The participants were engaged, took notes, and seemed enthusiastic. One month later? Less than 10% remembered or applied what they learned. This isn’t their fault. It’s a system problem. 📌 Here’s what doesn’t work: ❌ A one-day workshop with no reinforcement. ❌ Generic training that doesn’t match real-world challenges. ❌ Passive content with no accountability for application. 🚀 So when they came to us, here is what we did instead: ✔️ Shifted from one-time sessions to learning nudges delivered over months. ✔️ Created scenario-based microlearning, where managers had to solve real workplace challenges. ✔️ Integrated peer coaching, so learning became part of their daily routine. 🔥 The result? ✅ 15% of managers applied new skills within 6 months. ✅ Teams reported faster conflict resolution and better decision-making. ✅ The company saved thousands in lost productivity from ineffective leadership. 💡 What’s the best (or worst) training experience you’ve had? Drop a comment! 👇

Stop Chasing AI Hype. Your Best Agentic AI Use Case Is Hiding in Your Biggest Bottleneck If you want to know where AI agents can create a 10x impact, don't look at the latest tech demos. Look for the places your teams can’t catch up — no matter how hard they work. I call this the "Bottleneck Test," a simple 3-step framework to find your best AI use cases. Step 1: IDENTIFY the Chronic Bottleneck Ask: "Where does the work never end?" At one of our clients, this was the engineering team's code review process. They were perpetually behind, not because they were bad at their jobs, but because they were outnumbered by the sheer volume of pull requests. The bottleneck was structural. This isn't just a tech problem. It happens everywhere: • Legal teams buried in standard contract reviews. • Finance departments manually reconciling thousands of invoices. • Marketing teams trying to qualify an endless flood of inbound leads. Step 2: QUALIFY the Use Case The best candidates for an AI agent are tasks that are repetitive, rules-based, and have clear success metrics. For our client, code review was perfect. It required checking against internal standards, security policies, and documentation—all data an AI agent could be trained on. Step 3: PILOT the Agent Our client introduced an AI code review agent as a pilot. It didn’t replace engineers. It augmented them. The agent handled the routine work—flagging common errors, checking for compliance, and summarizing changes—freeing up senior engineers to focus on complex architectural issues. The results were transformative: • Cycle times dropped by 40%. • Code quality and security posture improved. • Engineers could finally focus on meaningful work. Your roadmap for Agentic AI shouldn't be a list of technologies to try. It should be a list of your most critical business bottlenecks to solve. What is the biggest "work never ends" bottleneck in your organization? Share in the comments—let's discuss which ones are prime candidates for an AI agent. Zinnov Dipanwita Ghosh Namita Adavi ieswariya k Arpit Bhatia Amita Goyal Karthik Padmanabhan Mohammed Faraz Khan Komal Shah Ashveen Pai Hani Mukhey Anandhu Ajith Vyas Vandna Lal

Why you should look for Spark UI when you are struggling with performance issues in your Spark Structured Streaming applications? 🤔 𝗙𝗶𝗿𝘀𝘁 𝗼𝗳 𝗮𝗹𝗹, 𝗪𝗵𝘆 𝗦𝗽𝗮𝗿𝗸 𝗨𝗜? ================== -> Spark UI is your window into the internals of Spark application. -> It provides real-time insights into your job's performance, resource utilization, and potential bottlenecks. ->For streaming applications, the Streaming tab is your go-to resource. 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗼 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 ----------------------- 𝟭. 𝗜𝗻𝗽𝘂𝘁 𝗥𝗮𝘁𝗲 𝘃𝘀. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗥𝗮𝘁𝗲   - Input Rate: How fast data is coming in   - Processing Rate: How fast your job is processing data   - 🚨 Alert: If Processing Rate < Input Rate, you're falling behind! 𝟮. 𝗕𝗮𝘁𝗰𝗵 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗧𝗶𝗺𝗲   - Shows how long each micro-batch takes to process   - 📈 Trend Analysis: Look for increasing trends over time 𝟯. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝗗𝗲𝗹𝗮𝘆   - Time between batch creation and the start of processing   - 🐢 High delay = Your system is overwhelmed 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗧𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁𝗶𝗻𝗴 ------------------------ 1. Use the "min/max/avg" toggle   - Helps identify outliers in batch processing times 2. Check the DAG visualization   - Understand your job's logical and physical plans   - Spot bottlenecks in specific stages 3. Monitor Watermark Progress   - Ensure your watermark is advancing as expected   - Stalled watermark = potential state store bloat 4. Analyze Task Metrics   - Look for data skew in shuffle read/write sizes   - High GC time might indicate memory pressure 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: ---------- 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗸𝗲𝘄 𝗶𝗻 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 👉 Scenario:  ↳ Your spark click-stream analysis job is running slower than expected. 👉 Spark UI Action:  ↳ Check the "Executors" tab to see if some executors are processing significantly more data than others. 👉 Solution:  ↳ If skew is detected, implement salting techniques or adjust partitioning strategies to distribute data more evenly. #pyspark #apachespark #dataengineers #dataengineering

4 strategies for multi-GPU training explained visually. By default, deep learning models only utilize a single GPU for training, even if multiple GPUs are available. An ideal way to proceed (especially in big-data settings) is to distribute the training workload across multiple GPUs. The graphic below depicts four common strategies for multi-GPU training: 1) Model parallelism - Different parts (or layers) of the model are placed on different GPUs. - Useful for huge models that do not fit on a single GPU. - However, model parallelism also introduces severe bottlenecks as it requires data flow between GPUs when activations from one GPU are transferred to another GPU. 2) Tensor parallelism - Distributes and processes individual tensor operations across multiple devices or processors. - It is based on the idea that a large tensor operation, such as matrix multiplication, can be divided into smaller tensor operations, and each smaller operation can be executed on a separate device or processor. - Such parallelization strategies are inherently built into standard implementations of PyTorch and other deep learning frameworks, but they become much more pronounced in a distributed setting. 3) Data parallelism - Replicate the model across all GPUs. - Divide the available data into smaller batches, and each batch is processed by a separate GPU. - The updates (or gradients) from each GPU are then aggregated and used to update the model parameters on every GPU. 4) Pipeline parallelism - This is often considered a combination of data parallelism and model parallelism. - So the issue with standard model parallelism is that 1st GPU remains idle when data is being propagated through layers available in 2nd GPU: - Pipeline parallelism addresses this by loading the next micro-batch of data once the 1st GPU has finished the computations on the 1st micro-batch and transferred activations to layers available in the 2nd GPU. - The process looks like this: ↳ 1st micro-batch passes through the layers on 1st GPU. ↳ 2nd GPU receives activations on 1st micro-batch from 1st GPU. ↳ While the 2nd GPU passes the data through the layers, another micro-batch is loaded on the 1st GPU. ↳ And the process continues. - GPU utilization drastically improves this way. This is evident from the animation below where multi-GPUs are being utilized at the same timestamp (look at t=1, t=2, t=5, and t=6). -- If you want to learn AI/ML engineering, I have put together a free PDF (530+ pages) with 150+ core DS/ML lessons. Get here: https://lnkd.in/gi6xKmDc -- 👉 Over to you: What are some other strategies for multi-GPU training?