Building Adaptive Workflows

agents that learn your workflows > agents that relearn you every day. I’m sharing a standout research report: Log2Plan, an adaptive GUI automation framework powered by task mining. It learns from real interaction logs, builds a reusable plan, and then adapts each step to the live screen. Think: global plan + local grounding, so agents get more reliable the longer you use them. ↳ Why this matters for UX/UI: ➤ Personalization without hero prompts, the system internalizes how you work (file paths, naming, exception paths). ➤ Recoverable runs, step-level checks and quick human-assist beats brittle macro replays. ➤ Transparent actions, structured plans you can read, audit, and improve. ➤ Resilience to UI drift, intent stays stable even when buttons and layouts move. ↳ What’s actually new here: ➤ Task mining turns messy click/keystroke logs into reusable “Task Groups” (ENV / ACT / Title / Description). ➤ Retrieval-augmented planning pulls the right pieces for a new goal, then the local planner fits them to the current screen. ➤ A clear separation of plan vs. interaction that reduces token bloat and flaky screenshot reasoning. ↳ Try this week (operator’s cut): ➤ Pick one high-volume desktop flow (e.g., monthly report collation). ➤ Curate 2–3 clean traces into “Task Groups.” ➤ Define success metrics (success rate, sub-task completion, time per task, assist rate). ➤ Add human-assist checkpoints for sensitive steps and ship a small pilot. Follow for more UX/UI & AI implementations. Re-share with your network.

Most AI projects fail for a very boring reason. They are designed like IT projects. MIT research found that out of 300+ enterprise AI pilots, only 5% created measurable business value. That statistic has barely improved in years. The problem is not the models or the data. It is the framing. Leaders keep asking: “Where can I apply AI in my existing processes?” The better question is: “What would this process look like if built today with AI at the center?” Jon Cooke's recent post on Agent APE highlights the same shift I have spoken about: stop trying to fit AI into workflows that were never designed to adapt. Start engineering AI-native processes that can learn, evolve, and improve in production. How do you actually make that happen? A few principles matter: 1. Start with outcomes, not outputs Most initiatives target outputs (more invoices processed, faster OCR, better NPS). The right framing is outcomes (cash flow acceleration, fraud detection, lifetime customer value). Outcomes drive re-engineering, not patchwork. 2. Engineer, do not design Traditional BPM meant mapping “as-is” and “to-be” diagrams. But design assumes you know the end state. With AI, the end state is discovered. Treat it as engineering: build, test, learn, and evolve with AI as a co-builder. 3. Capture processes as living systems Static swimlanes and PowerPoints do not work anymore. Represent processes as digital twins and data products that continuously learn. This allows AI to experiment with flows and adjust dynamically without rewriting the entire operating model. 4. Replace incrementally, not all at once The Strangler Pattern works. Do not freeze the business for two years of re-platforming. Start with one slice of the process, re-engineer it with AI-native methods, and expand from there. Over time, the old shell gives way to intelligent flows. 5. Optimize for adaptability, not just accuracy Too many projects chase 99 percent accuracy in narrow tasks. The real advantage comes from adaptability, the ability for processes to evolve as regulations, customer behaviors, and market conditions change. The lesson: AI process engineering is not a project. It is an operating discipline. Executives who understand this will stop measuring success by pilots launched and start measuring it by processes re-engineered. The question to ask the leadership teams is simple: If you had to build this business process from scratch today, would you replicate the legacy version, or would you let AI show you a better one? Those who can answer honestly will lead. The rest will stay stuck in pilot purgatory.

Agentic systems are transforming the way we build intelligent applications. But building one that scales 𝘳𝘦𝘭𝘪𝘢𝘣𝘭𝘺 requires more than just chaining prompts or APIs. It demands a robust architecture — one that blends structure, adaptability, and memory. Here’s a sketch I created to summarize a complete 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁, inspired by real-world systems: Core Components 1. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) – The foundation for reasoning, communication, and synthesis. 2. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 – Creates task decomposition and selects optimal workflows.     3. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗔𝗴𝗲𝗻𝘁𝘀 – Operate in:    →𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 (agent → agent handoff)    →𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 (simultaneous agent execution with a Decision Agent) 4. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 – Ensure ethical, safe, and bounded operations (PII protection, response filtering, etc.) 5. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 – Capture and use:    Chat History    User Profile    Conversation State 6. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 – Track performance, bottlenecks, and system drift.     Frameworks That Map to This Architecture This blueprint isn't theoretical — it's actionable with the right tools: • 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 → Graph-based stateful agent flows • 𝗖𝗿𝗲𝘄𝗔𝗜 → Autonomous teams of specialized agents • 𝗔𝘂𝘁𝗼𝗴𝗲𝗻 (𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁) → Conversational agent orchestration framework • 𝗠𝗲𝘁𝗮𝗚𝗣𝗧 → Multi-agent system for software generation • 𝗔𝗗𝗞 (𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗞𝗶𝘁) → Brings modularity, plug-and-play memory, observability, and execution logic to life.    Each of these fits naturally into this architecture — some emphasize planning, others coordination or tooling — but 𝘁𝗵𝗲𝘆 𝗮𝗹𝗹 𝘀𝗵𝗮𝗿𝗲 𝗮 𝗰𝗼𝗺𝗺𝗼𝗻 𝗴𝗼𝗮𝗹: 𝗯𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝗹𝘆 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀, 𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀.

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

I just solved a workflow problem that was eating hours of my time every week - and I want to share how I did it. Like many content creators, I was manually converting my Beehive newsletter drafts into markdown for my website. Copy, paste, reformat, fix images, adjust embeds... you know the drill. It was tedious and error-prone. So I built a custom MCP (Model Context Protocol) server in Java that: • Connects directly to Beehive's API • Pulls draft content automatically • Converts HTML to my specific markdown format • Handles images, YouTube embeds, and Twitter posts • Creates files in the right directory structure The best part? I can just tell Claude: "Grab the latest draft and create the markdown file for my website" - and it handles everything. This isn't just another toy tutorial. It's a real solution to a real problem that saves me hours every week. The MCP server gives Claude the exact tools it needs to automate complex workflows that would be painful to script manually. I've even set up GitHub Actions to build native images for Mac, Windows, and Linux - so you don't need Java installed to use it. The source code is available on GitHub if you want to see how it works or build something similar for your own workflow. What manual tasks in your workflow could benefit from this kind of automation? Sometimes the best solutions come from scratching your own itch. Watch the full demo: https://lnkd.in/e-M2fMZy ##MCP #Java

Building one AI agent vs. building an agentic workflow often require different considerations and approaches. One agent: You're thinking about system instructions, evaluation metrics, failure modes. It's contained. An agentic workflow: You're building a department. Multiple agents handing off tasks, making decisions that affect downstream agents, coordinating across boundaries. The complexity doesn't scale linearly. It's almost like it explodes. When clients come to us wanting to implement agentic workflows, they're usually thinking efficiency and not readiness. But the real question is actually whether the workflow is ready for a team of agents. Sometimes, the hardest part of our job isn't building the agents. It's actually getting their workflows to the point where it's ready to be agentified. (Meaning before we can build the agentic workflow, we need to create readiness in the workflow itself). This has been a helpful way to give me clarity around workflow readiness. What are you seeing? Are teams jumping to multi-agent systems before their workflows are ready?

20+ agents save me hours daily. Most people wait for AI to "get better" or for their company to provide training. Meanwhile, they're missing the obvious stuff happening right now. Here's what I learned building workflows that actually work: 𝗦𝘁𝗮𝗿𝘁 𝘀𝘁𝘂𝗽𝗶𝗱𝗹𝘆 𝘀𝗺𝗮𝗹𝗹. After coffee chats, I'd forget key insights within days. Built a simple workflow: Slack myself notes → copies to Google Sheet. Then added AI: now it creates follow-up calendar events, extracts discussion topics for next meeting, and builds my relationship "CRM". Started basic, became essential. 𝗧𝗮𝗿𝗴𝗲𝘁 𝗵𝗶𝗴𝗵-𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝘁𝗮𝘀𝗸𝘀 𝘆𝗼𝘂 𝗮𝘃𝗼𝗶𝗱. Everyone knows storytelling matters for career growth. Nobody does it because sitting down to write stories feels overwhelming. So I text ideas to Slack → agent saves to Notion → runs analysis/feedback → repeat with followups. Now I bank stories during my commute instead of procrastinating for months. 𝗙𝗼𝗿𝗴𝗲𝘁 𝘁𝗵𝗲 𝗴𝗿𝗮𝗻𝗱 𝗔𝗜 𝘃𝗶𝘀𝗶𝗼𝗻. Skip waiting for the "universal assistant" to solve everything for you. Build for your weird, specific habits. Those patterns become product ideas when you notice what you actually use. The one-person business isn't coming – it's here. While everyone debates job displacement, smart people are becoming force multipliers. Big platforms won't solve your niche problems. You will. Stop waiting. You don't need to be an engineer or know how to code. Building for hundreds of millions of users taught me: we all started exactly where you are now. Your repetitive tasks are someone else's $100M opportunity. Start building. What would you automate first? (Happy to share my exact workflows if helpful) #AI #Automation #ProductThinking #TechLeadership #FutureOfWork

Automation, AI workflow, or AI agent? To always 𝘬𝘯𝘰𝘸 𝘸𝘩𝘪𝘤𝘩 𝘰𝘯𝘦 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥, follow this 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬: Remember when I explained why many "𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴" shared on LinkedIn are actually 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 or 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 in disguise? Turns out: understanding the difference is only partially helpful. The real challenge is knowing 𝘸𝘩𝘪𝘤𝘩 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦. So I built this framework to help you decide. There are 6 key dimensions to consider - working in pairs: 𝐏𝐚𝐢𝐫 #1: 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 ↔️ 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐯𝐨𝐥𝐯𝐞𝐦𝐞𝐧𝐭 aka. how decisions are made - and how much human intervention is required: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: You make ALL decisions upfront when designing your automation, which means that no human intervention is needed after. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: You set boundaries for the AI to operate within; humans occasionally review outputs or intervene when the system encounters edge cases. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: You set high-level goals, and AI determines its own path; this means humans need to provide ongoing feedback to ensure it makes the right decisions. 𝐏𝐚𝐢𝐫 #2: 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ↔️ 𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 a.k.a which type of data the system should process - and how adaptable it has to be: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Requires strictly predefined data formats with no deviation; breaks when encountering unexpected inputs and needs to be re-engineered when processes change. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Handles mostly structured data with some variability allowed; can adjust to parameter variations within defined parameters but needs guidance for significant changes. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Processes diverse unstructured data across multiple sources with varying formats; independently adapts to different inputs and shifting environments without reprogramming. 𝐏𝐚𝐢𝐫 #3: 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 ↔️ 𝐑𝐢𝐬𝐤 𝐓𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞 a.k.a how predictable the outcomes must be - and what level of risk is acceptable: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Delivers highly consistent, predictable results every time; ideal for mission-critical processes where errors cannot be tolerated and predictability is essential. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Produces mostly reliable outcomes with occasional variations in edge cases; balances flexibility with guardrails to prevent major errors while allowing some adaptability. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Creates outcomes that can vary significantly between iterations; optimized for scenarios where discovering novel approaches and adaptability outweigh the need for consistent results. How to use this framework: Always 𝘴𝘵𝘢𝘳𝘵 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘭𝘦𝘧𝘵 and move right only when necessary. 1. Start with automation 2. Move to AI workflows when you need more flexibility within guardrails  3. Only move to agents when you need high adaptability Don’t fall for the AI agent hype - most processes can be automated without agents.

Recently helped a client cut their AI development time by 40%. Here’s the exact process we followed to streamline their workflows. Step 1: Optimized model selection using a Pareto Frontier. We built a custom Pareto Frontier to balance accuracy and compute costs across multiple models. This allowed us to select models that were not only accurate but also computationally efficient, reducing training times by 25%. Step 2: Implemented data versioning with DVC. By introducing Data Version Control (DVC), we ensured consistent data pipelines and reproducibility. This eliminated data drift issues, enabling faster iteration and minimizing rollback times during model tuning. Step 3: Deployed a microservices architecture with Kubernetes. We containerized AI services and deployed them using Kubernetes, enabling auto-scaling and fault tolerance. This architecture allowed for parallel processing of tasks, significantly reducing the time spent on inference workloads. The result? A 40% reduction in development time, along with a 30% increase in overall model performance. Why does this matter? Because in AI, every second counts. Streamlining workflows isn’t just about speed—it’s about delivering superior results faster. If your AI projects are hitting bottlenecks, ask yourself: Are you leveraging the right tools and architectures to optimize both speed and performance?

Don’t Make Me Prompt You – Vol. 1 Workflows Are Not Intelligence Everyone wants AI. Most are building workflows. Outbound cadences. CRM automations. Chatbot logic. Dozens of decision trees—layered, triggered, sequenced. Let’s be clear: workflows aren’t the problem. They’re good at what they’re designed to do—execute predefined steps. But that’s the issue. They only know what you already decided. Workflows follow rules. Agentic workflows make calls. The first asks: “If they do X, what should happen?” The second asks: “What’s the best next move—right now?” A real-world example: Let’s say a high-fit prospect visits your pricing page 3 times in 48 hours. Workflow logic: -Wait 24 hours -Send automated email -If no reply, trigger SDR task -2 days later: LinkedIn connection request Outcome: Maybe a meeting—after a week Agentic workflow: -Detects ICP match + pricing intent spike -Adjusts priority in real time -Bypasses email -Engages immediately via LinkedIn with personalized tone -If engaged, auto-books the meeting and notifies the rep One follows rules. The other takes initiative. This matters more than ever for GTM leaders and agencies. You’re not being judged on automation anymore. You’re being judged on adaptive outcomes: -Was the meeting booked? -Did the buyer stay engaged? -Are we moving faster than competitors? You don’t need more logic branches. You need systems that think and act. If reading this made you glance sideways at your workflows… That’s the signal. Tell me where your system breaks. I’ll tell you what an agent would do instead. This series is about that shift. Not more tools. Not more rules. Not Hype. Not Headlines Just architectural clarity on what AI is—and isn’t. Next up: Vol. 2 drops next: This Is Not a Chatbot We’ll break down why reaction ≠ intelligence, and why your “AI” probably still needs permission to act.