Workflow Automation Solutions

Instead of asking "what should I automate?" Focus on WHY you should automate and HOW it solves the data problem. Most data engineers automate the wrong things at the wrong time. Here's the framework I use after 8 years of building production systems: ✅ AUTOMATE WHEN: → Task runs daily/weekly → Human errors cause outages → Work blocks other priorities → Team growth = more manual work Examples: Reports, schema checks, alerts ❌ DON'T AUTOMATE WHEN: → Task happens quarterly → Requirements change weekly → Process isn't understood yet → Manual steps reveal insights My rule: If it’s done 3+ times, script it; 10+ times, automate it; fails 5+ times, redesign it. Automate what matters, when it matters—not everything! Here's how Airflow makes data automation ridiculously easy: 🎯 The Magic Triangle: → Scheduler: Triggers workflows on time → Executor: Distributes work to available workers → Workers: Actually run your Python code 💾 Smart State Management: → Metadata DB: Tracks every task run → Queue: Manages task priorities → Web UI: Visual monitoring & debugging 🔄 Why It Works: → Write Python DAGs once → Airflow handles the rest → Automatic retries & error handling → Parallel task execution → Visual dependency tracking Real Example: Instead of: ❌ Cron jobs that fail silently ❌ Manual dependency management ❌ No visibility into failures You get: ✅ Visual workflow monitoring ✅ Automatic failure notifications ✅ Smart task scheduling ✅ Easy debugging & restarting Image Credits: lakeFS The Bottom Line: Apache Airflow turns complex data workflows into manageable Python scripts. What's your biggest pipeline automation challenge? #data #engineering

Workflow Agents in #Oracle_Fusion_AI_Agent_Studio are redefining what “#Enterprise_AI_automation” actually means. Most tools can run steps. Some tools can call an LLM. But Workflow Agents do something much bigger---->> they combine deterministic control flow, reasoning, memory, and multi-agent orchestration directly inside the systems that run the business. Here are 4 patterns that give them some real power: 1. Chaining — Step-by-step intelligence Every step interprets context, transforms data, and feeds the next. Perfect for real enterprise flows with dependencies: onboarding, validation, document-to-decision processes, and month-end close. 2. Parallel — Collective decisioning at speed Multiple branches run at once: diagnostics, policy checks, data lookups, history, extraction. Everything merges into a single, high-quality decision. Faster outcomes with better signal coverage. 3. Switch — Context-aware routing without rule bloat Instead of giant rule trees, the workflow adapts to user, policy, intent, and application state on the fly. Same entry point, personalized paths. Automation that’s flexible, not fragile. 4. Iteration — Goal-seeking refinement Great for scheduling, planning, allocation, cost modeling. The agent loops intelligently until constraints are met. Not “first viable answer” — the right answer. This is only one layer of the bigger story. Fusion supports the full spectrum of AI automation: - Workflows for structure. - Workflow Agents for structure with reasoning. - Agent Teams for autonomous digital workers that pursue outcomes. And because all of this lives inside Oracle Fusion Applications, the automation is grounded in real Fusion data, policies, security, and transactions from the start. Enterprise AI that actually does the work — #built_in_not_bolted_on.

3 Workflows I've Automated for in-house teams. ① Ask Legal ② Procurement ③ Contract Review (not just the review!) 1. Ask Legal [or any department for that matter 🤷🏼♀️] You've heard me talk about legal teams and knowledge management. Long story short, your legal team is answering the same 20 questions over and over 😵💫 A simple way to save a CHUNK of time answering questions from the business (enabling them to go faster) ALL while having complete control & keeping a human in the loop? ↪️ Set up an 'Ask Legal' bot in your comms platform. ↪️ Sync it with your knowledge base (e.g GDrive/Notion/Sharepoint). ↪️ Set up your custom instructions (Want it to tag Bob on privacy questions only, specifically on a Tuesday? No problem).  ↪️ Don't want the answer to go straight out to the business without reviewing it first? Cool, turn on co-pilot mode. The result? 60-80% fewer repetitive queries. Your team focuses on the high value things that need a human lawyer. 2. Procurement Businesses have 100's of tools, but when departments don't speak to each other you end up with duplicate tools & subscriptions 😭 💵 🚽.  What if there was a way for the business to find out in <1 minute if there was a tool available that covered their needs, before needing to spend some hard secured department budget? Moreover, what if I told you, they could kick off the internal procurement process from the comfort of your comms platform? Team member : “Do we already have a tool for X?” in Slack/Teams ✅ Bot checks knowledge base (policies, procurement tool). ✅ If a match is found, it shares the approved tool & owner to contact. ✅ If not, the bot can ask the user for more info and direct them with next steps to kick off the procurement process from inside Slack/Teams. Ensuring your users ACTUALLY follow the process, without adding friction. Did I just see your CFO cry tears of joy? 3. Third Party Vendor Contract Review & Project Management Getting AI to redline a contract (as a first pass) is a huge win, but there's still the other pieces of the process missing, like: 🤷🏼♀️ The business figuring out IF legal review is even needed (according to company policy). 📨 The business actually submitting the contract to legal. 😩 Managing review capacity within the legal team. 🖥️ Getting the legal team to log & update the PM tool. The list never ends. Legal reviews only what actually needs their eyes, turnaround times improve, and the business stops pinging the team for “update pls?” in Slack : ) TLDR; Most legal teams are drowning in admin work that could be automated. I've built all of these using simple processes and tools (that I've found most businesses have). You also know I love a good Figma flow. So I’ve built them for all three of the above (see a sneak peak below). Want the entire thing? Comment "FLOWS" and I'll send them over. Also, tell me what you want to see - more of the above or step-by-step how-to build videos?

People compare MCP, RAG, and AI Agents like they compete. They don’t. They exist at different layers of the system. 𝟭. 𝗠𝗖𝗣 - 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 MCP is the USB-C port for AI. Before MCP, connecting an LLM to a database required custom glue code. It was messy and broke often. MCP standardizes the plug. Now, your model connects to your CRM, ERP, or Teams instantly. It doesn't care how the tool was built; the protocol handles it. The biggest impact? Enterprise adoption gets simpler because the interface is predictable. MCP doesn’t decide. It just makes action possible. 𝟮. 𝗥𝗔𝗚 - 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 RAG solves the "knowledge cut-off" problem. Your AI model is frozen in time. It doesn't know about the contract you signed this morning. You can’t retrain the brain daily. So RAG hands it a textbook. It fetches the correct PDF, email, or database row so the AI answers based on facts, not hallucinations. RAG makes models sound smarter. It doesn’t make them do anything. 𝟯. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 - 𝗧𝗵𝗶𝗻𝗸 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝘄𝗼𝗿𝗸𝗲𝗿𝘀 RAG reads, MCP connects. Agents do. An Agent has a goal. It loops until the goal is achieved. → Observes the problem. → Reasons through next steps. → Acts (creates accounts, sends emails, updates records). → Validates results and loops if needed. 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝘁𝗵𝗲𝗺 The real value isn't in one of these. It is in the stack. You build real workflows by combining them. Take a real workflow: Onboarding a client. 1/ 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 (brain) breaks down the task. 2/ It uses 𝗥𝗔𝗚 (memory) to pull the client’s contract details and your company’s onboarding SOPs. 3/ It uses 𝗠𝗖𝗣 (hands) to create a Slack channel, set up the client in CRM, and email the welcome packet without custom code for each step. Stop asking "Which one should we buy?" Start asking "How do we combine these to automate end-to-end workflows?" -- I’m building a newsletter to go deeper: Build What Matters. Weekly drops on AI agents + emerging workflows. Subscribe Free Here 👉 https://lnkd.in/g6f4pVUz ♻️ Repost to help your network understand AI. ➕ Follow Luís Rodrigues for practical AI + Business insights Kudos to 𝗔𝗹𝗲𝘅 𝗫𝘂 for the image!

What if building automation became a driver of production efficiency? At our Phoenix Contact site in Bad Pyrmont, we’re exploring exactly that. During a recent visit, I met with Dr. Hannah Peter to discuss how we’re connecting facility management and manufacturing. The goal is smarter use of energy and resources. Our PLCnext Factory continuously collects data, which is analyzed by AI to provide infrastructure on demand. This leads to up to 50% lower operating costs. Over the past three years, we’ve seen measurable impact: ⬆️ 30% more productivity ⬇️ 30% less energy consumed 💶 Approximately 1.5 million euros saved annually 🌍 Around 200 tons of CO₂ avoided per year Facility systems, production, EV charging infrastructure, and a battery storage unit are all connected and largely powered by our own solar energy. We also collaborate locally, for example via the district heating network, to make use of existing resources. What we test and validate here is shared with customers and partners who are looking to digitize their own operations. This is sector coupling in practice. A step closer to the 1.5°C goal. Do we have all the answers? Not yet. But we’re learning fast and sharing what works. And here’s one more idea: What if we made these systems even more open and scalable with a control solution built specifically for building applications, based on PLCnext Technology?

I started by asking AI to do everything. Six months later, 65% of my agent’s workflow nodes run as non-AI code. The first version was fully agentic : every task went to an LLM. LLMs would confidently progress through tasks, though not always accurately. So I added tools to constrain what the LLM could call. Limited its ability to deviate. I added a Discovery tool to help the AI find those tools. Better, but not enough. Then I found Stripe’s minion architecture. Their insight : deterministic code handles the predictable ; LLMs tackle the ambiguous. I implemented blueprints, workflow charts written in code. Each blueprint specifies nodes, transitions between them, trigger conditions for matching tasks, & explicit error handling. This differs from skills or prompts. A skill tells the LLM what to do. A blueprint tells the system when to involve the LLM at all. Each blueprint is a directed graph of nodes. Nodes come in two types : deterministic (code) & agentic (LLM). Transitions between nodes can branch based on conditions. Deal pipeline updates, chat messages, & email routing account for 29% of workflows, all without a single LLM call. Company research, newsletter processing, & person research need the LLM for extraction & synthesis only. Another 36%. The workflow runs 67-91% as code. The LLM sees only what it needs : a chunk of text to summarize, a list to categorize, processed in one to three turns with constrained tools. Blog posts, document analysis, bug fixes are genuinely hybrid. 21% of workflows. Multiple LLM calls iterate toward quality. Only 14% remain fully agentic. Data transforms & error investigations. These tend to be coding tasks rather than evaluating a decision point in a workflow. The LLM needs freedom to explore. AI started doing everything. Now it handles routing, exceptions, research, planning, & coding. The rest runs without it. Is AI doing less? Yes. Is the system doing more? Also yes. The blueprints, the tools, the skills might be temporary scaffolding. With each new model release, capabilities expand. Tasks that required deterministic code six months ago might not tomorrow.

LLMs struggle with rationality in complex game theory situations, which are very common in the real world. However integrating structured game theory workflows into LLMs enables them to compute and execute optimal strategies such as Nash Equilibria. This will be vital for bringing AI into real-world situations, especially with the rise of agentic AI. The paper "Game-theoretic LLM: Agent Workflow for Negotiation Games" (link in comments) examines the performance of LLMs in strategic games and how to improve them. Highlights from the paper: 💡 Strategic Limitations of LLMs in Game Theory: LLMs struggle with rationality in complex game scenarios, particularly as game complexity increases. Despite their ability to process large amounts of data, LLMs often deviate from Nash Equilibria in games with larger payoff matrices or sequential decision trees. This limitation suggests a need for structured guidance to improve their strategic reasoning capabilities. 🔄 Workflow-Driven Rationality Improvements: Integrating game-theoretic workflows significantly enhances the performance of LLMs in strategic games. By guiding decision-making with principles like Nash Equilibria, Pareto optimality, and backward induction, LLMs showed improved ability to identify optimal strategies and robust rationality even in negotiation scenarios. 🤝 Negotiation as a Double-Edged Sword: Negotiations improved outcomes in coordination games but sometimes led LLMs away from Nash Equilibria in scenarios where these equilibria were not Pareto optimal. This reflects a tendency for LLMs to prioritize fairness or trust over strict game-theoretic rationality when engaging in dialogue with other agents. 🌐 Challenges with Incomplete Information: In incomplete-information games, LLMs demonstrated difficulty handling private valuations and uncertainty. Novel workflows incorporating Bayesian belief updating allowed agents to reason under uncertainty and propose envy-free, Pareto-optimal allocations. However, these scenarios highlighted the need for more nuanced algorithms to account for real-world negotiation dynamics. 📊 Model Variance in Performance: Different LLM models displayed varying levels of rationality and susceptibility to negotiation-induced deviations. For instance, model o1 consistently adhered more closely to Nash Equilibria compared to others, underscoring the importance of model-specific optimization for strategic tasks. 🚀 Practical Implications: The findings suggest LLMs can be optimized for strategic applications like automated negotiation, economic modeling, and collaborative problem-solving. However, careful design of workflows and prompts is essential to mitigate their inherent biases and enhance their utility in high-stakes, interactive environments.

If you’ve ever wished the browser could just do the repetitive work for you, Composite is here to make that a reality. What is Composite? + Composite is an AI agent that lives inside your existing browser and automates tedious web tasks — from clicks, form fills, navigation, to chaining workflows — all through natural language. + You simply tell it what you want done (“Send the weekly sales recap to the team”, “Update LinkedIn prospects into CRM”, etc.), and it executes the steps behind the scenes. ✅ Key Features & Differentiators + Feature Why It Matters Natural-language tasking You don’t need to script or wire APIs — just describe the task in plain English. + Real-time browser actions The agent works in your live browser session — you can see it scroll, click, type — and step in if needed. + Local-first / privacy-friendly Everything runs on-device — your credentials, DOM data, or private content are not sent to remote servers. + Low latency Since actions are local, there’s minimal lag. You can reliably automate even while screen-sharing. + Workflow chaining Because it drives the real UI of websites, you can combine steps across platforms (e.g. LinkedIn → CRM → Slack) seamlessly. + Minimal setup friction No API keys, no reauthentication hoops — it piggybacks on your existing browser session. 🎯 Benefits You’ll Feel Immediately + Save time: Offload repetitive tasks (data entry, updates, moves between systems) so you can focus on high-value work. + Reduce errors & drift: Automations follow exact steps consistently, reducing human mistakes from manual copy-pasting. + Faster onboarding & scaling: Because it needs little setup, teams can adopt it quickly without heavy IT or integration overhead. + Privacy & security comfort: The local-first design alleviates many concerns around sending sensitive data into cloud agents. + Flexibility & control: You retain visibility and the ability to override what the agent does in real time. 💡 Use Cases That Shine + CRM / sales teams: Auto-log LinkedIn leads into pipelines, send follow-ups, pull metrics + Recruiting / HR: Move candidate data between job boards, ATS systems, emails + Operations & admin: Sync dashboards, generate status reports, coordinate between tools + Marketing: Automate content updates, campaign dashboards, cross-platform publishing 🔗 Check it out at https://lnkd.in/eG-qCMwT #Productivity #AI #Automation #BrowserAgent #Composite #Tech

𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗠𝘆 𝗣𝗠 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝗻 10 𝗠𝗶𝗻𝘂𝘁𝗲𝘀—𝗛𝗲𝗿𝗲’𝘀 𝗘𝘅𝗮𝗰𝘁𝗹𝘆 𝗛𝗼𝘄 👇 Product teams waste 17% of their time on documentation and comms (McKinsey). And I automated the most tedious part for me using Lovable - with zero code.  👉 Turning detailed PRDs into internal launch comms, Notion posts, stakeholder briefs, and checklists. It was eating up hours every week — across product and growth team. So I thought, what if we just automated it? 🤔 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁: ✅ Reads our PRDs ✅ Extracts the key details ✅ Generates a Notion-ready launch post ✅ And even creates a structured PM checklist No code. Just a few smart prompt blocks. Now this agent saves our team 4–6 hours per launch, and keeps everyone aligned without the usual back-and-forth. In this post, I’m breaking down exactly how I built it step by step: - My exact 10-step framework   - Battle-tested prompts you can copy   - Common pitfalls (and how to avoid them)  👉 Swipe through to see how you can build your own AI teammate too. P.S. Should product teams have an "AI Agent Manager" role by 2025?

From Woodworking to 3D Printing: Scaling a 40-Year-Old Fabrication Shop 💥 A customer asks for something that sounds almost impossible. The design is complex. The deadline is tight. And waiting weeks for an outside supplier isn't an option. Many shops would turn the project down. Britten, Inc. doesn’t say "No". After decades of building custom displays with CNC machining and foam fabrication, the Michigan-based company added the BigRep - Large-Format 3D Printers ONE to its production floor. The goal wasn't simply to adopt a new technology. It was to remove the bottlenecks that kept slowing projects down. The results changed the way they work. Instead of outsourcing oversized components, Britten now produces large, one-piece clear light globes in-house. Printing them as a single part eliminates seams, reduces finishing work, and shortens production time. The benefits extend far beyond customer projects. The engineering team also prints custom jigs, fixtures, and production aids whenever they're needed, replacing hours spent sourcing specialized hardware with a print that starts the same day. Today, roughly half of Britten's sculptures incorporate 3D-printed parts. Bringing large-format additive manufacturing in-house didn't just expand their capabilities. It gave them greater control over production, faster turnaround times, and the flexibility to move from design to finished part in days instead of weeks. When every project depends on speed and customization, reducing reliance on outside suppliers can become one of your biggest competitive advantages. 👉 Read the full case study here: https://lnkd.in/g8SRyunR #3DPrinting